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Toward Bayesian Synchronous Tree Substitution Grammars for Sentence Planning

Developing conventional natural language generation systems requires extensive attention from human experts in order to craft complex sets of sentence planning rules. We propose a Bayesian nonparametric approach to learn sentence planning rules by inducing synchronous tree substitution grammars for pairs of text plans and morphosyntactically-specified dependency trees. Our system is able to learn rules which can be used to generate novel texts after training on small datasets.

Toward Bayesian Synchronous Tree Substitution Grammars for Sentence Planning November 5-8, 2018 David M Howcroft Department of Language Science and Technology Saarland Informatics Campus Saarland University Germany Dietrich Klakow Department of Language Science and Technology Saarland Informatics Campus Saarland University Germany Vera Demberg Department of Language Science and Technology Saarland Informatics Campus Saarland University Germany Toward Bayesian Synchronous Tree Substitution Grammars for Sentence Planning Proceedings of The 11th International Natural Language Generation Conference The 11th International Natural Language Generation ConferenceTilburg, The NetherlandsNovember 5-8, 2018391 Developing conventional natural language generation systems requires extensive attention from human experts in order to craft complex sets of sentence planning rules. We propose a Bayesian nonparametric approach to learn sentence planning rules by inducing synchronous tree substitution grammars for pairs of text plans and morphosyntactically-specified dependency trees. Our system is able to learn rules which can be used to generate novel texts after training on small datasets. Introduction Developing and adapting natural language generation (NLG) systems for new domains requires substantial human effort and attention, even when using off-the-shelf systems for surface realization. This observation has spurred recent interest in automatically learning end-to-end generation systems (Mairesse et al., 2010;Konstas and Lapata, 2012;Wen et al., 2015;Dušek and Jurčíček, 2016); however, these approaches tend to use shallow meaning representations (Howcroft et al., 2017) and do not make effective use of prior work on surface realization to constrain the learning problem or to ensure grammaticality in the resulting texts. Based on these observations, we propose a Bayesian nonparametric approach to learning sentence planning rules for a conventional NLG system. Making use of existing systems for surface realization along with more sophisticated meaning representations allows us to cast the problem as a grammar induction task. Our system induces synchronous tree substitution grammars for pairs of text plans and morphosyntactically-specified dependency trees. Manual inspection of the rules and texts currently produced by our system indicates that they are generally of good quality, encouraging further evaluation. Overview Whether using hand-crafted or end-to-end generation systems, the common starting point is collecting a corpus with semantic annotations in the target domain. Such a corpus should exhibit the range of linguistic variation that developers hope to achieve in their NLG system, while the semantic annotations should be aligned with the target input for the system, be that database records, flat 'dialogue act' meaning representations, or hierarchical discourse structures. For our system (outlined in Figure 1) we focus on generating short paragraphs of text containing one or more discourse relations in addition to propositional content. To this end we use as input a text plan representation based on that used in the SPaRKy Restaurant Corpus (Walker et al., 2007). These text plans connect individual propositions under nodes representing relations drawn from Rhetorical Structure Theory (Mann and Thompson, 1988). Rather than using a fully end-to-end approach to learn a tree-to-string mapping from our text plans to paragraphs of text, we constrain the learning problem by situating our work in the context of a conventional NLG pipeline (Reiter and Dale, 2000). In the pipeline approach, NLG is decomposed into three stages: document planning, sentence planning, and surface realization. Our approach assumes that the text plans we are working with are the product of document planning, and we use an existing parser-realizer for surface realization. This allows us to constrain the learning problem by limiting our search to the set of tree-to-tree mappings which produce valid input for the sur- Blue boxes represent our system and the outputs dependent on it, while boxes with white background represent existing resources used by our system. face realizer, leveraging the linguistic knowledge encoded in this system. Restricting the problem to sentence planning also means that our system needs to learn lexicalization, aggregation, and referring expression generation rules but not rules for content selection, linearization, or morphosyntactic agreement. The input to our statistical model and sampling algorithm consists of pairs of text plans (TPs) and surface realizer input trees, here called logical forms (LFs). At a high level, our system uses heuristic alignments between individual nodes of these trees to initialize the model and then iteratively samples possible alternative and novel alignments to determine the best set of synchronous derivations for TP and LF trees. The synchronous tree substitution grammar rules induced in this way are then used for sentence planning as part of our NLG pipeline. Synchronous TSGs Synchronous tree substitution grammars (TSGs) are a subset of synchronous tree adjoining grammars, both of which represent the relationships between pairs of trees (Shieber and Schabes, 1990;Eisner, 2003). A tree substitution grammar consists of a set of elementary trees which can be used to expand non-terminal nodes into a complete tree. Consider the example in Figure 2, which shows the text plan and logical form trees for the sentence, Sonia Rose has very good food quality, but Bienvenue has excellent food quality. The logical form in this figure could be derived in one of three ways. First, we could simply have this entire tree memorized in our grammar as an elementary tree. This would make the derivation trivial but would also result in a totally ungeneralizable rule. On the other hand, we could have the equivalent of a CFG derivation for the tree consisting of rules like but → First Next, First → have, have → Arg0 Arg1, and so on. These rules would be very general, but the derivation then requires many more steps. The third option, illustrating the appeal of using a tree substitution grammar, involves elementary trees of intermediate size, like those in Figure 3. The rules in Figure 3 represent a combination of small, CFG-like rules (e.g. the elementary tree rooted at but), larger trees representing memorized chunks (i.e. the rule involving Bienvenue), and intermediate trees, like the one including have → quality → food. In these elementary trees, the empty node sites at the end of an arc represent substitution sites, where another elementary tree must be expanded for a complete derivation. In typical applications of TSGs over phrase structure grammars, these substitution sites would be labeled with non-terminal categories which then correspond to the root node of the elementary tree to be expanded. In our (synchronous) TSGs over trees with labeled arcs, we consider the 'nonterminal label' at each substitution site to be the tree location, which we define as the label of the parent node paired with the label of the incoming arc. A synchronous tree substitution grammar, then, consists of pairs of elementary trees along with an alignment between their substitution sites. For example, we can combine the TP elementary tree rooted at contrast with the LF elementary tree rooted at but, aligning each (contrast, Arg) substitution site in the TP to the (but, F irst) and (but, Next) sites in the LF. Dirichlet Processes The Dirichlet process (DP) provides a natural way to trade off between prior expectations and observations. For our purposes, this allows us to define prior distributions over the infinite, discrete space of all possible pairs of TP and LF elementary trees and to balance these priors against the full trees we observe in the corpus. We follow the Chinese Restaurant Process formulation of DPs, with concentration parameter α = 1. 1 Here, the probability of a particular elementary tree e being observed is given by: P (e) = freq(e) #obs + α + α #obs + α P prior (e),(1) where freq(e) is the number of times we have observed the elementary tree e, #obs is the total number of observations, and P prior is our prior. It is clear that when we have no observations, we estimate the probability of e entirely based on our prior expectations. As we observe more data, however, we rely less on our priors in general. Statistical Model Our model uses Dirichlet processes with other DPs as priors (i.e. Hierarchical Dirichlet Processes, or HDPs). This allows us to learn more informative prior distributions (the lower-level DPs) to improve the quality of our predictions for higherlevel DPs. Section 5.1 describes the HDPs used to model elementary trees for text plans and logical forms, which rely on prior distributions over possible node and arc labels. This model in turn serves as the prior for the synchronous TSG's pairs of elementary trees, as described in Section 5.2 along with the HDP over possible alignments between the frontier nodes of these pairs of elementary trees. A plate diagram of the model is presented in Figure 4. HDP for TSG Derivations We begin by defining TSG base distributions for text plans and logical forms independently. Our generative story begins with sampling an elementary tree for the root of the tree and then repeating this sampling procedure for each frontier node in the expanded tree. Since the tree locations l corresponding to frontier nodes are completely determined by the current expansion of the tree, we only need to define a distribution over possible elementary trees conditioned on the tree location: T |l ∼DP(1.0, P (e|l)) (2) P (e|l) =N (n(root(e))|l) (3) Π a∈a(root(e)) A(a|n(root(e))) Π child∈children(root(e)) P (child|l(child)), where N and A are Dirichlet processes over possible node labels and arc labels, we use N (n|l) for the probability of node label n at tree location l according to DP N , and similarly for A. We further overload our notation to use n(node) to indicate the node label for a given node, a(node) to indicate the outward-going arc labels from node, and l(e) or l(node) to indicate the location of a given subtree or node within the tree as an (n, l) pair. root(e) is a function selecting the root node of an elementary tree e and children(node) indicates the child subtrees of a given node. The distributions over node labels given tree locations N |l and arc labels given source node labels A|n are DPs over simple uniform priors: Figure 4: Dependencies in our statistical model, omitting parameters for clarity. Each node represents a Dirichlet process over base distributions (see Sec. 5) with α = 1. n here indexes node labels for TPs or LFs as appropriate, while l similarly represents tree locations. A T P n N T P l T T P l TP pair (l,l) T LF N LF A LF n l l LF Al L T P L LF Alignments N |l ∼DP(1.0, Uniform({n ∈ corpus})) (4) A|n ∼DP(1.0, Uniform({a ∈ corpus})) (5) HDP for sTSG Derivations Our synchronous TSG model has two additional distributions: (1) a distribution over pairs of TP and LF elementary trees; and (2) a distribution over pairs of tree locations representing the probability of those locations being aligned to each other. Similarly to the generative story for a single TSG, we begin by sampling a pair of TP & LF elementary trees, a TreePair, for the root of the derivation. We then sample alignments for the frontier nodes of the TP to the frontier nodes of the LF. For each of these alignments, we then sample the next TreePair in the derivation and repeat this sampling procedure until no unfilled frontier nodes remain. The distribution over TreePairs for a given pair of tree locations is given by a Dirichlet process with a simple prior which multiplies the probability of a given TP elementary tree by the probability of a given LF elementary tree: pair|l TP , l LF ∼ DP(1.0, P (e TP , e LF |l TP , l LF )) (6) P (e TP , e LF |l TP , l LF ) = T TP (e TP |l TP )T LF (e LF |l LF ) The distribution over possible alignments is given by an DP whose prior is the product of the probabilities of pair of (TP and LF) tree locations in question. These probabilities are each modeled as a DP with a uniform prior over possible tree locations. Al ∼DP(1.0, P (l TP , l LF )) (8) P (l TP , l LF ) =P (l TP )P (l LF ) (9) P (l · ) ∼DP(1.0, Uniform({l · })) (10) Sampling Our Gibbs sampler adapts the blocked sampling approach of (Cohn et al., 2010) to synchronous grammars. For each text in the corpus, we resample a synchronous derivation for the entire text before updating the associated model parameters. Generation While our pipeline can in principle work with any reversible parser-realizer, our current implementation uses OpenCCG 2 (White, 2006;White and Rajkumar, 2012). We use the broad-coverage grammar for English based on CCGbank (Hockenmaier, 2006). The 'logical forms' associated with this grammar are more or less syntactic in nature, encoding the lemmas to be used, the dependencies among them, and morphosyntactic annotations in a dependency semantics. Parsing the corpus with OpenCCG provides the LFs we use for training. After training the model, we have a collection of synchronous TSG rules which can be applied to (unseen) text plans to produce new LFs. For this rule application we use Alto 3 (Koller and Kuhlmann, 2012) because of its efficient implementation of parsing for synchronous grammars. The final stage in the generation pipeline is to realize these LFs using OpenCCG, optionally performing reranking on the resulting texts. Some examples of the resulting texts are provided in the next section. Example output As a testbed during development we used the SPaRKy Restaurant Corpus (2007), a corpus of restaurant recommendations and comparisons generated by a hand-crafted NLG system. While the controlled nature of this corpus is ideal for testing during development, our future evaluations will also use the more varied Extended SRC (Howcroft et al., 2017). 4 After training on about 700 TP-LF pairs for 5k epochs, our system produces texts such as: Here we see examples of pronominalization throughout, as well as the deictic referring expression this Thai restaurant (in 1), which avoids repeating either the pronoun 'it' or the name of the restaurant again. The system also makes good use of discourse connectives (like 'since' in 2) as well as non-restrictive relative clauses (as in 3). However, the system does not always handle punctuation correctly (as in 3) and sometimes learns poor semantic alignments, aligning but ommitting part of the meaning in saying 'Vegetarian' for 'Kosher, Vegetarian' in 3 and completely misaligning 'good' to 'food' in (4) due to the frequent co-occurrence of these words in the corpus. Moreover, example 4 also demonstrates that some combinations of rules based on poor alignments can lead to repetition. While there is clearly still room for improvement, the quality of the texts overall is encouraging, and we are currently preparing a systematic human evaluation of the system. Related Work While the present work aims to learn sentence planning rules in general, White and Howcroft (2015) focused on learning clause-combining rules, using a set of templates of possible rule types to extract a set of clause-combining operations based on pattern matching. The resulting rules were, like ours, tree-to-tree mappings; however, our rules proceed directly from text plans to final logical forms, while their approach assumed lexicalized text plans (i.e. logical forms without any aggregation operations applied) paired with logical forms as training input. In learning a synchronous TSG, the model presented here aims to avoid using hand-crafted rule templates, which are more dependent on the specific representation chosen for surface realizer input. As mentioned in the introduction, there have been a number of attempts in recent years to learn end-to-end generation systems which produce text directly from database records (Konstas and Lapata, 2012), dialogue acts with slot-value pairs (Mairesse et al., 2010;Wen et al., 2015;Dušek and Jurčíček, 2016), or semantic triples like those used in the recent WebNLG challenge (Gardent et al., 2017). In contrast, we assume that content selection and discourse structuring are handled before sentence planning. In principle, however, our methods can be applied to any generation subtask involving tree-to-tree mappings. Discussion and Conclusion We have presented a Bayesian nonparametric approach to learning synchronous tree substitution grammars for sentence planning. This approach is designed to address specific weaknesses of endto-end approaches with respect to discourse structure as well as grammaticality. Our preliminary analysis suggests that our approach can learn useful sentence planning rules from smaller datasets than those typically used for training neural models. We are currently preparing to launch an extensive human evaluation of our model compared to current neural approaches to text generation. Figure 1 : 1Overview of our pipeline. Square boxes represent data; rounded boxes represent programs. Figure 3 : 3Possible elementary trees for the TP (top row) and LF (bottom row) in Figure 2, omitting some detail for simplicity. Other concentration parameters are possible, but α = 1 is the standard default value, and we do not perform any search for a more optimal value at this time. https://github.com/OpenCCG/openccg 3 https://bitbucket.org/tclup/alto . Chanpen Thai has the best overall quality among the selected restaurants. Its price is 24 dollars and it has good service. This Thai restaurant has good food quality, with decent decor.2. Since Komodo's price is 29 dollars and it has good decor, it has the best overall quality among the selected restaurants.3. Azuri Cafe, which is a Vegetarian restaurant has very good food quality. Its price is 14 dollars. It has the best overall quality among the selected restaurants.4. Komodo has very good service. It has food food quality, with very good food quality, it has very good food quality and its price is 29 dollars. For details about differences between these two corpora, we refer the interested reader toHowcroft et al. (2017). AcknowledgmentsWe would like to thank Leon Bergen for advice on Bayesian induction of TSGs, Jonas Groschwitz for assistance with Alto, and Sacha Beniamine & Meaghan Fowlie for proofreading and revision advice. This work was supported by DFG collaborative research center SFB 1102 'Information Density and Linguistic Encoding'. 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USF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings

In this paper, we present a system description for the SemEval-2019 Task 6 submitted by our team. For the task, our system takes tweet as an input and determine if the tweet is offensive or non-offensive (Sub-task A). In case a tweet is offensive, our system identifies if a tweet is targeted (insult or threat) or nontargeted like swearing (Sub-task B). In targeted tweets, our system identifies the target as an individual or group (Sub-task C). We used data pre-processing techniques like splitting hashtags into words, removing special characters, stop-word removal, stemming, lemmatization, capitalization, and offensive word dictionary. Later, we used keras tokenizer and word embeddings for feature extraction. For classification, we used the LSTM (Long shortterm memory) model of keras framework. Our accuracy scores for Sub-task A, B and C are 0.8128, 0.8167 and 0.3662 respectively. Our results indicate that fine-grained classification to identify offense target was difficult for the system. Lastly, in the future scope section, we will discuss the ways to improve system performance.

USF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings June 6-7, 2019 Bharti Goel Department of Computer Science and Engineering University of South Florida USA Ravi Sharma ravis@mail.usf.edu Department of Computer Science and Engineering University of South Florida USA USF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019) the 13th International Workshop on Semantic Evaluation (SemEval-2019)Minneapolis, Minnesota, USAJune 6-7, 2019796 In this paper, we present a system description for the SemEval-2019 Task 6 submitted by our team. For the task, our system takes tweet as an input and determine if the tweet is offensive or non-offensive (Sub-task A). In case a tweet is offensive, our system identifies if a tweet is targeted (insult or threat) or nontargeted like swearing (Sub-task B). In targeted tweets, our system identifies the target as an individual or group (Sub-task C). We used data pre-processing techniques like splitting hashtags into words, removing special characters, stop-word removal, stemming, lemmatization, capitalization, and offensive word dictionary. Later, we used keras tokenizer and word embeddings for feature extraction. For classification, we used the LSTM (Long shortterm memory) model of keras framework. Our accuracy scores for Sub-task A, B and C are 0.8128, 0.8167 and 0.3662 respectively. Our results indicate that fine-grained classification to identify offense target was difficult for the system. Lastly, in the future scope section, we will discuss the ways to improve system performance. Introduction In recent years, there has been a rapid rise in social media platforms and surge in the number of users registering in order to communicate, publish content, showcase their skills and express their views. Social media platforms like Facebook and Twitter have millions of registered users influenced by the countless user-generated posts on daily basis (Zeitel-Bank and Tat, 2014). While on one hand social media platforms facilitate the exchange of views, effective communication and can be seen as a helping mode in crisis. On the other hand, they open up the window for anti-social behavior such as bullying, stalking, harassing, trolling and hate speech Wiegand et al., 2018;ElSherief et al., 2018;Zhang et al., 2018). These platforms provide the anonymity and hence aid users to indulge in aggressive behavior which propagates due to the increased willingness of people sharing their opinions (Fortuna and Nunes, 2018). This aggression can lead to foul language which is seen as "offensive", "abusive", or "hate speech", terms, which are used interchangeably (Waseem et al., 2017). In general, offensive language is defined as derogatory, hurtful/ obscene remarks or comments made by an individual (or group) to an individual (or group) (Wiegand et al., 2018;Baziotis et al., 2018). The offensive language can be targeted towards a race, religion, color, gender, sexual orientation, nationality, or any characteristics of a person or a group. Hate Speech is slowly plaguing the social media users with depression and anxiety Zhang et al., 2018), which can be presented in the form of images, text or media such as audio, video, etc. (Schmidt and Wiegand, 2017). Our paper presents the data and task description followed by results, conclusion and future work. The purpose of Task 6 is to address and provide an effective procedure for detecting offensive tweets from the data set provided by shared task report paper (Zampieri et al., 2019b). The shared task is threefold. The Sub-task A ask us to identify whether the given tweet is offensive or non-offensive. In Sub-task B offensive tweets are to be classified as targeted (person/group) or non-targeted (general). Sub-task C ask us to do classification of the offensive tweets into individual, group or others. We apply the LSTM with word embeddings in order to perform the multilevel classification. Related Work Technological giants like Facebook, Google, YouTube and Twitter have been investing a significant amount of time and money towards the detection and removal of offensive and hate speech posts that give users a direct or indirect negative influence (Fortuna and Nunes, 2018). However, lack of automation techniques and ineffectiveness of manual flagging has lead to a lot of criticism for not having potent control for the problem (Zhang et al., 2018). The process of manual tagging is not sustainable or scalable with the large volumes of data exchanged in Social media. Hence, the need of the hour is to do automatic detection and filtering of offensive posts to give the user quality of service (Fortuna and Nunes, 2018). The problem of automatic Hate Speech Detection is not trivial as offensive language may or may not be meant to insult or hurt someone and can be used in common conversations. Different language contexts are rampant in social media . In recent years, linguistics, researchers, computer scientists, and related professionals have conducted research towards finding an effective yet simple solution for the problem. In papers, (Schmidt and Wiegand, 2017;Fortuna and Nunes, 2018), authors survey the state of the art methods along with the description of the nature of hate speech, limitations of the methods proposed in literature and categorization of the hate speech. Further, authors mainly classified the features as general features like N-grams, Part-Of-Speech (POS) and sentiment scores and, specific features such as othering language, the superiority of in-group and stereotype. In (Silva et al., 2016), authors list categories of hate speech and possible targets examples. The research carried over the years has employed various features and classification techniques. The features include the bag of words (Greevy and Smeaton, 2004;Kwok and Wang, 2013), dictionaries, distance metrics, N-grams, IF-IDF scores, and profanity windows (Fortuna and Nunes, 2018). Authors in ) used a crowdsourced hate speech lexicon to collect tweets and train a multi-class classifier to distinguish between hate speech, offensive language, and non-offensive language. The authors in paper (Waseem et al., 2017) presents a typology that gives the relationship between various sub-tasks such as cyber-bullying, hate speech, offensive, and online abuse. They synthesize the various literature definitions and contradiction together to emphasize the central affinity among these sub-tasks. In (Gambäck and Sikdar, 2017), authors used a Convolutional Neural Network model for Twitter hate speech text classification into 4 classes: sexism, racism, both sexism-racism and neither. Similar approaches using deep learning have been employed in (Agrawal and Awekar, 2018;Badjatiya et al., 2017) for detecting hate speech and cyberbullying respectively. Authors in (Zhang et al., 2018) have proposed Deep Neural Network (DNN) structures which serve as a feature extractor for finding key semantics from hate speech. Prior to that, they emphasize the linguistics of hate speech, that it lacks discriminative features making hate speech difficult to detect. Authors in (ElSherief et al., 2018) have carried out the analysis and detection of the hate speech by classifying the target as directed towards a specific person/identity or generalized towards a group of people sharing common characteristics. Their assessment states that directed hate consists of informal, angrier and name calling while generalized hate consists of more religious hate and use of lethal words like murder, kills and exterminate. Problem Statement In Task 6, three level offensive language identification is described as three Sub-tasks A, B and C. For Sub-task A, tweets were identified as offensive (tweet with any form of unacceptable language, targeted/non-targeted offense) or not offensive. For Sub-task B, the offensive tweets are further categorized into targeted or non-targeted tweets. The targeted offense is made for an individual or group while the untargeted offense is a general use of offensive language. Later for Subtask C, targeted tweets are further categorized according to the target, individual, group or others. This step by step tweet classification will lead to the detailed categorization of offensive tweets. Data The data collection methods used to compile the dataset used in OffensEval are described in Zampieri et al. (2019a). The OLID dataset collected from Twitter has tweet id, tweet text, and labels for Sub-task A, B, and C. We have also used Offensive/Profane Word List (Ahn, 2019) with 1,300+ English terms that could be found offensive for lexical analysis of tweets to see check probability of tweet being offensive if a tweet has an offensive word. Data Pre-processing The data is raw tweet data from Twitter and hence data cleaning and pre-processing is required. We used the following steps for data pre-processing: 1. Split hashtags over Capital letters: In this step hashtags are divided into separate words (for example, "#TweetFromTheSeat" will be converted to "Tweet from the seat"). Generally, while writing hashtags multiple words are combined to form single hashtag, where each word is started with a capital letter. Here, we take advantage of this property of hashtag and generate a string of words from it. 2. Remove special characters: In this step we removed all special characters from the tweet and resultant tweet will contain only alphabets and number. In Twitter domain "#" is important special character. Splitting of hashtags,"#Text" into "#" and "Text", retains the purpose of the hashtags after removal of "#" . Other special characters (for e.g. ",", ".","!", etc) are not much informative for given context. 3. Removal of stop-words, Stemming and Lemmatization: In this step we used NLTK (Loper and Bird, 2002) list of stop-words to remove stopwords, classic Porter Stemmer (Porter, 1980) for stemming and NLTK (Loper and Bird, 2002) Word Net Lemmatizer for lemmatization. 4. Capitalization: This is the last step for data pre-processing and all characters are converted to capital letters. In Twitter domain uppercase characters are said to portray expression, but this is not true for all cases. Also, keeping cases intact may lead to over-fitting during training. 5. Embedding "Offensive": This is an optional step. We used offensive word list (Ahn, 2019) to find offensive words in the tweet. Later for tweets with matched offensive words were embedded with "Offensive" as a word. System Description We used a four-layer neural network with each layer detailed below: The first layer of our network is an embedding layer, which takes tokens as inputs (each sentence is converted into index sequences using tokenizer). This layer convert sequences to dense vector sequences generating embedding table used by the next layer. We used tokenizer for top 1000 words and embedding dimension of 128 for our system. The second layer is SpatialDropout1D layer which helps promote independence between feature maps. We used 0.1 as rate or Fraction of the input units to drop. This layer is mainly used to covert multi-dimensional input to onedimensional input using dropout method. Third layer is LSTM (Hochreiter and Schmidhuber, 1997) layer with dropout and recurrent dropout as 0.2. This layer serves as a recurrent neural network layer which was only for short term memory. LSTM (long short-term memory) takes care of longer dependencies. The dimension of LSTM hidden states is 200 for our system. Finally, we used a dense layer with Softmax function for binary classification in-case of Sub-task A and B, and three class classification for Sub-task C. The dimension of the dense layer is 200. For hyperparameter selection, we used different train and validation splits. The batch size is 64, and the maximal training epoch is varied with different system ranging from 5 to 50 (Performance was decreasing for higher epochs). We used RM-SProp as the optimizer for network training. The performance is evaluated by macro-averaged F1score and Accuracy by task organizers. Table 3: Results for Sub-task C. Results Tables 1, 2 and 3 shows F1 (macro) and Accuracy scores for the submitted systems. We can see that for all the sub-tasks our system has F1 (macro) and as described in (Zampieri et al., 2019a) F1 (macro) is used for performance analysis by task coordinators. Best results are highlighted in bold ink in tables and confusion matrix for them is also shown in Figure 1 for Sub-tasks A, B and C. In Sub-task A we achieved 0.8128 and 0.7382 as accuracy and F1 respectively. For this task we submitted only one system with LSTM network dropout 0.2 and 5 epochs. For Sub-task B we submitted three runs but, the best performance is achieved by system with LSTM dropout of 0.2, 20 epochs and offensive word list described in Section 4. Later in Subtask C we submitted three runs and best performance was LSTM with 50 epochs and 0.2 dropout. Conclusion In this paper we used LSTM network to identify offensive tweets and categorize offense in subcategories as described in Section 3 for Task 6: Identifying and Categorizing Offensive Language in Social Media. We used an embedding layer followed by LSTM layer for tweet classification. Three tasks of OffensEval Sub-task A, B, and C were of varied difficulty level. The main reason can be decreasing amount of data for each of them, where Sub-task A has more data followed by Sub-task B categorizing offensive tweets identified by Subtask A and, Sub-task C categorizing targeted offense identified by Sub-task B. Data was also unbalanced leading to more importance for majority class but after applying cost function we found that accuracy was decreased with increased errors in identification of majority class. Future Work For future work, we would like to use additional datasets like TRAC-1 data (Kumar et al., 2018), , and would collect data from Twitter to get diverse data. To be consistent with substantial research done in recent years we want to employ a combination of textual features like the bag of words n-grams, capitalized characters, sentiment scores, e.t.c. Also, we want to focus more on specific features like semantics and linguistic features intrinsic to hate/offensive rather than just generic text-based features. For that, we want to use character level deep LSTM which can be used to extract the semantic and syntactic information. Finally, we want to explore more about the similarities and dissimilarities between the profanity and hate speech, establishing more profound way of extracting features in order to make the detection system more responsive. Figure 1 1: a) Sub-task A, LSTM (epoch=5, dropout= 0.2), b) Sub-task B, LSTM (dropout=0.2, epochs=20), c) Sub-task C, LSTM(dropout=0. Table 1: Results for Sub-task A.System F1 (macro) Accuracy All NOT baseline 0.4189 0.7209 All OFF baseline 0.2182 0.2790 LSTM (5,0.2) 0.7382 0.8128 System F1 (macro) Accuracy All TIN baseline 0.4702 0.8875 All UNT baseline 0.1011 0.1125 LSTM (5,0.2) 0.5925 0.8167 LSTM (20,0.2) 0.5291 0.6125 LSTM (20,0.2) and 0.6171 0.7667 word list Table 2 : 2Results for Sub-task B. Deep learning for detecting cyberbullying across multiple social media platforms. 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ALICO: A multimodal corpus for the study of active listening

The Active Listening Corpus (ALICO) is a multimodal database of spontaneous dyadic conversations with diverse speech and gestural annotations of both dialogue partners. The annotations consist of short feedback expression transcription with corresponding communicative function interpretation as well as segmentation of interpausal units, words, rhythmic prominence intervals and vowel-to-vowel intervals. Additionally, ALICO contains head gesture annotation of both interlocutors. The corpus contributes to research on spontaneous human-human interaction, on functional relations between modalities, and timing variability in dialogue. It also provides data that differentiates between distracted and attentive listeners. We describe the main characteristics of the corpus and present the most important results obtained from analyses in recent years.

ALICO: A multimodal corpus for the study of active listening Hendrik Buschmeier CITEC Faculty of Technology Zofia Malisz CITEC Faculty of Linguistics and Literary Studies Bielefeld University BielefeldGermany Joanna Skubisz Faculty of Linguistics and Literary Studies Bielefeld University BielefeldGermany Marcin Włodarczak wlodarczak@ling.su.se Faculty of Linguistics and Literary Studies Bielefeld University BielefeldGermany Department of Linguistics Stockholm University StockholmSweden Ipke Wachsmuth CITEC Faculty of Technology Stefan Kopp CITEC Faculty of Technology Petra Wagner CITEC Faculty of Linguistics and Literary Studies Bielefeld University BielefeldGermany ALICO: A multimodal corpus for the study of active listening active listeningmultimodal feedbackhead gesturesattention The Active Listening Corpus (ALICO) is a multimodal database of spontaneous dyadic conversations with diverse speech and gestural annotations of both dialogue partners. The annotations consist of short feedback expression transcription with corresponding communicative function interpretation as well as segmentation of interpausal units, words, rhythmic prominence intervals and vowel-to-vowel intervals. Additionally, ALICO contains head gesture annotation of both interlocutors. The corpus contributes to research on spontaneous human-human interaction, on functional relations between modalities, and timing variability in dialogue. It also provides data that differentiates between distracted and attentive listeners. We describe the main characteristics of the corpus and present the most important results obtained from analyses in recent years. Introduction Multimodal corpora are a crucial part of scientific research investigating human-human interaction. Recent developments in data collection of spontaneous communication emphasise the co-influence of verbal and non-verbal behaviour between dialogue partners (Oertel et al., 2013). In particular, the listener's role during interaction has attracted attention in both fundamental research and technical implementations (Sidner et al., 2004;Kopp et al., 2008;Truong et al., 2011;de Kok and Heylen, 2011;. The Active Listening Corpus (ALICO) collected at Bielefeld University is a multimodal corpus built to study verbal/vocal and gestural behaviour in face-to-face communication, with a special focus on the listener. The communicative situation in ALICO, interacting with a storytelling partner, was designed to facilitate active and spontaneous listening behaviour. Although the active speaker usually fulfills the more dynamic role in dialogue, the listener contributes to successful grounding by giving verbal and non-verbal feedback. Short vocalisations like 'mhm', 'okay', 'm' that constitute listener's turns express the ability and willingness to interact, understand, convey emotions and attitudes and constitute an integral part of face-to-face communication. We use the term short feedback expressions (SFE; cf. Schegloff, 1982;Ward and Tsukahara, 2000;Edlund et al., 2010) and classify SFEs using an inventory of communicative feedback functions (Buschmeier et al., 2011). Both SFE transcriptions and feedback function labels are annotated and included in the ALICO database. Apart from vocal feedback, listeners show their engagement in conversation by means of non-vocal behaviour such The first four authors are listed in alphabetical order. as head gestures. Visual feedback emphasises the degree of listener involvement in conversation and encourages the speaker to stay active during his or her speech at turn relevance places (Wagner et al., 2014;Heldner et al., 2013). Head movements also co-occur with mutual gaze (Peters et al., 2005) and correlate with active listening displays. ALICO contains head gesture annotations, including gesture type labeling such as nod, shake or tilt, for both interlocutors. First evaluations of the head gesture inventory can be found in (Kousidis et al., 2013). Additionally, the ALICO conversational sessions included a task in which the listener's attention was experimentally manipulated, with a view to revealing communicative strategies listeners use when distracted. Previous studies have reported that the listener's attentional state has an influence on the quality of speaker's narration and the number of feedback occurrences in dialogue. Bavelas et al. (2000) carried out a study in which the listener was distracted by an ancillary task during a conversational session. The findings have shown the preoccupied listener to produce less contextspecific feedback. These findings are in accordance with the results of Kuhlen and Brennan (2010). All the above authors confirm that distractedness of the listener affects the behaviour of the interlocutor and interferes with the speaker's speech. Several analyses performed so far on the ALICO corpus deal with the question of how active listening behaviour changes when the attention level is varied in dialogue (Buschmeier et al., 2011;Włodarczak et al., 2012). The corpus was also annotated for the purpose of studying temporal relations across modalities, within and between interlocutors. The rhythmic annotation layer (vocalic beat intervals and rhythmic prominence intervals) has served as input for coupled oscillator models providing an important 3 m storyteller listener Figure 1: Screenshot from a video file capturing the whole scene (long camera shot), and perspectives of each participant (medium camera shots). The listener is being distracted by counting words beginning with letter 's' and pressing a button on a remote control hidden in her left hand. testbed for hypotheses concerning interpersonal entrainment in dialogue . First evaluations of entrained timing behaviour in two modalities implemented in an artificial agent are reported on by Inden et al. (2013). By enabling a targeted study of active listening that includes varying listener attention levels, the ALICO corpus contributes to better understanding of human discourse. Analysis outcomes have proven useful in applications such as artificial listening agents (Inden et al., 2013). The corpus also provides a unique environment for studying temporal interactions between multimodal phenomena. In the present report we describe the main corpus characteristics and summarise the most important results obtained from analyses done so far. Corpus architecture ALICO's audiovisual dataset consists of 50 same-sex conversations between 25 German native speaker dyads (34 female and 16 male). All the participants were students at Bielefeld University and, apart from 4 dialogue partners, did not know each other before the study. Participants were randomly assigned to dialogue pairs and rewarded for their effort with credit points or 4 euros. No hearing impairments were reported by the participants. The total length of the recorded material is 5 hours 31 minutes. Each dialogue has a mean length of 6 minutes and 36 seconds (Min = 2:00 min, Max = 14:48 min, SD = 2:50 min). A face-to-face dialogue study forms the core of the corpus. The study was carried out in a recording studio (Mint-Lab; Kousidis et al., 2012) at Bielefeld University. Dialogue partners were placed approximately three metres apart in a comfortable setting (see Figure 1). Participants wore high quality headset microphones (Sennheiser HSP 2 and Sennheiser ME 80), another condenser microphone captured the whole scene and three Sony VX 2000 E camcorders recorded the video. One of the dialogue partners (the 'storyteller') told two holiday stories to the other participant (the 'listener'), who was instructed to listen actively, make remarks and ask questions, if appropriate. Participants were assigned to their roles randomly and received their instructions separately. Furthermore, similar to Bavelas et al. (2000), the listener was engaged in an ancillary task during one of the stories (the order was counterbalanced across dyads): he or she was instructed to press a button on a hidden remote control (see Figure 1) every time they heard the letter 's' at the beginning of a word. The letter 's' is the second most common wordinitial letter in German and often corresponds to perceptually salient sibilant sounds. A fourth audio channel was used to record the 'clicks' synthesised by a computer when listeners pressed the button on the remote control. The listeners were also required to retell the stories after the study and to report on the number of 's' words. The storyteller was aware that the listener is going to search for something in the stories; no further information about the details of the listener's tasks was disclosed to the storyteller. Speech annotation Annotation of the interlocutors' speech was performed in Praat (Boersma and Weenink, 2013), independently from head gesture annotation. Speech annotation tiers differ for listener and speaker role (see Table 1 for an overview of the annotation tiers). The listener The listener's SFEs with corresponding communicative feedback functions have been annotated in 40 dialogues thus far, i.e. in 20 sessions involving the distraction task and 20 sessions with no distractions. Segmentation of the listener SFEs was carried out automatically in Praat based on signal intensity and was subsequently checked manually. After that, another annotator transcribed the pre-segmented SFEs according to German orthographic conventions. Longer listener turns were marked but not transcribed. A total number of 1505 feedback signals was identified. The mean ratio of time spent producing feedback signals to other listener turns ("questions and remarks", normalised by their respective mean duration per dialogue) equals 65% (Min = 32%; Max = 100%), suggesting that the corpus contains a high density of spoken feedback phenomena. The mean feedback rate is 10 signals per minute, mean turn rate is 5 turns per minute, with a significantly higher turn rate in the attentive listener (6 turns/min) than in the distracted listener (4 turns/min, two-sample Wilcoxon rank sum test: p < .01). Three labelers independently assigned feedback functions to listener SFEs in each dialogue. A feedback function inventory was developed and first described in Buschmeier et al. (2011), largely based on Allwood et al. (1992). The inventory involves core feedback functions that signal perception of the speaker's message (category P1), understanding (category P2) of what is being said, acceptance/agreement (category P3) with the speaker's message. These levels can be treated as a hierarchy with an increasing value judgement of grounding 'depth'. The negation of the respective functions was marked as N1-N3. An option to extend listener (Kopp et al., 2008). Modifier A was also appended to the resulting majority label if it was used by at least one annotator so that subtle (especially emotion-related) distinctions were preserved. Modifiers C and E referred to feedback expressions occurring at the beginning or the end of a discourse segment initiated by the listener (Gravano et al., 2007). The most frequent SFEs with corresponding feedback functions found in the corpus are presented in Table 2. Communicative context was carefully and independently taken into account by each annotator during feedback function interpretation. Majority labels between annotators determined the feedback functions in the final version of the listener's speech annotation. Disagreements in the labeling, i.e. cases which could not be settled by majority labels, corresponding to 10% of all feedback expressions, were discussed and resolved. The storyteller The storyteller's speech was annotated in 20 sessions involving no distractions. The following rhythmic phenomena were delimited in the storyteller's speech: vowel-to-vowel intervals, rhythmic prominence intervals and minor phrases (Breen et al., 2012). Vowel onsets were extracted semiautomatically from the data. Algorithms in Praat ( Barbosa, 2006) were used first, after which the resulting segmentation was checked for accuracy by two annotators who inspected the spectrogram, formants and pitch curve in Praat as well as verified each other's corrections. Rhythmic prominences, judged perceptually, were marked whenever a 'beat' on a given syllable was perceived, regardless of lexical or stress placement rules (Breen et al., 2012). Phrase boundaries were marked manually every time a perceptually discernible gap in the storyteller's speech occurred. The resulting minimum pause length of 60 msec is comparable to pauses between so called Interpausal Units as segmented automatically, in e.g., Beňus et al. (2011). Interannotator agreement measurements regarding prominence and phrase annotations are forthcoming. In the study by Inden et al. (2013), the prosodic annotation carried out on storyteller's speech served as input to the modeling of local timing for an embodied converstational agent. Apart from manual rhythmic segmentation, forced alignment was carried out on the storytellers' speech, using the WebMAUS tool (Kisler et al., 2012). Automatic segmentation and labeling facilitates work with large speech data and is less time-consuming, expensive and error prone than manual annotation. It produces a fairly accurately aligned and multi-layered annotation on small linguistic units, in e.g. segmented data. WebMAUS output provides tiers with word segmentation, SAMPA transcription and vowel-consonant segmentation). Head gesture annotation The corpus contains gestural annotation of both dialogue partners (see Table 1). Annotations were performed in ELAN (Wittenburg et al., 2006) by close inspection of the muted video (stepping through the video frame-by-frame). Uninterrupted, communicative head movements were segmented as annotation events. Movements resulting from inertia, slow body posture shifts, ticks, etc. were excluded from the annotation. Thus obtained head gesture units (HGUs) contain perceptually coherent, communicative head movement sequences, without perceivable gaps. Each constituent gesture in an HGU label was marked for head gesture type. The full inventory of gesture types is presented in Table 3. Prototypical movements along particular axes are presented in Figure 2. Mathematical conventions for 3D spatial coordinates are used in Figure 2, as done in biomechanical and physiological studies (Yoganandan et al., 2009) on head movements. The identified constituent gestures in each HGU were also annotated for the number of gesture cycles and, where applicable, the direction of the gesture (left or right, from the perspective of the annotator). For example, the label nod-2+tilt-1-right describes a sequence consisting of two different movement types with two-and one cycle, respectively, where the head tilting is performed to the right side of the screen. The resulting head gesture labels describe simple, complex or single gestural units. Complex HGUs denote multiple head movement types with different number of cycles, whereas single units refer to one head movement with one re- petition. Simple head movement types consist of one movement type and at least two cycles (see Figure 3). The annotated HGU labels may provide information about the following features: complexity (the number of subsequent gesture types in the phrase) or cycle frequencies of all HGUs and both dialogue partners. The listener Listener head gestures were annotated in 40 dialogues so far, i.e. in 20 sessions involving the distraction task and 20 sessions with no distractions. Listener head gesture type categories were found to be limited to the subset of the inventory presented in Table 3, namely to nod, shake, tilt, turn, jerk, protrusion and retraction (Włodarczak et al., 2012). The most frequent head gestures found for listeners in the corpus are presented in Table 4. Listener HGUs were labeled and checked for errors by two annotators, however no interannotator agreement was calculated. The storyteller Co-speech head gestures produced by the storyteller are much more differentiated than those of the listener. Consequently, we used an extended inventory, as described and evaluated on a different German spontaneous dialogue corpus by Kousidis et al. (2013) and presented in Table 3. Several additional categories were necessary to fully describe the variety of head movements in the storyteller, e.g. slide, shift and bobble. The inter-annotator agreement values found for the full inventory in Kousidis et al. (2013) equaled 77% for event segmentation, 74% for labeling and 79% for duration. The labeling of storyteller's head gestures has been completed in 9 conversations in the no-distraction subset so far, as the density and complexity of gestural phenomena is much greater than in the listener. Analysis and results Analysis toolchain Typically, ALICO annotations prepared in Praat and ELAN are combined and processed using TextGridTools (Buschmeier and Włodarczak, 2013, http://purl.org/net/tgt), a Python toolkit for manipulating and querying annotations stored in Praat's TextGrid format. Data analyses are then carried out in a Python-based scientific computing environment (IPython, NumPy, pandas, SciPy, matplotlib; McKinney, 2012) as well as in R when more complex statistical methods are needed. Results Analyses on the ALICO corpus so far show that distracted listeners communicate understanding by feedback significantly less frequently than attentive listeners (Buschmeier et al., 2011). They do however, communicate acceptance of the interlocutor's message, thereby conveying implied understanding. We discuss this strategy in a few possible pragmatic scenarios in Buschmeier et al. (2011). Furthermore, the ratio of non-verbal to verbal feedback significantly increases in the distracted condition, suggesting that distracted listeners choose a more basic modality of expressing feedback, i.e. with head gestures rather than verbally (Włodarczak et al., 2012). We also found that spoken feedback expressions of distracted listeners have a different prosodic profile than those produced by attentive listeners . Significant differences were found in the intensity and pitch domain. Regarding the interaction between modalities and feedback functions in the corpus, Włodarczak et al. (2012) found that in HGUs overlapping with verbal feedback expressions (bimodal feedback), nods, especially multiple ones, predominate. However, the tilt was found to be more characteristic of higher feedback categories in general, while the jerk was found to express understanding. A significant variation shown in the use of the jerk, between distracted and attentive listeners (Włodarczak et al., 2012) is in accordance with the previous result in Buschmeier et al. (2011). Hitherto ALICO provided two converging sources of evidence confirming the hypothesis that communicating understanding is a marker of attentiveness. Beyond the analysis of correlates of distractedness and multimodal feedback function, Inden et al. (2013) report on timing analyses of multimodal feedback in ALICO. The analysis, conducted on attentive listeners only, was implemented in an artificial agent by Inden et al. (2013). The results indicate that listeners distribute head gestures uniformly across the interlocutor's utterances, while the probability of verbal and bimodal feedback increases sharply towards the end of the storyteller's turn and into the following pause. While the latter hypothesis is established, the former was not strongly attested in the literature: the specific nature of the conversational situation in ALICO, strongly concentrated on active listening, provided a sufficiently constrained setting, revealing the function of the visual modality in this discourse context. Most recent results suggest that onsets of Head Gesture Units in attentive listeners are timed with the interlocutor's vowel onsets, providing evidence that listeners are entrained to the vocalic rhythms of the dialogue partner (Malisz and Wagner, under review). Conclusions and future work The Active Listening Corpus offers an opportunity to study multimodal and cognitive phenomena that characterise listeners in spontaneous dialogue and to observe mutual influences between dialogue partners. The annotations are being continuously updated. Work on additional tiers containing lexical, morphological information, turn segmentations and further prosodic labels is ongoing. A corpus extension is planned with recordings using motion capture and gaze tracking available in the MintLab (Kousidis et al., 2012). Figure 2 :Figure 3 : 23Schematic overview of rotations and translations along three axes as well as example movements most frequently used in communicative head gesturing (reprinted fromWagner et al. (2014) with permission from Elsevier). Examples of (a) simple, (b) single and (c) complex head movement types found in the ALICO inventory. Table Table 2 : 2Proportions of the most frequent German SFEs (short feedback expressions) and their corresponding feed- back functions (P1: perception, P2: understanding, P3: ac- ceptance/agreement and other) produced by listeners in forty ALICO dialogues. % P1 P2 P3 other ∑ ja 6.9 6.4 5.4 7.6 26.3 m 13.2 5.5 1.5 2.6 22.8 others 0.2 2.2 2.5 15.1 19.9 mhm 6.6 4.2 0.4 1.5 12.7 okay 0.2 5.4 2.5 2.7 10.8 achso 0 1.4 0 1.8 3.2 cool 0 0 0 1.5 1.5 klar 0 0.1 0.9 0.5 1.4 ah 0.2 0.1 0 1.1 1.4 ∑ 27.2 25.2 13.2 34.4 100.0 feedback function labels by three modifiers was available to the annotators, where modifier A referred to the listener's emotions/attitudes co-occurring with SFEs, leading to labels such as P3A Table 3 : 3Head gesture type inventory (adapted fromKousidis et al. (2013).Label Description nod Rotation down-up jerk 'Inverted nod', head upwards tilt 'Sideways nod' shake Rotation left-right horizontally protrusion Pushing the head forward retraction Pulling the head back turn Rotation left OR right bobble Shaking by tilting left-right slide Sideways movement(no rotation) shift Repeated slides left-right waggle Irregular connected movement Table 4 : 4Frequency table of listener's head movement types found in 40 dialogues in the Active Listening Corpus.Listener's head movement types count % nod 1685 69.06 jerk 105 4.30 shake 89 3.65 turn 48 1.97 retraction 30 1.23 protrusion 6 0.25 complex HGUs 385 15.78 other 92 3.76 ∑ 2440 100 Acknowledgements On the semantics and pragmatics of linguistic feedback. Jens Allwood, Joakim Nivre, Elisabeth Ahlsén, Journal of Semantics. 9Jens Allwood, Joakim Nivre, and Elisabeth Ahlsén. 1992. On the semantics and pragmatics of linguistic feedback. Journal of Semantics, 9:1-26. Incursõeses em torno do ritmo da fala. 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Vi-xfst: A Visual Regular Expression Development Environment for Xerox Finite State Tool

This paper describes Vi-xfst, a visual interface and a development environment, for developing finite state language processing applications using the Xerox Finite State Tool, xfst. Vi-xfst lets a user construct complex regular expressions via a drag-anddrop visual interface, treating simpler regular expressions as "Lego Blocks." It also enables the visualization of the structure of the regular expression components, providing a bird's eye view of the overall system, enabling a user to easily understand and track the structural and functional relationships among the components involved. Since the structure of a large regular expression (built in terms of other regular expressions) is now transparent, users can also interact with regular expressions at any level of detail, easily navigating among them for testing. Vi-xfst also keeps track of dependencies among the regular expressions at a very finegrained level. So when a certain regular expression is modified as a result of testing, only the dependent regular expressions are recompiled resulting in an improvement in development process time, by avoiding file level recompiles which usually causes redundant regular expression compilations.

Vi-xfst: A Visual Regular Expression Development Environment for Xerox Finite State Tool Kemal Oflazer oflazer@sabanciuniv.edu Human Language and Speech Technology Laboratory Sabancı University Istanbul Turkey Yasin Yılmaz yyilmaz@su.sabanciuniv.edu Human Language and Speech Technology Laboratory Sabancı University Istanbul Turkey Vi-xfst: A Visual Regular Expression Development Environment for Xerox Finite State Tool This paper describes Vi-xfst, a visual interface and a development environment, for developing finite state language processing applications using the Xerox Finite State Tool, xfst. Vi-xfst lets a user construct complex regular expressions via a drag-anddrop visual interface, treating simpler regular expressions as "Lego Blocks." It also enables the visualization of the structure of the regular expression components, providing a bird's eye view of the overall system, enabling a user to easily understand and track the structural and functional relationships among the components involved. Since the structure of a large regular expression (built in terms of other regular expressions) is now transparent, users can also interact with regular expressions at any level of detail, easily navigating among them for testing. Vi-xfst also keeps track of dependencies among the regular expressions at a very finegrained level. So when a certain regular expression is modified as a result of testing, only the dependent regular expressions are recompiled resulting in an improvement in development process time, by avoiding file level recompiles which usually causes redundant regular expression compilations. Introduction Finite state machines are widely used in many language processing applications to implement components such as tokenizers, morphological analyzers/generators, shallow parsers, etc. Large scale finite state language processing systems built using tools such as the Xerox Finite State Tool (Karttunen et al., 1996;Karttunen et al., 1997;Beesley and Karttunen, 2003), van Noord's Prolog-based tool (van Noord, 1997), the AT&T weighted finite state machine suite (Mohri et al., 1998) or the IN-TEX System (Silberztein, 2000), involve tens or hundreds of regular expressions which are compiled into finite state transducers that are interpreted by the underlying run-time engines of the (respective) tools. Developing such large scale finite state systems is currently done without much of a support for the "software engineering" aspects. Regular expressions are constructed manually by the developer with a text-editor and then compiled, and the resulting transducers are tested. Any modifications have to be done afterwards on the same text file(s) and the whole project has to be recompiled many times in a development cycle. Visualization, an important aid in understanding and managing the complexity of any large scale system, is limited to displaying the finite state machine graph (e.g., Gansner and North (1999), or the visualization functionality in INTEX (Silberztein, 2000)). However, such visualization (sort of akin to visualizing the machine code of a program written in a high-level language) may not be very helpful, as developers rarely, and possibly never, think of such large systems in terms of states and transitions. The relationship between the regular expressions and the finite state machines they are compiled into are opaque except for the simplest of regular expressions. Further, the size of the resulting machines, in terms of states and transitions, is very large, usually in the thousands to hundreds of thousands states, if not more, making such visualization meaningless. On the other hand, it may prove quite useful to visualize the structural components of a set of regular expressions and how they are put together, much in the spirit of visualizing the relationships amongst the data objects and/or modules in a large program. However such visualization and other maintenance operations for large finite state projects spanning over many files, depend on tracking the structural relationships and dependencies among the regular expressions, which may prove hard or inconvenient when text-editors are the only development tool. This paper presents a visual interface and development environment, Vi-xfst (Yılmaz, 2003), for the Xerox Finite State Tool, xfst, one of the most sophisticated tools for constructing finite state language processing applications (Karttunen et al., 1997). Vi-xfst enables incremental construction of complex regular expressions via a drag-and-drop interface, treating simpler regular expressions as "Lego Blocks". Vi-xfst also enables the visualization of the structure of the regular expression components, so that the developer can have a bird's eye view of the overall system, easily understanding and tracking the relationships among the components involved. Since the structure of a large regular expression (built in terms of other regular expressions) is now transparent, the developer can interact with regular expressions at any level of detail, easily navigating among them for testing and debugging. Vi-xfst also keeps track of the dependencies among the regular expressions at a very fine-grained level. So, when a certain regular expression is modified as a result of testing or debugging, only the dependent regular expressions are recompiled. This results is an improvement in development time, by avoiding file level recompiles which usually causes substantial redundant regular expression compilations. In the following sections, after a short overview of the Xerox xfst finite state machine development environment, we describe salient features of Vi-xfst through some simple examples. Overview of xfst xfst is a sophisticated command-line-oriented interface developed by Xerox Research Centre Europe, for building large finite state transducers for language processing applications. Users of xfst employ a high-level regular expression language which provides an extensive palette of high-level operators. 1 Such regular expressions are then compiled into finite state transducers and interpreted by a run-time engine built into the tool. xfst also provides a further set of commands for combining, testing and inspecting the finite state transducers produced by the regular expression compiler. Transducers may be loaded onto a stack maintained by the system, and the topmost transducer on the stack is available for testing or any further operations. Transducers can also be saved to files which can later be reused or used by other programs in the Xerox finite state suite. Although xfst provides quite useful debugging facilities for testing finite state networks, it does not provide additional functionality beyond the command-1 Details of the operators are available at http://www. xrce.xerox.com/competencies/content-analysis/ fsCompiler/fssyntax.html and http://www.xrce. xerox.com/competencies/content-analysis/ fsCompiler/fssyntax-explicit.html. line interface to alleviate the complexity of developing large scale projects. Building a large scale finite state transducer-based application such as a morphological analyzer or a shallow finite state parser, consisting of tens to hundreds of regular expressions, is also a large software engineering undertaking. Large finite state projects can utilize the make functionality in Linux/Unix/cygwin environments, by manually entering (file level) dependencies between regular expressions tered into a makefile. The make program then invokes the compiler at the shell level on the relevant files by tracking the modification times of files. Since whole files are recompiled at a time even when a very small change is made, there may be redundant recompilations that may increase the development time. Vi-xfst -a visual interface to xfst As a development environment, Vi-xfst has two important features that improve the development process of complex large scale finite state projects with xfst. 1. It enables the construction of regular expressions by combining previously defined regular expressions via a drag-and-drop interface. 2. As regular expressions are built by combining other regular expressions, Vi-xfst keeps track of the topological structure of the regular expression -how component regular expressions relate to each other. It derives and maintains the dependency relationships of a regular expression to its components, and via transitive closure, to the components they depend on. This structure and dependency relations can then be used to visualize a regular expression at various levels of detail, and also be used in very fine-grained recompilations when some regular expressions are modified. Using Vi-xfst In this section, we describe important features Vixfst through some examples. 2 The first example is for a simple date parser described in Karttunen et al. (1996). This date parser is implemented in xfst using the following regular expressions: 3 The most important regular expression above is AllDates, a pattern that describes a set of calendar dates. It matches date expressions such as Sunday, January 23, 2004 or just Monday. The subsequent regular expression AllDatesParser uses the longest match downward bracket operator (the combination of @-> and ...) to define a transducer that puts [ and ] around the longest matching patterns in the input side of the transducer. Figure 1 shows the state of the screen of Vi-xfst just after the AllDatesParser regular expression is constructed. In this figure, the left side window shows, under the Definitions tab, the regular expressions defined. The top right window shows the template for the longest match regular expression slots filled by drag and drop from the list on the left. The AllDatesParser regular expression is entered by selecting the longest-match downward bracket operator (depicted with the icon @-> with ... underneath) from the palette above, which then inserts a template that has empty slots -three in this case. The user then "picks up" regular expressions from the left and drops them into the appropriate slots. When the regular expression is completed, it can be sent to the xfst process for compilation. The bottom right window, under the Messages tab, shows the messages received from the xfst process running in the background during the compilation of this and the previous regular expressions. Figure 2 shows the user testing a regular expression loaded on to the stack of the xfst. The left window under the Networks tab, shows the networks pushed on to the xfst stack. The bottom right window under Test tab lists a series of input, one of which can be selected as the input string and then applied up or down to the topmost network on the stack. 4 The result of application appears on the bottom pane on the right. In this case, we see the input with the brackets inserted around the longest matching date pattern, Sunday, January 23, 2004 in this case. Visualizing regular expression structure When developing or testing a large finite state transducer compiled from a regular expression built as a hierarchy of smaller regular expressions, it is very helpful, especially during development, to visualize the overall structure of the regular expression to easily see how components relate to each other. Vi-xfst provides a facility for viewing the structure of a regular expression at various levels of detail. To illustrate this, we use a simple cascade of transducers simulating a coke machine dispensing cans of soft drink when the right amount of coins are dropped in. Figure 4 shows the rendering of the same transducer after the top transducer is expanded where we see the union of three cross-product operators, while Figure 5 shows the rendering after both components are expanded. When a regular expression laid out, the user can select any of the regular expressions displayed and make that the active transducer for testing (that is, push it onto the top of the xfst transducer stack) Figure 3: Simplest view of a regular expression and rapidly navigate among the regular expressions without having to remember their names and locations in the files. As we re-render the layout of a regular expression, we place the components of the compose and crossproduct operators in a vertical layout, and others in a horizontal layout and determine the best layout of the components to be displayed in a rectangular bounding box. It is also possible to render the upward and downward replace operators in a vertical layout, but we have opted to render them in a hori-zontal layout (as in Figure 1). The main reason for this is that although the components of the replace part of such an expression can be placed vertically, the contexts need to be placed in a horizontal layout. A visualization of a complex network employing a different layout of the replace rules is shown in Figure 6 with the Windows version of Vi-xfst. Here we see a portion of a Number-to-English mapping network 7 where different components are visualized at different structural resolutions. Interaction of Vi-xfst with xfst Vi-xfst interacts with xfst via inter-process communication. User actions on the Vi-xfst side get translated to xfst commands and get sent to xfst which maintains the overall state of the system in its own universe. Messages and outputs produced by xfst are piped back to Vi-xfst, which are then parsed and presented back to the user. If a direct API is available to xfst, it would certainly be possible to implement tighter interface that would provide better error-handling and slightly improved interaction with the xfst functionality. All the files that Vi-xfst produces for a project are directly compatible with and usable by xfst; that is, as far as xfst is concerned, those files are valid regular expression script files. Vi-xfst maintains all the additional bookkeeping as comments in these files and such information is meaningful only to Vi-xfst and used when a project is re-loaded to recover all dependency and debugging information originally computed or entered. Currently, Vi-xfst has some primitive facilities for directly importing hand generated files for xfst to enable manipulation of already existing projects. Selective regular expression compilation Selective compilation is one of the simple facilities available in many software development environments. A software development project uses selective compilation to compile modules that have been modified and those that depend (transitively) in some way (via say header file inclusion) to the modified modules. This selective compilation scheme, typically known as the make operation, depends on a manually or automatically generated makefile capturing dependencies. It can save time during development as only the relevant files are recompiled after a set of modifications. In the context of developing large scale finite state language processing application, we encounter the same issue. During testing, we recognize that a certain regular expression is buggy, fix it, and then have to recompile all others that use that regular expression as a component. It is certainly possible to use make and recompile the appropriate regular expression files. But, this has two major disadvantages: • The user has to manually maintain the makefile that captures the dependencies and invokes the necessary compilation steps. This may be a non-trivial task for a large project. • When even a singular regular expression is modified, the file the regular expression resides in, and all the other files containing regular expressions that (transitively) depend on that file, have to be recompiled. This may waste a considerable amount of time as many other regular expressions that do not need to be recompiled, are compiled just because they happen to reside in the same file with some other regular expression. Since some regular expressions may take a considerable amount of time to compile, this unnecessarily slows down the development process. Vi-xfst provides a selective compilation functionality to address this problem by automatically keeping track of the regular expression level dependencies as they are built via the drag-and-drop interface. This dependency can then be exploited by Vi-xfst when a recompile needs to be done. Figure 7 shows the directed acyclic dependency graph of the regular expressions in Section 3.1, extracted as the regular expressions are being defined. A node in this graph represents a regular expression that has been defined, and when there is an arc from a node to another node, it indicates that the regular expression at the source of the arc directly depends on the regular expression at the target of the arc. For instance, in Figure 7, the regular expression AllDates directly depends on the regular expressions Date, DateYear, Month, SPACE, and def16. After one or more regular expressions are modified, we first recompile (by sending a define command to xfst) those regular expressions, and then recompile all regular expressions starting with immediate dependents and traversing systematically upwards to the regular expressions of all "top" nodes on which no other regular expressions depend, making sure that • all regular expressions that a regular expression depends on and have to be recompiled, are recompiled before that regular expression is recompiled, and • every regular expression that needs to be recompiled is recompiled only once. To achieve these, we compute the subgraph of the dependency graph that has all the nodes corresponding to the modified regular expressions and any other regular expressions that transitively depends on these regular expressions. Then, a topological sort of the resulting subgraph gives a possible linear ordering of the regular expression compilations. For instance for the dependency subgraph in Figure 7, if the user modifies the definition of the network 1to9, the dependency subgraph of the regular expressions that have to be recompiled would be the one shown in Figure 8. A (reverse) topological sort of this depen- Conclusions and future work We have described Vi-xfst, a visual interface and a development environment for the development of large finite state language processing application components, using the Xerox Finite State Tool xfst. In addition to a drag-and-drop user interface for constructing regular expressions in a hierarchical manner, Vi-xfst can visualize the structure of a regular expression at different levels of detail. It also keeps track of how regular expressions depend on each other and uses this dependency information for selective compilation of regular expressions when one or more regular expressions are modified during development. The current version of Vi-xfst lacks certain features that we plan to add in the future versions. One important functionality that we plan to add is user customizable operator definitions so that new regular expression operators can be added by the user as opposed to being fixed at compile-time. The user can define the relevant aspects (slots, layout) of an operator in a configuration file which can be read at the program start-up time. Another important feature is the importing of libraries of regular expressions much like symbol libraries in drawing programs and the like. The interface of Vi-xfst to the xfst itself is localized to a few modules. It is possible to interface with other finite state tools by rewriting these modules and providing user-definable operators. Figure 1 : 1Constructing a regular expression via the drag-and-drop interfaceThe last regular expression here BuyCoke defines a transducer that consist of the composition of two other transducers. The transducer [ CENTS ]* maps any sequence of symbols n, d, and q representing, nickels, dimes and quarters, into the appropriate number of cents, represented as a sequence of c symbols. The transducer SixtyFiveCents maps a sequence of 65 c symbols to the symbol PLONK representing a can of soft drink (falling). Figure 3 3shows the simplest visualization of the BuyCoke transducer in which only the top level components of the compose operator (.o.) are displayed. The user can navigate among the visible regular expressions and "zoom" into any regular expressions further, if necessary. For instance, Figure 2 : 2Testing a regular expression. Figure 4 : 4View after the top regular expression is expanded. Figure 6 :Figure 5 : 65Mixed visualization of a complex network View after both regular expressions are expanded. Figure 7 : 7The dependency graph for the regular expressions of the DateParser. Figure 8 : 8The dependency subgraph graph induced by the regular expression 1to9.dency subgraph gives us one of the possible orders for recompiling only the relevant regular expressions as: 1to9, 0To9, Date, Year, DateYear, AllDates, AllDatesParser 5 The regular expressions for this example are: 6define N [ n ]; define D [ d ]; define Q [ q ]; define DefPLONK [ PLONK ]; define CENT [ c ]; define SixtyFiveCents [ [ [ CENT ]^65 ] .x. DefPLONK ]; define CENTS [[N .x. [[ CENT ]^5 ]| [D .x. [[ CENT ]^10 ]]]| [Q .x. [[ CENT ]^25 ]]]; define BuyCoke [ [ [ CENTS ]* ] .o. SixtyFiveCents ]; The examples we provide are rather simple ones, as length restrictions do not allow us to include large figures to visualize complex finite state projects.3 The define command defines a named regular expression which can then be subsequently referred to in later regular expressions. | denotes the union operator. 0 (without quotes) denotes the empty string traditionally represented by in the literature. The quotes " are used to literalize sequence of symbols which have special roles in the regular expression language. xfst only allows the application of inputs to the topmost network on the stack. 5 See http://www.xrce.xerox.com/competencies/ content-analysis/fsCompiler/fsexamples.html for this example.6 The additional operators in this example are: .x. representing the cross-product and .o. representing the composition of transducers, and caret operator (ˆ)denoting the repeated concatenation of its left argument as many times as indicated by its right argument. Due to Lauri Karttunen; see http://www.cis. upenn.edu/~cis639/assign/assign8.html for the xfst script for this transducer. It maps numbers like 1234 into English strings like One thousand two hundred and thirty four. AcknowledgmentsWe thank XRCE for providing us with the xfst and other related programs in the finite state suite. Finite State Morphology. R Kenneth, Lauri Beesley, Karttunen, CSLI PublicationsStanford UniversityKenneth R. Beesley and Lauri Karttunen. 2003. Fi- nite State Morphology. CSLI Publications, Stan- ford University. R Emden, Stephen C Gansner, North, An open graph visualization system and its applications to software engineering. Software -Practice and Experience. Emden R. Gansner and Stephen C. North. 1999. An open graph visualization system and its applications to software engineering. Software -Practice and Experience. Regular expressions for language engineering. Lauri Karttunen, Jean-Pierre Chanod, Gregory Grefenstette, Anne Schiller, Natural Language Engineering. 24Lauri Karttunen, Jean-Pierre Chanod, Gregory Grefenstette, and Anne Schiller. 1996. Regular ex- pressions for language engineering. Natural Lan- guage Engineering, 2(4):305-328. . Lauri Karttunen, Tamas Gaal, Andre Kempe, Lauri Karttunen, Tamas Gaal, and Andre Kempe. Xerox Finite-State Tool. Xerox Research Centre EuropeTechnical reportXerox Finite-State Tool. Technical report, Xerox Research Centre Europe. A rational design for a weighted finitestate transducer library. Mehryar Mohri, Fernando Pereira, Michael Riley, Lecture Notes in Computer Science. Springer Verlag1436Mehryar Mohri, Fernando Pereira, and Michael Ri- ley. 1998. A rational design for a weighted finite- state transducer library. In Lecture Notes in Com- puter Science, 1436. Springer Verlag. FSA utilities: A toolbox to manipulate finite state automata. Max Silberztein, Automata Implementation, number 1260 in Lecture Notes in Computer Science. D. Raymond, D. Wood, and S. YuSpringer Verlag231Intex: An fst toolbox. Theoretical Computer ScienceMax Silberztein. 2000. Intex: An fst toolbox. The- oretical Computer Science, 231(1):33-46, January. Gertjan van Noord. 1997. FSA utilities: A toolbox to manipulate finite state automata. In D. Raymond, D. Wood, and S. Yu, editors, Automata Implemen- tation, number 1260 in Lecture Notes in Computer Science. Springer Verlag. Vi-XFST: A visual interface for Xerox Finite State Toolkit. Yasin Yılmaz, Sabancı UniversityMaster's thesisYasin Yılmaz. 2003. Vi-XFST: A visual interface for Xerox Finite State Toolkit. Master's thesis, Sa- bancı University, July.

Exploiting Sentence Similarities for Better Alignments

We study the problem of jointly aligning sentence constituents and predicting their similarities. While extensive sentence similarity data exists, manually generating reference alignments and labeling the similarities of the aligned chunks is comparatively onerous. This prompts the natural question of whether we can exploit easy-to-create sentence level data to train better aligners. In this paper, we present a model that learns to jointly align constituents of two sentences and also predict their similarities. By taking advantage of both sentence and constituent level data, we show that our model achieves state-of-the-art performance at predicting alignments and constituent similarities.

Exploiting Sentence Similarities for Better Alignments Association for Computational LinguisticsCopyright Association for Computational LinguisticsNovember 1-5, 2016. 2016 Tao Li University of Utah Vivek Srikumar University of Utah Exploiting Sentence Similarities for Better Alignments Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing the 2016 Conference on Empirical Methods in Natural Language ProcessingAustin, TexasAssociation for Computational LinguisticsNovember 1-5, 2016. 2016 We study the problem of jointly aligning sentence constituents and predicting their similarities. While extensive sentence similarity data exists, manually generating reference alignments and labeling the similarities of the aligned chunks is comparatively onerous. This prompts the natural question of whether we can exploit easy-to-create sentence level data to train better aligners. In this paper, we present a model that learns to jointly align constituents of two sentences and also predict their similarities. By taking advantage of both sentence and constituent level data, we show that our model achieves state-of-the-art performance at predicting alignments and constituent similarities. Introduction The problem of discovering semantic relationships between two sentences has given birth to several NLP tasks over the years. Textual entailment (Dagan et al., 2013, inter alia) asks about the truth of a hypothesis sentence given another sentence (or more generally a paragraph). Paraphrase identification (Dolan et al., 2004, inter alia) asks whether two sentences have the same meaning. Foregoing the binary entailment and paraphrase decisions, the semantic textual similarity (STS) task (Agirre et al., 2012) asks for a numeric measure of semantic equivalence between two sentences. All three tasks have attracted much interest in the form of shared tasks. While various approaches have been proposed to predict these sentence relationships, a commonly employed strategy (Das and Smith, 2009;Chang et Gunmen abduct 7 foreign workers ∅ Seven foreign workers kidnapped E Q U I E Q U I Figure 1: Example constituent alignment. The solid lines represent aligned constituents (here, both labeled equivalent). The chunk Gunmen is unaligned. al., 2010a) is to postulate an alignment between constituents of the sentences and use this alignment to make the final prediction (a binary decision or a numeric similarity score). The implicit assumption in such approaches is that better constituent alignments can lead to better identification of semantic relationships between sentences. Constituent alignments serve two purposes. First, they act as an intermediate representation for predicting the final output. Second, the alignments help interpret (and debug) decisions made by the overall system. For example, the alignment between the sentences in Figure 1 can not only be useful to determine the equivalence of the two sentences, but also help reason about the predictions. The importance of this intermediate representation led to the creation of the interpretable semantic textual similarity task (Agirre et al., 2015a) that focuses on predicting chunk-level alignments and similarities. However, while extensive resources exist for sentence-level relationships, human annotated chunk-aligned data is comparatively smaller. In this paper, we address the following question: can we use sentence-level resources to better pre-dict constituent alignments and similarities? To answer this question, we focus on the semantic textual similarity (STS) task and its interpretable variant. We propose a joint model that aligns constituents and integrates the information across the aligned edges to predict both constituent and sentence level similarity. The key advantage of modeling these two problems jointly is that, during training, the sentence-level information can provide feedback to the constituent-level predictions. We evaluate our model on the SemEval-2016 task of interpretable STS. We show that even without the sentence information, our joint model that uses constituent alignments and similarities forms a strong baseline. Further, our easily extensible joint model can incorporate sentence-level similarity judgments to produce alignments and chunk similarities that are comparable to the best results in the shared task. In summary, the contributions of this paper are: 1. We present the first joint model for predicting constituent alignments and similarities. Our model can naturally take advantage of the much larger sentence-level annotations. 2. We evaluate our model on the SemEval-2016 task of interpretable semantic similarity and show state-of-the-art results. Problem Definition In this section, we will introduce the notation used in the paper using the sentences in Figure 1 as a running example. The input to the problem is a pair of sentences, denoted by x. We will assume that the sentences are chunked (Tjong Kim Sang and Buchholz, 2000) into constituents. We denote the chunks using subscripts. Thus, the input x consists of two sequences of chunks s = (s 1 , s 2 , · · · ) and t = (t 1 , t 2 , · · · ) respectively. In our running example, we have s = (Gunmen, abduct, seven foreign workers) and t = (Seven foreign workers, kidnapped). The output consists of three components: 1. Alignment: The alignment between a pair of chunks is a labeled, undirected edge that explains the relation that exists between them. The labels can be one of EQUI (semantically equivalent), OPPO (opposite meaning in context), SPE1, SPE2 (the chunk from s is more specific than the one from t and vice versa), SIMI (similar meaning, but none of the previous ones) or REL (related, but none of the above) 1 . In Figure 1, we see two EQUI edges. A chunk from either sentence can be unaligned, as in the case of the chunk Gunmen. We will use y to denote the alignment for an input x. The alignment y consists of a sequence of triples of the form (s i , t j , l). Here, s i and t j denote a pair of chunks that are aligned with a label l. For brevity, we will include unaligned chunks into this format using a special null chunk and label to indicate that a chunk is unaligned. Thus, the alignment for our running example contain the triple (Gunmen, ∅, ∅). 2. Chunk similarity: Every aligned chunk is associated with a relatedness score between zero and five, denoting the range from unrelated to equivalent. Note that even chunks labeled OPPO can be assigned a high score because the polarity is captured by the label rather than the score. We will denote the chunk similarities using z, comprising of numeric z i,j,l for elements of the corresponding alignment y. For an unaligned chunk, the corresponding similarity z is fixed to zero. 3. Sentence similarity: The pair of sentences is associated with a scalar score from zero to five, to be interpreted as above. We will use r to denote the sentence similarity for an input x. Thus, the prediction problem is the following: Given a pair of chunked sentences x = (s, t), predict the alignment y, the alignment similarities z and the sentence similarity r. Note that this problem definition integrates the canonical semantic textual similarity task (only predicting r) and its interpretable variant (predicting both y and z) into a single task. Predicting Alignments and Similarities This section describes our model for predicting alignments, alignment scores, and the sentence similarity scores for a given pair of sentences. We will assume that learning is complete and we have all the scoring functions we need and defer discussing the parameterization and learning to Section 4. We frame the problem of inference as an instance of an integer linear program (ILP). We will first see the scoring functions and the ILP formulation in Section 3.1. Then, in Section 3.2, we will see how we can directly read off the similarity scores at both chunk and sentence level from the alignment. Alignment via Integer Linear Programs We have two kinds of 0-1 inference variables to represent labeled aligned chunks and unaligned chunks. We will use the inference variables 1 i,j,l to denote the decision that chunks s i and t j are aligned with a label l. To allow chunks to be unaligned, the variables 1 i,0 and 1 0,j denote the decisions that s i and t j are unaligned respectively. Every inference decision is scored by the trained model. Thus, we have score(i, j, l), score(i, 0) and score(0, j) for the three kinds of inference variables respectively. All scores are of the form A w T Φ (·, s, t) , where w is a weight vector that is learned, Φ (·, s, t) is a feature function whose arguments include the constituents and labels in question, and A is a sigmoidal activation function that flattens the scores to the range [0,5]. In all our experiments, we used the function A(x) = 5 1+e −x . The goal of inference is to find the assignment to the inference variables that maximizes total score. That is, we seek to solve arg max 1∈C i,j,l score(i, j, l)1 i,j,l + i score(i, 0)1 i,0 + j score(0, j)1 0,j(1) Here 1 represents all the inference variables together and C denotes the set of all valid assignments to the variables, defined by the following set of constraints: 1. A pair of chunks can have at most one label. 2. Either a chunk can be unaligned or it should participate in a labeled alignment with exactly one chunk of the other sentence. We can convert these constraints into linear inequalities over the inference variables using standard techniques for ILP inference (Roth and Yih, 2004) 2 . Note that, by construction, there is a oneto-one mapping from an assignment to the inference variables 1 and the alignment y. In the rest of the paper, we use these two symbols interchangeably, using 1 referring details of inference and y referring to the alignment as a sequence of labeled edges. From Alignments to Similarities To complete the prediction, we need to compute the numeric chunk and sentence similarities given the alignment y. In each case, we make modeling assumptions about how the alignments and similarities are related, as described below. Chunk similarities To predict the chunk similarities, we assume that the label-specific chunk similarities of aligned chunks are the best edge-weights for the corresponding inference variables. That is, for a pair of chunks (s i , t j ) that are aligned with a label l, the chunk pair similarity z i,j,l is the coefficient associated with the corresponding inference variable. If the alignment edge indicates an unaligned chunk, then the corresponding score is zero. That is, z i,j,l = A w T Φ (s i , t j , l, s, t) if l = ∅ 0 if l = ∅.(2) But can chunk similarities directly be used to find good alignments? To validate this assumption, we performed a pilot experiment on the chunk aligned part of our training dataset. We used the gold standard chunk similarities as scores of the inference variables in the integer program in Eq. 1, with the variables associated with unaligned chunks being scored zero. We found that this experiment gives a near-perfect typed alignment F-score of 0.9875. The slight disparity is because the inference only allows 1-to-1 matches between chunks (constraint 2), which does not hold in a small number of examples. Sentence similarities Given the aligned chunks y, the similarity between the sentences s and t (i.e., in our notation, r) is the weighted average of the chunk similarities (i.e., z i,j,l ). Formally, r = 1 |y| (s i ,t j ,l)∈y α l z i,j,l .(3) Note that the weights α l depend only on the labels associated with the alignment edge and are designed to capture the polarity and strength of the label. Eq. 3 bridges sentence similarities and chunk similarities. During learning, this provides the feedback from sentence similarities to chunk similarities. The values of the α's can be learned or fixed before learning commences. To simplify our model, we choose the latter approach . Section 5 gives more details. Features To complete the description of the model, we now describe the features that define the scoring functions. We use standard features from the STS literature (Karumuri et al., 2015;Agirre et al., 2015b;Banjade et al., 2015). For a pair of chunks, we extract the following similarity features: (1) Absolute cosine similarities of GloVe embeddings (Pennington et al., 2014) of head words, (2) WordNet based Resnik (Resnik, 1995), Leacock (Leacock and Chodorow, 1998) and Lin (Lin, 1998) similarities of head words, (3) Jaccard similarity of content words and lemmas. In addition, we also add indicators for: (1) the part of speech tags of the pair of head words, (2) the pair of head words being present in the lexical large section of the Paraphrase Database (Ganitkevitch et al., 2013), (3) a chunk being longer than the other while both are not named entity chunks, (4) a chunk having more content words than the other, (5) contents of one chunk being a part of the other, (6) having the same named entity type or numeric words, (7) sharing synonyms or antonyms, (8) sharing conjunctions or prepositions, (9) the existence of unigram/bigram/trigram overlap, (10) if only one chunk has a negation, and (11) a chunk having extra content words that are also present in the other sentence. For a chunk being unaligned, we conjoin an indicator that the chunk is unaligned with the part of speech tag of its head word. Discussion In the model proposed above, by predicting the alignment, we will be able to deterministically calculate both chunk and sentence level similarities. This is in contrast to other approaches for the STS task, which first align constituents and then extract features from alignments to predict similarities in a pipelined fashion. The joint prediction of alignment and similarities allows us to address the primary motivation of the paper, namely using the abundant sentence level data to train the aligner and scorer. The crucial assumption that drives the joint model is that the same set of parameters that can discover a good alignment can also predict similarities. This assumption -similar to the one made by Chang et al. (2010b) -and the associated model described above, imply that the goal of learning is to find parameters that drive the inference towards good alignments and similarities. Learning the Alignment Model Under the proposed model, the alignment directly predicts the chunk and sentence similarities as well. We utilize two datasets to learn the model: 1. The alignment dataset D A consists of fully annotated aligned chunks and respective chunk similarity scores. 2. The sentence dataset D S that consists of pairs of sentences where each pair is labeled with a numeric similarity score between zero and five. The goal of learning is to use these two datasets to train the model parameters. Note that unlike standard multi-task learning problems, the two tasks in our case are tightly coupled both in terms of their definition and via the model described in Section 3. We define three types of loss functions corresponding to the three components of the final output (i.e., alignment, chunk similarity and sentence similarity). Naturally, for each kind of loss, we assume that we have the corresponding ground truth. We will denote ground truth similarity scores and alignments using asterisks. Also, the loss functions defined below depend on the weight vector w, but this is not shown to simplify notation. 1. The alignment loss L a is a structured loss function that penalizes alignments that are far away from the ground truth. We used the structured hinge loss Tsochantaridis et al., 2005) for this purpose. L a (s, t, y * ) = max y w T Φ (s, t, y) +∆ (y, y * ) − w T Φ (s, t, y * ) . Here, ∆ refers to the Hamming distance between the alignments. 2. The chunk score loss L c is designed to penalize errors in predicted chunk level similarities. To account for cases where chunk boundaries may be incorrect, we define this loss as the sum of squared errors of token similarities. However, neither our output nor the gold standard similarities are at the granularity of tokens. Thus, to compute the loss, we project the chunk scores z i,j,l for an aligned chunk pair (s i , t j , l) to the tokens that constitute the chunks by equally partitioning the scores among all possible internal alignments. In other words, for a token w i in the chunk s i and token w j in chunk s j , we define token similarity scores as z(w i , w j , l) = z i,j,l N (s i ,t j ) Here, the normalizing function N is the product of the number of tokens in the chunks 3 . Note that this definition of the token similarity scores applies to both predicted and gold standard similarities. Unaligned tokens are associated with a zero score. We can now define the loss for a token pair (w i , w j ) ∈ (s, t) and a label l as the squared error of their token similarity scores: l(w i , w j , l) = (z(w i , w j , l) − z * (w i , w j , l)) 2 3 Following the official evaluation of the interpretable STS task, we also experimented with the max(|si|, |tj|) for the normalizer, but we found via cross validation that the product performs better. The chunk loss score L c for a sentence pair is the sum of all the losses over all pairs of tokens and labels. L c (s, t, y, y * , z, z * ) = w i ,w j ,l l(w i , w j , l) 3. The sentence similarity loss L s provides feedback to the aligner by penalizing alignments that are far away from the ground truth in their similarity assessments. For a pair of sentences (s, t), given the ground truth sentence similarity r * and the predicted sentence similarity r (using Equation (3)), the sentence similarity loss is the squared error: L s (s, t, r * ) = (r − r * ) 2 . Our learning objective is the weighted combination of the above three components and a 2 regularizer on the weight vector. The importance of each type of loss is controlled by a corresponding hyperparameter: λ a , λ c and λ s respectively. Learning algorithm We have two scenarios to consider: with only alignment dataset D A , and with both D A and sentence dataset D S . Note that even if we train only on the alignment dataset D A , our learning objective is not convex because the activation function is sigmoidal (in Section 3.1). In both cases, we use stochastic gradient descent with minibatch updates as the optimizer. In the first scenario, we simply perform the optimization using the alignment and the chunk score losses. We found by preliminary experiments on training data that initializing the weights to one performed best. Algorithm 1 Learning alignments and similarities, given alignment dataset D A and sentence similarity dataset D S . See the text for more details. When we have both D A and D S (Algorithm 1), we first initialize the model on the alignment data only. Using this initial model, we hypothesize alignments on all examples in D S to get fully labeled examples. Then, we optimize the full objective (all three loss terms) on the combined dataset. Because our goal is to study the impact on the chunk level predictions, in the full model, the sentence loss does not play a part on examples from D A . Experiments and Results The primary research question we seek to answer via experiments is: Can we better predict chunk alignments and similarities by taking advantage of sentence level similarity data? Datasets We used the training and test data from the 2016 SemEval shared tasks of predicting semantic textual similarity (Agirre et al., 2016a) and interpretable STS (Agirre et al., 2016b), that is, tasks 1 and 2 respectively. For our experiments, we used the headlines and images sections of the data. The data for the interpretable STS task, consisting of manually aligned and scored chunks, provides the alignment datasets for training (D A ). The headlines section of the training data consists for 756 sentence pairs, while the images section consists for 750 sentence pairs. The data for the STS task acts as our sentence level training dataset (D S ). For the headlines section, we used the 2013 headlines test set consisting of 750 sentence pairs with gold sentence similarity scores. For the images section, we used the 2014 images test set consisting of 750 examples. We evaluated our models on the official Task 2 test set, consisting of 375 sentence pairs for both the headlines and images sections. In all experiments, we used gold standard chunk boundaries if they are available (i.e., for D A ). Pre-processing We pre-processed the sentences with parts of speech using the Stanford CoreNLP toolkit . Since our setting assumes that we have the chunks as input, we used the Illinois shallow parser (Clarke et al., 2012) to extract chunks from D S . We post-processed the predicted chunks to correct for errors using the following steps: 1. Split on punctuation; 2. Split on verbs in NP; 3. Split on nouns in VP; 4. Merge PP+NP into PP; 5. Merge VP+PRT into VP if the PRT chunk is not a preposition or a subordinating conjunction; 6. Merge SBAR+NP into SBAR; and 7. Create new contiguous chunks using tokens that are marked as being outside a chunk by the shallow parser. We found that using the above postprocessing rules, improved the F1 of chunk accuracy from 0.7865 to 0.8130. We also found via crossvalidation that this post-processing improved overall alignment accuracy. The reader may refer to other STS resources (Karumuri et al., 2015) for further improvements along this direction. Experimental setup We performed stochastic gradient descent for 200 epochs in our experiments, with a mini-batch size of 20. We determined the three λ's using cross-validation, with different hyperparameters for examples from D A and D S . Table 1 lists the best hyperparameter values. For performing inference, we used the Gurobi optimizer 4 . Setting λ a , λ c , λ s headlines, D A 100, 0.01, N/A headlines, D S 0.5, 1, 50 images, D A 100, 0.01, N/A images, D S 5, 2.5, 50 As noted in Section 3.1, the parameter α l combines chunk scores into sentence scores. To find these hyper-parameters, we used a set of 426 sentences from the from the headlines training data that had both sentence and chunk annotation. We simplified the search by assuming that α Equi is always 1.0 and all labels other than OPPO have the same α. Using grid search over [−1, 1] in increments of 0.1, we selected α's that gave us the highest Pearson correlation for sentence level similarities. The best α's (with a Pearson correlation of 0.7635) were: α l =      1, l = EQUI, −1, l = OPPO, 0.7, otherwise Results Following the official evaluation for the SemEval task, we evaluate both alignments and their Table 2: F-score for headlines and images datasets. These tables show the result of our systems, baseline and top-ranked systems. D A is our strong baseline trained on interpretable STS dataset; D A + D S is trained on interpretable STS as well as STS dataset. The rank 1 system on headlines is Inspire (Kazmi and Schüller, 2016) and UWB (Konopik et al., 2016) on images. Bold are the best scores. corresponding similarity scores. The typed alignment evaluation (denoted by typed ali in the results table) measures F1 over the alignment edges where the types need to match, but scores are ignored. The typed similarity evaluation (denoted by typed score) is the more stringent evaluation that measures F1 of the alignment edge labels, but penalizes them if the similarity scores do not match. The untyped versions of alignment and scored alignment evaluations ignore alignment labels. These metrics, based on Melamed (1997), are tailored for the interpretable STS task 5 . We refer the reader to the guidelines of the task for further details. We report both scores in Table 2. We also list the performance of the baseline system (Sultan et al., 2014a) and the top ranked systems from the 2016 shared task for each dataset 6 . By comparing the rows labeled D A and D A + D S in Table 2 (a) and Table 2 (b), we see that in both the headlines and the images datasets, adding sentence level information improves the untyped score, lifting the stricter typed score F1. On the headlines dataset, incorporating sentence-level information degrades both the untyped and typed alignment quality because we cross-validated on the typed score metric. The typed score metric is the combination of untyped alignment, untyped score and typed alignment. From the row D A + D S in Table 2(a), we observe that the typed score F1 is slightly behind that of rank 1 system while all other three metrics are significantly better, indicating that we need to improve our modeling of the intersection of the three aspects. However, this does not apply to images dataset where the improvement on the typed score F1 comes from the typed alignment. Further, we see that even our base model that only depends on the alignment data offers strong alignment F1 scores. This validates the utility of jointly modeling alignments and chunk similarities. Adding sentence data to this already strong system leads to performance that is comparable to or better than the state-of-the-art systems. Indeed, our final results would have been ranked first on the images task and a close second on the headlines task in the official standings. The most significant feedback coming from sentence-level information is with respect to the chunk similarity scores. While we observed slight change in the unscored alignment performance, for both the headlines and the images datasets, we saw improvements in both scored precision and recall when sentence level data was used. Analysis and Discussion In this section, first, we report the results of manual error analysis. Then, we study the ability of our model to handle data from different domains. Error Analysis To perform a manual error analysis, we selected 40 examples from the development set of the headlines section. We classified the errors made by the full model trained on the alignment and sentence datasets. Below, we report the four most significant types of errors: 1. Contextual implication: Chunks that are meant to be aligned are not synonyms by them-selves but are implied by the context. For instance, Israeli forces and security forces might be equivalent in certain contexts. Out of the 16 instances of EQUI being misclassified as SPE, eight were caused by the features' inability to ascertain contextual implications. This also accounted for four out of the 15 failures to identify alignments. 2. Semantic phrase understanding: These are the cases where our lexical resources failed, e. g., ablaze and left burning. This accounted for ten of the 15 chunk alignment failures and nine of the 21 labeling errors. Among these, some errors (four alignment failures and four labeling errors) were much simpler than others that could be handled with relatively simple features (e.g. family reunions ↔ family unions). Preposition semantics: The inability to account for preposition semantics accounts for three of the 16 cases where EQUI is mistaken as a SPE. Some examples include at 91 ↔ aged 91 and catch fire ↔ after fire. 4. Underestimated EQUI score: Ten out of 14 cases of score underestimation happened on EQUI label. Our analysis suggests that we need better contextual features and phrasal features to make further gains in aligning constituents. Does the text domain matter? In all the experiments in Section 5, we used sentence datasets belonging to the same domain as the alignment dataset (either headlines or images). Given that our model can take advantage of two separate datasets, a natural question to ask is how the domain of the sentence dataset influences overall alignment performance. Additionally, we can also ask how well the trained classifiers perform on out-ofdomain data. We performed a series of experiments to explore these two questions. Table 3 summarizes the results of these experiments. The columns labeled Train and Test of the table show the training and test sets used. Each dataset can be either the headlines section (denoted by hdln), or the images section (img) or not used (∅). The last two columns report performance on the test set. The rows 1 and 5 in the table correspond to the in-domain settings and match the results of typed alignment and score in Table 2 When the headlines data is tested on the images section, we see that there is the usual domain adaptation problem (row 3 vs row 1) and using target images sentence data does not help (row 4 vs row 3). In contrast, even though there is a domain adaptation problem when we compare the rows 5 and 7, we see that once again, using headlines sentence data improves the predicted scores (row 7 vs row 8). This observation can be explained by the fact that the images sentences are relatively simpler and headlines dataset can provide richer features in comparison, thus allowing for stronger feedback from sentences to constituents. The next question concerns how the domain of the sentence dataset D S influences alignment and similarity performance. To answer this, we can compare the results in every pair of rows (i.e., 1 vs 2, 3 vs 4, etc.) We see that when the sentence data from the image data is used in conjunction to the headlines chunk data, it invariably makes the classifiers worse. In contrast, the opposite trend is observed when the headlines sentence data augments the images chunk data. This can once again be explained by relatively simpler sentence constructions in the images set, suggesting that we can leverage linguistically complex corpora to improve alignment on simpler ones. Indeed, surprisingly, we obtain marginally better performance on the images set when we use images chunk level data in conjunction with the headlines sentence data (row 6 vs the row labeled D A + D S in the Table 2(b)). Related Work Aligning words and phrases between pairs of sentences is widely studied in NLP. Machine translation has a rich research history of using alignments (for e.g., (Koehn et al., 2003;Och and Ney, 2003)), going back to the IBM models (Brown et al., 1993). From the learning perspective, the alignments are often treated as latent variables during learning, as in this work where we treated alignments in the sentence level training examples as latent variables. Our work is also conceptually related to (Ganchev et al., 2008), which asked whether improved alignment error implied better translation. Outside of machine translation, alignments are employed either explicitly or implicitly for recognizing textual entailment (Brockett, 2007;Chang et al., 2010a) and paraphrase recognition (Das and Smith, 2009;Chang et al., 2010a). Additionally, alignments are explored in multiple ways (tokens, phrases, parse trees and dependency graphs) as a foundation for natural logic inference (Chambers et al., 2007;MacCartney and Manning, 2007;Mac-Cartney et al., 2008). Our proposed aligner can be used to aid such applications. For predicting sentence similarities, in both variants of the task, word or chunk alignments have extensively been used (Sultan et al., 2015;Sultan et al., 2014a;Sultan et al., 2014b;Hänig et al., 2015;Karumuri et al., 2015;Agirre et al., 2015b;Banjade et al., 2015, and others). In contrast to these systems, we proposed a model that is trained jointly to predict alignments, chunk similarities and sentence similarities. To our knowledge, this is the first approach that combines sentence-level similarity data with fine grained alignments to train a chunk aligner. Conclusion In this paper, we presented the first joint framework for aligning sentence constituents and predicting constituent and sentence similarities. We showed that our predictive model can be trained using both aligned constituent data and sentence similarity data. Our jointly trained model achieves stateof-the-art performance on the task of predicting in-terpretable sentence similarities. 1 : 1Initialize all weights to one. 2: w 0 ← SGD(D A ): Train an initial model 3: Use w 0 to predict alignments on examples in D S . Call this D S . 4: w ← SGD(D A ∪ D S ): Train on both sets of examples. 5: return w Table 1 : 1Hyperparameters for the various settings, chosen by cross-validation. The alignment dataset do not have a λ associated with the sentence loss. .Id Train Test Typed F1 D A D S ali score 1. hdln ∅ hdln 0.7350 0.6776 2. img 0.6826 0.6347 3. ∅ img 0.6547 0.5989 4. img 0.6161 0.5854 5. img ∅ img 0.6933 0.6411 6. hdln 0.7033 0.6793 7. ∅ hdln 0.6702 0.6274 8. hdln 0.6672 0.6445 Table 3 : 3F-score for the domain adaptation experiments. This table shows the performance of training on different dataset combinations. We refer the reader to the guidelines of the task(Agirre et al., 2015a) for further details on these labels. Also, for simplicity, in this paper, we ignore the factuality and polarity tags from the interpretable task. While it may be possible to find the score maximizing alignment in the presence of these constraints using dynamic programming (say, a variant of the Kuhn-Munkres algorithm), we model inference as an ILP to allow us the flexibility to explore more sophisticated output interactions in the future. http://www.gurobi.com/ In the SemEval 2016 shared task, the typed score is the metric used for system ranking. 6 http://alt.qcri.org/semeval2016/task2/ AcknowledgmentsThe authors wish to thank the anonymous reviewers and the members of the Utah NLP group for their valuable comments and pointers to references. SemEval-2012 task 6: A pilot on semantic textual similarity. Agirre, *SEM 2012: The First Joint Conference on Lexical and Computational Semantics. Agirre et al.2012] Eneko Agirre, Mona Diab, Daniel Cer, and Aitor Gonzalez-Agirre. 2012. 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Journal of Machine Learning Research, Volume 6.

Identifying Cross-Cultural Differences in Word Usage

Personal writings have inspired researchers in the fields of linguistics and psychology to study the relationship between language and culture to better understand the psychology of people across different cultures. In this paper, we explore this relation by developing cross-cultural word models to identify words with cultural bias -i.e., words that are used in significantly different ways by speakers from different cultures. Focusing specifically on two cultures: United States and Australia, we identify a set of words with significant usage differences, and further investigate these words through feature analysis and topic modeling, shedding light on the attributes of language that contribute to these differences.IntroductionAccording toShweder et al. (1998), "to be a member of a group is to think and act in a certain way, in the light of particular goals, values, pictures of the world; and to think and act so is to belong to a group."Culture can be defined as any characteristic of a group of people, which can affect and shape their beliefs and behaviors (e.g., nationality, region, state, gender, or religion). It reflects itself in people's everyday thoughts, beliefs, ideas, and actions, and understanding what people say or write in their daily lives can help us understand and differentiate cultures. In this work, we use very large corpora of personal writings in the form of blogs from multiple cultures 1 to understand cultural differences in word usage.We find inspiration in a line of research in psychology that poses that people from different cultural backgrounds and/or speaking different languages perceive the world around them differently, which is reflected in their perception of time and space(Kern, 2003;Boroditsky, 2001), body shapes(Furnham and Alibhai, 1983), or surrounding objects(Boroditsky et al., 2003). As an example, consider the study described byBoroditsky et al. (2003), which showed how the perception of objects in different languages can be affected by their gender differences. For instance, one of the words used in their study is the word "bridge," which is masculine in Spanish and feminine in German: when asked about the descriptive properties of a bridge, Spanish speakers described bridges as being big, dangerous, long, strong, sturdy, and towering, while German speakers said they are beautiful, elegant, fragile, peaceful, pretty, and slender.While this previous research has the benefit of careful in-lab studies that explore differences in world view for one dimension (e.g., time, space) or word (e.g., bridge, sun) at a time, it also has limitations in terms of the number of experiments that can be run when subjects are being brought to the lab for every new question being asked. We aim to address this shortcoming by using the power of large-scale computational linguistics, which allows us to identify cultural differences in word usage in a data-driven bottom-up fashion.We hypothesize that we can use computational models to identify differences in word usage between cultures, regarded as an approximation of their differences in world view. Rather than starting with predetermined hypotheses (e.g., that Spanish and German speakers would have a different way of talking about bridges), we can use computational linguistics to run experiments on hundreds of words, and This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http: //creativecommons.org/licenses/by/4.0/ 1 Throughout this paper, we use the term culture to represent the nationality (country) of a group of people.

Identifying Cross-Cultural Differences in Word Usage December 11-17 2016 Aparna Garimella University of Michigan University of Texas at Austin Rada Mihalcea mihalcea@umich.edu University of Michigan University of Texas at Austin James Pennebaker pennebaker@utexas.edu University of Michigan University of Texas at Austin Identifying Cross-Cultural Differences in Word Usage Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers COLING 2016, the 26th International Conference on Computational Linguistics: Technical PapersOsaka, JapanDecember 11-17 2016 Personal writings have inspired researchers in the fields of linguistics and psychology to study the relationship between language and culture to better understand the psychology of people across different cultures. In this paper, we explore this relation by developing cross-cultural word models to identify words with cultural bias -i.e., words that are used in significantly different ways by speakers from different cultures. Focusing specifically on two cultures: United States and Australia, we identify a set of words with significant usage differences, and further investigate these words through feature analysis and topic modeling, shedding light on the attributes of language that contribute to these differences.IntroductionAccording toShweder et al. (1998), "to be a member of a group is to think and act in a certain way, in the light of particular goals, values, pictures of the world; and to think and act so is to belong to a group."Culture can be defined as any characteristic of a group of people, which can affect and shape their beliefs and behaviors (e.g., nationality, region, state, gender, or religion). It reflects itself in people's everyday thoughts, beliefs, ideas, and actions, and understanding what people say or write in their daily lives can help us understand and differentiate cultures. In this work, we use very large corpora of personal writings in the form of blogs from multiple cultures 1 to understand cultural differences in word usage.We find inspiration in a line of research in psychology that poses that people from different cultural backgrounds and/or speaking different languages perceive the world around them differently, which is reflected in their perception of time and space(Kern, 2003;Boroditsky, 2001), body shapes(Furnham and Alibhai, 1983), or surrounding objects(Boroditsky et al., 2003). As an example, consider the study described byBoroditsky et al. (2003), which showed how the perception of objects in different languages can be affected by their gender differences. For instance, one of the words used in their study is the word "bridge," which is masculine in Spanish and feminine in German: when asked about the descriptive properties of a bridge, Spanish speakers described bridges as being big, dangerous, long, strong, sturdy, and towering, while German speakers said they are beautiful, elegant, fragile, peaceful, pretty, and slender.While this previous research has the benefit of careful in-lab studies that explore differences in world view for one dimension (e.g., time, space) or word (e.g., bridge, sun) at a time, it also has limitations in terms of the number of experiments that can be run when subjects are being brought to the lab for every new question being asked. We aim to address this shortcoming by using the power of large-scale computational linguistics, which allows us to identify cultural differences in word usage in a data-driven bottom-up fashion.We hypothesize that we can use computational models to identify differences in word usage between cultures, regarded as an approximation of their differences in world view. Rather than starting with predetermined hypotheses (e.g., that Spanish and German speakers would have a different way of talking about bridges), we can use computational linguistics to run experiments on hundreds of words, and This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http: //creativecommons.org/licenses/by/4.0/ 1 Throughout this paper, we use the term culture to represent the nationality (country) of a group of people. consequently identify those words where usage differences exist between two cultures. We explore this hypothesis by seeking answers to two main research questions. First, given a word W , are there significant differences in how this word is being used by two selected cultures? We build cross-cultural word models in which we use classifiers based on several classes of linguistic features and attempt to differentiate between usages of the given word W in different cultures. By applying them to a large number of words, these models are used to identify those words for which there exist significant usage differences between the two cultures of interest. Second, if such significant differences in the usage of a word are identified, can we use feature analysis to understand the nature of these differences? We perform several analyses: (1) Feature ablation that highlights the linguistic features contributing to these differences; (2) Topic modeling applied to the words with significant differences, used to identify the dominant topic for each culture and to measure the correlation between the topic distributions in the two cultures; and (3) One-versus-all cross-cultural classification models, where we attempt to isolate the idiosyncrasies in word usage for one culture at a time. Data We base our work on personal writings collected from blogs, and specifically target word usage differences between Australia and United States. These two countries are selected for two main reasons: (1) they both use English as a native language, and therefore we can avoid the noise that would otherwise be introduced by machine translation; and (2) they have a significant number of blogs contributed in recent years, which we can use to collect a large number of occurrences for a large set of words. We obtain a large corpus of blog posts by crawling the blogger profiles and posts from Google Blogger. For each profile, we consider up to a maximum of 20 blogs, and for each blog, we consider up to 500 posts. Table 1 gives statistics of the data collected in this process. We process the blog posts by removing the HTML tags and tagging them with part-of-speech labels (Toutanova et al., 2003). Country Profiles Blogs Posts Australia 469 1129 320316 United States 374 1267 471257 Table 1: Blog statistics for the two target cultures. Next, we create our pool of candidate target words by identifying the top 1, 500 content words based on their frequency in the blog posts, additionally placing a constraint that they cover all open-class parts-ofspeech: of the 1, 500 words, 500 are nouns, 500 verbs, 250 adjectives, and 250 adverbs. These numbers are chosen based on the number of examples that exist for the target words; e.g., most (> 490) of the 500 selected nouns have more than 300 examples; etc. We consider all possible inflections for these words, for instance for the verb write we also consider the forms writes, wrote, written, writing. The possible inflections for the target words are added manually, to ensure correct handling of grammatical exceptions. To obtain usage examples for the two cultures for these words, we extract paragraphs from the blog posts that include the selected words with the given part-of-speech. Of these paragraphs, we discard those that contain less than ten words. We also truncate the long paragraphs so they include a maximum of 100 words to the left and right of the target word, disregarding sentence boundaries. The contexts of the target words are then parsed to get the dependency tags related to the target word . We also explicitly balance the data across time. Noting there could be cases where the number of blog posts published in a specific year is higher compared to that in other years due to certain events (e.g., an Olympiad, or a major weather related event), we draw samples for our dataset from several different time periods. Specifically, for each culture, we consider an equal number of instances from four different years (2011)(2012)(2013)(2014). Table 2 shows the per-word average number of data instances obtained in this way for each part-of-speech for each culture for the years 2011-2014. Note that we do not attempt to balance the data across topics (domains), as we regard potentially different topic distributions as a reflection of the culture (e.g., Australia may be naturally more interested in water sports than United States is). We do instead explicitly balance our data over time, as described above, to avoid temporal topic peaks related to certain events. Finding Words with Cultural Bias We start by addressing the first research question: given a word W , are there significant differences in how this word is being used by two different cultures? We formulate a classification task where the goal is to identify, for the given target word W , the culture of the writer of a certain occurrence of that word. If the accuracy of such a classifier exceeds that of a random baseline, this can be taken as an indication of word usage differences between the two cultures. We run classification experiments on each of the 1, 500 words described in the previous section, and consequently aim to identify those words with significant usage differences between Australia and United States. Features We implement and extract four types of features: Local features. These features consist of the target word itself, its part-of-speech, three words and their parts-of-speech to the left and right of the target concept, nouns and verbs before and after the target concept. These features are used to capture the immediately surrounding language (e.g., descriptors, verbs) used by the writers while describing their views about the target word. Contextual features. These features are determined from the global context, and represent the most frequently occurring open-class words in the contexts of the word W in each culture. We allow for at most ten such features for each culture, and impose a threshold of a minimum of five occurrences for a word to be selected as a contextual feature. Contextual features express the overall intention of the blogger while writing about the target word. Socio-linguistic features. These features include (1) fractions of words that fall under each of the 70 Linguistic Inquiry and Word Count (LIWC) categories (Pennebaker et al., 2001); the 2001 version of LIWC includes about 2,200 words and word stems grouped into broad categories relevant to psychological processes (e.g., emotion, cognition); (2) fractions of words belonging to each of the five fine-grained polarity classes in OpinionFinder (Wilson et al., 2005), namely strongly negative, weakly negative, neutral, weakly positive, and strongly positive; (3) fractions of words belonging to each of five Morality classes (Ignatow and Mihalcea, 2012), i.e., authority, care, fairness, ingroup, sanctity; and (4) fractions of words belonging to each of the six Wordnet Affect classes (Strapparava et al., 2004), namely anger, disgust, fear, joy, sadness, and surprise. These features provide social and psychological insights into the perceptions bloggers have about the words they use. Syntactic features. These features consist of parser dependencies (De Marneffe et al., 2006) obtained from the Stanford dependency parser for the context of the target word. Among these, we select different dependencies for each part-of-speech: (1) nouns: root word of context (root), governor 2 if noun is nominal subject (nsubj), governor verb if noun is direct object (dobj), adjectival modifier (amod); (2) verbs: root, nominal subject (nubj), direct object (dobj), adjectival complement (acomp), adverb modifier (advmod); (3) adjectives: root, noun being modified (amod), verb being complemented (acomp), adverb modifier (advmod); (4) adverbs: root, adverb modifier (advmod). These features capture syntactic dependencies of the target word that are not always obtained using just its context. Cross-cultural Word Models The features described above are integrated into an AdaBoost classifier. 3 This classifier was selected based on its performance on a development dataset, when compared to other learning algorithms. We compare the performance of the classifier with a random choice baseline, which is always 50%, given the equal distribution of data between the two cultures. This allows us to identify the words for which we can automatically identify the culture of the writers of those words, which is taken as an indication of word usage differences between the two cultures. Throughout the paper, all the results reported are obtained using ten-fold cross-validation on the word data. When creating the folds, we explicitly ensure that posts authored by the same blogger are not shared between the folds, which in turn ensures no overlap between bloggers in training and test sets. This is important as repeating bloggers in both the train and the test splits could potentially overfit the model to the writing styles of individual bloggers rather than learning the underlying culture-based differences between the bloggers. To summarize the cross-validation process: First, for each of the 1,500 target words, we collect an equal number of instances containing the given target word or its inflections from Australia and United States, from each of the selected years (2011)(2012)(2013)(2014). We then divide the posts belonging to Australia and United States each into ten approximately equal groups, such that no two groups have bloggers in common. We finally combine the corresponding groups to form a total of ten bi-cultural groups that are approximately of equal size, which form our cross-validation splits. 4 To compute the statistical significance of the results obtained, we perform a two-sample t-test over the correctness of predictions of the two systems namely, Adaboost and random chance classifiers. Disambiguation results that are significantly better (p < 0.05) than the random chance baseline of 50% are marked with * . On average, the classifier leads to an accuracy of 58.36%*, which represents an absolute significant improvement of 8.36% over the baseline (a random chance of 50%). Table 3 shows the average classification results for each part-of-speech, as well as the number of words for which the AdaBoost classifier leads to an accuracy significantly larger than the baseline. These results suggest that there are indeed differences in the ways in which writers from Australia and United States use the target words with respect to all the parts-of-speech. Table 3: Average ten-fold cross-validation accuracies and number of words with an accuracy significantly higher than the baseline, for each part-of-speech, for United States vs. Australia. Where is the Difference? We now turn to our second research question: Once significant differences in the usage of a word are identified, can we use feature analysis to understand the nature of these differences? Feature Ablation We first study the role of the different linguistic features when separating between word usages in Australia and United States through ablation studies. For each of the feature sets specified in Section 3, we retrain our concept models using just that feature set type, which helps us locate the features that contribute the most to the observed cultural differences. The left side of Table 4 shows the ablation results averaged over the 1,500 target words for the four sets of features. We observe that the contextual and socio-linguistic features perform consistently well across all the parts-of-speech, and they alone can obtain an accuracy close to the all-feature performance. We also perform a feature ablation experiment to explore the role played by the various socio-linguistic features. The right side of Table 4 shows the classification accuracy obtained by using one sociolinguistic lexicon at a time: LIWC, OpinionFinder, Morality, and WordNet Affect. Among all these resources, LIWC and OpinionFinder appear to contribute the most to the classifier; while the morality lexicon and WordNet Affect also lead to an accuracy higher than the baseline, their performance is clearly smaller. Topic Modeling We next focus our analysis on the top 100 words (25 words for each part-of-speech) that have the most significant improvements over the random chance baseline, considered to be words with cultural bias in their use. The average accuracy of the classifier obtained on this set of words is 65.45%; the accuracy for each part-of-speech is shown in the second column of Table 7. We model the different usages of the words in our set of 100 words by using topic modeling. Specifically, we use Latent Dirichlet Allocation (LDA) 5 (Blei et al., 2003) to find a set of topics for each word, and consequently identify the topics specific to either Australia or United States. As typically done in topic modeling, we preprocess the data by removing a standard list of stop words, words with very high frequency (> 0.25%×datasize), and words that occur only once. To determine the number of topics that best describe the corpus for each of the 100 words, we use the average corpus likelihood over ten runs (Heinrich, 2005). Specifically, we choose that number of topics (>= 2, <= 10) for which the corpus likelihood is maximum. For each data instance, we say that a topic dominates the other topics if its probability is higher than that of the remaining topics. For a given word, we then identify the dominating topic for each culture as the topic that dominates the other topics in a majority of data instances. We use this definition of dominating topic in all the analyses done in this section. Quantitative Evaluation. To get an overall measure of how different cultures use the words that were found to have significant differences, we compute the Spearman's rank correlation between the topic distributions for the two cultures. For each topic, we get the number of data instances in which it dominates the other topics, in both cultures (Australia and United States). Subsequently, we measure the overall Spearman correlation coefficient between the dominating topic distributions for all 100 words. In other words, the distribution of topics is compared across cultures for each word. The Spearman coefficient is calculated as 0.63, which reflects a medium correlation between the usages of the words by the two cultures. Qualitative Evaluation. For a qualitative evaluation, Table 5 shows five sample words for each partof-speech, along with the identified number of topics and the dominating topic for Australia and United States. We associate labels to the hidden topics manually after looking at the corresponding top words falling under each of them. As seen in this table, the number of topics that best describe each word can vary widely between two topics for words such as start and economic, up to ten topics (which is the maximum allowed number of topics) for words such as color or support. The dominating topics illustrate the biases that exist in each culture for these words; for instance, the word teach is dominantly used to describe academic teaching in Australia, whereas in United States it is majorly used to talk about general life teaching. Several additional examples of differences are shown in Table 5. One-versus-all Classification For additional insight into word usage differences between United States and Australia, we expand our study to develop word models to separate word usages in Australia (or United States) from a mix of ten different cultures. In other words, we conduct a one-versus-all classification using the same process as described in Section 3, but using Australia (United States) against a mix of other cultures to examine any features specific to Australia (United States). In order to do this, we collect data from nine additional English speaking countries, as shown in the left side of Table 6. As before, the data for each country is balanced over time, and it includes an equal number of instances for four different years (2011)(2012)(2013)(2014). The right side of Table 6 shows the average number of instances per word collected for each part-of-speech. In this classification, for a given target word, one half of the data is collected from Australia (United States), and the other half is collected from the remaining countries, drawing 10% from each country. We run these classifiers for all the 100 words previously identified as having cultural bias in their use. The average classifier accuracy for the Australia-versus-all classification, using ten-fold cross validation, is 64.23%, as shown in the third column of Table 7. We repeat the same one-versus-all classification for United States, with an average accuracy of 54.89%; the results of this experiment are listed in the last column of Table 7. Overall, the performance improvement over the baseline is higher for Australia versus other countries (14.23% absolute improvement) than it is for United States versus others (4.89% absolute improvement). From this, we can infer that that the performance improvement over the baseline for the Australia versus United States task can be majorly attributed to the different word usages in Australia from the remaining countries. In other words, United States is more aligned with the "typical" (as measured over ten different countries) usage of these words than Australia is. Related Work Most of the previous cross-cultural research work has been undertaken in fields such as sociology, psychology, or antropology (De Secondat and others, 1748;Shweder, 1991;Cohen et al., 1996;Street, 1993). For instance, Shweder (1991) examined the cross-cultural similarities and differences in the perceptions, emotions, and ideologies of people belonging to different cultures, while Pennebaker et al. (1996) measured the emotional expressiveness among the northerners and southerners in their own countries, to test Montesquieu's geography hypothesis (De Secondat and others, 1748). More recently, the findings of Boroditsky et al. (2003) indicate that people's perception of certain inanimate objects (such as bridge, key, violin, etc.) is influenced by the grammatical genders assigned to these objects in their native languages. To our knowledge, there is only limited work in computational linguistics that explored cross-cultural differences through language analysis. Our work is most closely related to that by Paul and Girju (2009), in which they identify cultural differences in people's experiences in various countries from the perspective of tourists and locals. Specifically, they analyzed forums and blogs written by tourists and locals about their experiences in three countries, namely Singapore, India, and United Kingdom, using an extension of LDA. One of their findings is that while topic modeling on tourist forums offered an unsupervised aggregation of factual data specific to each country that would be important to travelers (such as destination's climate, law, and language), topic modeling on blogs authored by locals showed cultural differences between the three countries with respect to several topics (e.g., fashion, pets, religion, health). Yin et al. (2011) used topic models along with geographical configurations in Flickr to analyze cultural differences in the tags used for specific target image categories, such as cars, activities, festivals, or national parks. They performed a comparison over the topics across different geographical locations for each of the categories using three strategies of modeling geographical topics (location-driven, textdriven, and latent geographical topic analysis (LGTA) that combines location and text information), and found that the LGTA model worked well not only for finding regions of interest, but also for making effective comparisons of different topics across locations. Ramirez et al. (2008) performed two studies to examine the expression of depression among English and Spanish speakers on the Internet. The first study used LIWC categories to process depression and breast cancer posts to identify linguistic style of depressed language. Significantly more first person singular pronouns were used in both English and Spanish posts, supporting the hypothesis that depressed people tend to focus on themselves and detach from others. The second study focused on discovering the actual topics of conversation in the posts using Meaning Extraction Method . It was found that relational concerns (e.g., family, friends) were more likely expressed by depressed people writing in Spanish, while English people mostly mentioned medical concerns. Conclusions In this paper, we explored the problem of identifying word usage differences between people belonging to different cultures. Specifically, we studied differences between Australia and United States based on the words they used frequently in their online writings. Using a large number of examples for a set of 1, 500 words, covering different parts-of-speech, we showed that we can build classifiers based on linguistic features that can separate between the word usages from the two cultures with an accuracy higher than chance. We take this as an indication that there are significant differences in how these words are used in the two cultures, reflecting cultural bias in word use. To better understand these differences, we performed several analyses. First, using feature ablation, we identified the contextual and socio-linguistic features as the ones playing the most important role in these word use differences. Second, focusing on the words with the most significant differences, we used topic modeling to find the main topics for each of these words, which allowed us to identify the dominant topic for a word in each culture, pointing to several interesting word use differences as outlined in Table 5. We also measured the correlation between the topic distributions for the top 100 words between the two cultures, and found a medium correlation of 0.63. Finally, we also performed a one-versus-all classification for these 100 words, where word use instances drawn from one of Australia or United States were compared against a mix of instances drawn from ten other cultures, which suggested that United States is a more "typical" culture when it comes to word use (with significantly smaller differences in these one-versus-all classifications than Australia). In future work, we plan to extend this work to understand differences in word usages between a larger number of cultures, as well as for a larger variety of words (e.g., function words). The cross-cultural word datasets used in the experiments reported in this paper are available at http://lit.eecs.umich.edu. Table 2 : 2Average number of instances for the 1,500 target words for the years 2011-2014. Part-of-speech Average accuracy Words with significant differenceNouns 57.51* 393 Verbs 58.01* 395 Adjectives 59.25* 207 Adverbs 61.77* 215 Overall 58.36* 1210 Table 5 : 5Five sample words per part-of-speech with significant usage difference in the Australian and American cultures. All: All the features, Loc: Local features, Con: Contextual features, Soc: Sociolinguistic features, Syn: Syntactic features, NT: Number of topics. Table 6 : 6Statistics for blog data collected for additional English speaking countries.Part-of-speech United States vs. Australia Australia vs. all United States vs. allTable 7: Ten-fold cross-validation accuracies averaged over the top 100 target words for United States vs. Australia; Australia vs. a mix of ten other countries; United States vs. a mix of ten other countries.Nouns 65.54* 63.45* 57.07* Verbs 64.20* 63.97* 53.87* Adjectives 65.13* 64.36* 54.48* Adverbs 66.92* 65.13* 54.13* Overall 65.45* 64.23* 54.89* We follow the convention provided in http://nlp.stanford.edu/software/dependencies_manual.pdf We use the open source machine learning framework Weka(Hall et al., 2009) for all our experiments. We use the default base classifier for AdaBoost, i.e., a DecisionStump.4 This condition of equal data in each group is only approximate, as there will generally not be an exact division of bloggers with equal data. The average size of a cross-validation train split for a target word is 6246.57, while that for a test split is 819.88. LDA has been shown to be effective in text-related tasks, such as document classification(Wei and Croft, 2006). AcknowledgmentsThis material is based in part upon work supported by the National Science Foundation (#1344257), the John Templeton Foundation (#48503), and the Michigan Institute for Data Science. 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Who needs context? Classical techniques for Alzheimer's disease detection

Natural language processing (NLP) has shown great potential for Alzheimer's disease (AD) detection, particularly due to the adverse effect of AD on spontaneous speech. The current body of literature has directed attention toward context-based models, especially Bidirectional Encoder Representations from Transformers (BERTs), owing to their exceptional abilities to integrate contextual information in a wide range of NLP tasks. This comes at the cost of added model opacity and computational requirements. Taking this into consideration, we propose a Word2Vec-based model for AD detection in 108 age-and sex-matched participants who were asked to describe the Cookie Theft picture. We also investigate the effectiveness of our model by fine-tuning BERTbased sequence classification models, as well as incorporating linguistic features. Our results demonstrate that our lightweight and easyto-implement model outperforms some of the state-of-the-art models available in the literature, as well as BERT models.

Who needs context? Classical techniques for Alzheimer's disease detection July 14, 2023 Behrad Taghibeyglou behrad.taghibeyglou@mail.utoronto.ca Institute of Biomedical Engineering University of Toronto TorontoCanada KITE-Toronto Rehabilitation Institute University Health Network TorontoCanada Frank Rudzicz Faculty of Computer Science Dalhousie University HalifaxCanada Department of Computer Science University of Toronto TorontoCanada Vector Institute for Artificial Intelligence TorontoCanada Who needs context? Classical techniques for Alzheimer's disease detection Proceedings of the 5th Clinical Natural Language Processing Workshop the 5th Clinical Natural Language Processing WorkshopJuly 14, 2023 Natural language processing (NLP) has shown great potential for Alzheimer's disease (AD) detection, particularly due to the adverse effect of AD on spontaneous speech. The current body of literature has directed attention toward context-based models, especially Bidirectional Encoder Representations from Transformers (BERTs), owing to their exceptional abilities to integrate contextual information in a wide range of NLP tasks. This comes at the cost of added model opacity and computational requirements. Taking this into consideration, we propose a Word2Vec-based model for AD detection in 108 age-and sex-matched participants who were asked to describe the Cookie Theft picture. We also investigate the effectiveness of our model by fine-tuning BERTbased sequence classification models, as well as incorporating linguistic features. Our results demonstrate that our lightweight and easyto-implement model outperforms some of the state-of-the-art models available in the literature, as well as BERT models. Introduction Alzheimer's disease (AD) is the most prevalent form of dementia, a neurodegenerative disease that impairs cognitive functioning and is increasingly common in our aging society (Luz et al., 2021;Ilias and Askounis, 2022). According to the World Health Organization, approximately 55 million people currently suffer from dementia, with this number expected to surge to 78 million and 139 million by 2030 and 2050, respectively (Ilias and Askounis, 2022). Symptoms of AD include (but are not limited to) memory decline, disorientation, confusion, and behavioural changes. Importantly, AD progression can lead to loss of independence which significantly impacts patients, their families, and society as a whole (Pappagari et al., 2021). Given that late-stage AD progression is inevitable, early detection of AD through cost-effective and scal-able technologies is critical. While most clinical diagnoses of AD rely on neuroimaging, there is a critical need for more accessible and efficient methods of diagnosis. Accessible evaluation methods for AD include cognitive tests such as the Mini-Mental Status Examination (MMSE) (Kurlowicz and Wallace, 1999) and the Montréal Cognitive Assessment (MoCA) (Nasreddine et al., 2003). However, these methods still require active integration with an expert, and their specificity in early-stage diagnosis is questionable. During the course of AD, patients experience a gradual deterioration of cognitive function and accordingly may face a loss of lexical-semantic skills, including anomia, reduced word comprehension, object naming problems, semantic paraphasia, and a reduction in vocabulary and verbal fluency (Mirheidari et al., 2018;Pan et al., 2021;Chen et al., 2021). Speech processing and, consequently, natural language processing (NLP) can therefore provide new precision medicine tools for AD diagnosis that deliver objective quantitative analyses and reliable proof, analysis, comparison, and circulation for faster diagnosis. The Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) challenge of IN-TERSPEECH 2020 is a shared database developed to advance research into automatic AD detection based on spontaneous speech and transcripts (Luz et al., 2020). Participants in the challenge were tasked with describing the Cookie Theft picture in English, which is part of the Boston Diagnostic Aphasia Exam (Guo et al., 2021). The first set of the ADReSS 2020 database comprises speech recordings and CLAN-annotated transcripts of 54 AD patients and 54 sex-and age-matched controls. Various groups have worked with the ADReSS dataset, approaching the problem from different perspectives and leveraging available information. These studies typically combined speech processing and linguistic feature extraction or NLP-based fine-tuning. The literature on speech processing mostly focused on zero-crossing rate, spectral bandwidth, roll-off, and centroids of audio recordings, as well as active data representation cluster-based feature extraction methods including the emobase (Eyben et al., 2010), ComParE (Eyben et al., 2013), and Multi-Resolution Cochleagram (MRCG) (Chen et al., 2014) feature sets. Meanwhile, linguistic features have extracted lexical richness, the proportion of various PoS tags, utterance duration, total utterances, type-token ratio, openclosed class word ratio, and similarity between consecutive utterances. NLP-based methods have comprised from-scratch training or fine-tuning contextbased models, such as bidirectional long shortterm memory (bi-LSTM) (Cummins et al., 2020), bi-directional Hierarchical Attention Network (bi-HANN) (Cummins et al., 2020), Convolutional Recurrent Neural Network (CRNN) (Koo et al., 2020), and Bidirectional Encoder Representations from Transformer (BERT) (Balagopalan et al., 2020). Despite excellent performance compared to baseline methods (Luz et al., 2020), the complexity of these methodologies and the need to implement them on high-memory GPUs highlights the need to explore simpler methodologies that can ensure ease and performance in AD detection. In this paper, we present a novel approach for detecting AD in the first set of ADReSS dataset by integrating a new Word2Vec-based model and dimension reduction method. We not only implement and compare top-cited and recent state-of-the-art models on the same dataset, but also demonstrate that our approach outperforms these models. Our proposed approach is simple, easy to implement, and highly accurate. Methodology Other models In order to evaluate the performance of our proposed language processing model, we have considered several publicly available models for comparison including: • Linguistic-Based Features (LBF): In this study, we utilized the CLAN package to extract 34 linguistic-based features (LBFs) from transcripts, including duration, total utterances, mean length of utterance (MLU), typetoken ratio, open-closed class word ratio, and percentages of 9 parts of speech. We also incorporated demographic information such as age and sex. To identify the most informative features for classification, we performed correlation and variance analyses on the extracted features using the FeatureWiz package (Au-toViML, 2020). We set a correlation threshold of 0.6 and repeated the analyses 5 times with random seeds over all samples. We then selected the top 5 features that appeared in at least 3 iterations for further classification. • BERT Models: Since BERT models have shown promising performance in different applications of NLP, in this study we leveraged some of BERT-based architectures with a maximum length of 512 tokens as a reference for our model. We tested three versions of uncased base BERT (Devlin et al., 2018): one with no extension in the last layers, called baseBERT1, another with two fully connected layers at the end (768 → 64 and 64 → 1), called baseBERT2, and the last one with three fully connected layers (768 → 128, 128 → 16, and 16 → 1), called baseBERT3. ) with a batch size of 4 and 3 epochs. We used binary cross-entropy as the loss function for all models and AdamW (Adam with weight decay) (Loshchilov and Hutter, 2017) as the optimizer with a learning rate of 2 × 10 −5 . To address potential issues with local optima, we applied a linear warm-up scheduler. Each transcript is classified as AD if the average of the probabilities (after the sigmoid layer) over all sentences in the transcript is greater than or equal to 0.5; otherwise, it is classified as control. Pre-processing To preprocess the data for our proposed model, we have neglected the first four sentences of each transcript, as the initial speaker is typically a member of the data collection team. Additionally, stop words were removed from each sentence using the Gensim library (Řehřek et al., 2011). Proposed model In this study, we used Wikipedia2Vec (Yamada et al., 2018), a tool that generates embeddings (or vector representations) of words and entities from Wikipedia, to convert tokens to vector embeddings. We used the skip-gram strategy for training, and the embedding dimension of the model was set to 500. We denote this model as W 2V throughout this paper. Suppose that each participant's transcript consists of N k sentences, each comprising m words, where m varies from 1 to M k (the maximum length among all sentences in the k th transcript). We input each word ⟨w i,k ⟩ into the W 2V model (W 2V (⟨w i,k ⟩)), which outputs the corresponding embedded vector x i,k ∈ R 500 . All embeddings of the k th transcript form the set X k . We standardized each 500-dimensional vector across all embeddings of each subject using the following formula: y k = med(X k ) std(X k ) ,(1) where med is the median operator applied to each dimension independently, std is the standard deviation of embeddings, and y k denotes the standardized vector for the k th participant. So far, we developed the first framework and leveraged the previously introduced feature selection method by iteratively applying FeatureWiz five times. We then selected features that were chosen at least three times during the process to identify the most informative dimensions for AD detection. This feature selection procedure reduced the dimension from 500 to 64. We refer to this first framework as model 1, and Figure 1 illustrates the process. To further enhance our analysis, we concatenate linguisticsbased features from the previous section with W2Vbased feature vectors and apply feature selection in a similar manner to model 1. This second framework, called model 2, resulted in the selection of 86 features (out of 537 features). Prior to inputting the features into the classifiers of each model, the zero-mean-unit-variance standardization technique is applied to normalize the features. Evaluation and Metrics All results presented in this study were obtained using the leave-one-subject-out (LOSO) crossvalidation technique to evaluate the generalizability of the models. Thus, a total of 104 models were trained per architecture/classifier. For each model, accuracy, sensitivity, specificity, and F1 were re-ported as performance metrics. For the featurebased models, such as linguist-based features and our proposed frameworks, we employed various classifiers including logistic regression (LR), decision tree (DT), linear and Nu-support vector classification (SVC), linear and quadratic discriminant analysis (LDA and QDA), Gaussian naive Bayes (GNB), extreme gradient boosting (XG-Boost), adaptive boosting (AdaBoost), and extra trees classifier. Results Other models We investigated different BERT models for AD classification, and the results are presented in Table 1. As expected, the performance of Bio-Clinical BERT and DistilBERT models were comparable; however, Bio-Clinical BERT showed superior sensitivity and was chosen as the best BERT model in this study. Additionally, as demonstrated in Table 2, integrating linguistic-based features with feature selection and a combination of classifiers achieved an accuracy of 0.81 in AD detection. Table 1: LOSO performance of other BERT-based models. "E" denotes the number of epochs, "BS" denotes the batch size, and "AC", "SP", and "SE" represent accuracy, specificity, and sensitivity, respectively. Proposed frameworks The performance of our proposed frameworks is presented in Table 3. The best performance was achieved by model 2 with the help of the GNB classifier, which obtained an accuracy of 0.90. On the other hand, the best performance of model 1 was achieved by the ExtraTrees classifier. Comparison with previous literature LOSO Classification Feature Normalization zero-mean unit-variance standardization models on the same dataset (Balagopalan et al., 2020(Balagopalan et al., , 2021. It is worth noting that our proposed model also outperformed the baseline linguistic model introduced in the ADReSS challenge. (Balagopalan et al., 2020(Balagopalan et al., , 2021 0.87 0.91 0.83 0.87 Gated LSTM on acoustic and lexical (Rohanian et al., 2021) 0.77 ---Baseline Linguistic (Luz et al., 2020) 0.77 0.77 0.76 0.77 Best proposed model 0.90 0.89 0.91 0.9 Table 4: LOSO performance comparison of the best proposed model and explored models with some existing models on the same dataset. The best linguist-based features model uses QDA classifier with linguist-based features, and the best proposed model is our proposed model 2 with GNB classifier. Discussion By mapping each word into a 500-dimensional space where words with similar context are closer together, the proposed model can identify when all words in a transcript are focused on the same topic with minimal deviations. Coupled with the suggested standardization method, the results demonstrate a significant difference in performance between the proposed model and the only linguistbased model, which prioritizes utterances, pauses, and interactions between text and speech. The BERT models explored in this study are relatively massive and require significant computational resources, and training them requires delicate hyperparameter optimization. In this study, we followed the BERT authors' recommendations to keep the model's trainability on an Nvidia RTX 3080 GPU and to avoid changing the weights of the model by selecting smaller epoch numbers. Conclusions In this study, we introduced a word2vec-based model that combines pre-trained Wikipedia embeddings with linguistic features. We also employed correlation-based feature selection to reduce the dimensionality of the embeddings. The results demonstrated that our proposed model outperformed existing models on the same dataset. However, as BERT models offer diverse applicability, a potential future direction is to incorporate feature maps extracted from the hidden states of these networks to enhance the performance of our model. 105 For baseBERT2, we varied the training epochs between 3 and 5. Additionally, we tested Bio-CLinical BERT (Alsentzer et al., 2019) with a batch size of 4 and 3 epochs, DistilBERT (Sanh et al., 2019) with a batch size of 4 and 3 epochs, and BioMed-RoBERTa-based (Gururangan et al., 2020 Figure 1 : 1Proposed Table 4 4compares our proposed model with the ex- isting models in the literature as well as the ones explored in this paper. Our model achieved sig- nificantly higher performance, including a 3% im- provement in accuracy and an 8% improvement in sensitivity compared to one of the BERT-based Table 2 : 2LOSO performance of the linguist feature- based model, in combination with the proposed feature selection technique. 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Victor Sanh, Lysandre Debut, Julien Chaumond, Thomas Wolf, arXiv:1910.01108Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprintVictor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. Ikuya Yamada, Akari Asai, Jin Sakuma, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, Yuji Matsumoto, arXiv:1812.06280Wikipedia2vec: An efficient toolkit for learning and visualizing the embeddings of words and entities from wikipedia. arXiv preprintIkuya Yamada, Akari Asai, Jin Sakuma, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, and Yuji Matsumoto. 2018. Wikipedia2vec: An efficient toolkit for learning and visualizing the embeddings of words and entities from wikipedia. arXiv preprint arXiv:1812.06280.

21 ème Traitement Automatique des Langues Naturelles

Pour une communauté, la terminologie est essentielle car elle permet de décrire, échanger et récupérer les données. Dans de nombreux domaines, l'explosion du volume des données textuelles nécessite de recourir à une automatisation du processus d'extraction de la terminologie, voire son enrichissement. L'extraction automatique de termes peut s'appuyer sur des approches de traitement du langage naturel. Des méthodes prenant en compte les aspects linguistiques et statistiques proposées dans la littérature, résolvent quelques problèmes liés à l'extraction de termes tels que la faible fréquence, la complexité d'extraction de termes de plusieurs mots, ou l'effort humain pour valider les termes candidats. Dans ce contexte, nous proposons deux nouvelles mesures pour l'extraction et le "ranking" des termes formés de plusieurs mots à partir des corpus spécifiques d'un domaine. En outre, nous montrons comment l'utilisation du Web pour évaluer l'importance d'un terme candidat permet d'améliorer les résultats en terme de précision. Ces expérimentations sont réalisées sur le corpus biomédical GENIA en utilisant des mesures de la littérature telles que C-value.Abstract. Comprehensive terminology is essential for a community to describe, exchange, and retrieve data. In multiple domain, the explosion of text data produced has reached a level for which automatic terminology extraction and enrichment is mandatory. Automatic Term Extraction (or Recognition) methods use natural language processing to do so. Methods featuring linguistic and statistical aspects as often proposed in the literature, rely some problems related to term extraction as low frequency, complexity of the multi-word term extraction, human effort to validate candidate terms. In contrast, we present two new measures for extracting and ranking muli-word terms from domain-specific corpora, covering the all mentioned problems. In addition we demonstrate how the use of the Web to evaluate the significance of a multi-word term candidate, helps us to outperform precision results obtain on the biomedical GENIA corpus with previous reported measures such as C-value.Mots-clés : Extraction Automatique de Termes, Mesure basée sur le Web, Mesure Linguistique, Mesure Statistique, Traitement Automatique du Langage Biomédical.

21 ème Traitement Automatique des Langues Naturelles 2014 Juan Antonio Lossio-Ventura Université de Montpellier 2 CNRS Montpellier -France (2) IrsteaCirad TETIS MontpellierFrance Clement Jonquet clement.jonquet@lirmm.fr Université de Montpellier 2 CNRS Montpellier -France (2) IrsteaCirad TETIS MontpellierFrance Mathieu Roche mathieu.roche@cirad.fr Université de Montpellier 2 CNRS Montpellier -France (2) IrsteaCirad TETIS MontpellierFrance Maguelonne Teisseire teisseire@teledetection.fr Université de Montpellier 2 CNRS Montpellier -France (2) IrsteaCirad TETIS MontpellierFrance 21 ème Traitement Automatique des Langues Naturelles Marseille2014407Automatic Term ExtractionWeb-based measureLinguistic-based measureStatistic-based measureBiomedical Natural Language Processing Pour une communauté, la terminologie est essentielle car elle permet de décrire, échanger et récupérer les données. Dans de nombreux domaines, l'explosion du volume des données textuelles nécessite de recourir à une automatisation du processus d'extraction de la terminologie, voire son enrichissement. L'extraction automatique de termes peut s'appuyer sur des approches de traitement du langage naturel. Des méthodes prenant en compte les aspects linguistiques et statistiques proposées dans la littérature, résolvent quelques problèmes liés à l'extraction de termes tels que la faible fréquence, la complexité d'extraction de termes de plusieurs mots, ou l'effort humain pour valider les termes candidats. Dans ce contexte, nous proposons deux nouvelles mesures pour l'extraction et le "ranking" des termes formés de plusieurs mots à partir des corpus spécifiques d'un domaine. En outre, nous montrons comment l'utilisation du Web pour évaluer l'importance d'un terme candidat permet d'améliorer les résultats en terme de précision. Ces expérimentations sont réalisées sur le corpus biomédical GENIA en utilisant des mesures de la littérature telles que C-value.Abstract. Comprehensive terminology is essential for a community to describe, exchange, and retrieve data. In multiple domain, the explosion of text data produced has reached a level for which automatic terminology extraction and enrichment is mandatory. Automatic Term Extraction (or Recognition) methods use natural language processing to do so. Methods featuring linguistic and statistical aspects as often proposed in the literature, rely some problems related to term extraction as low frequency, complexity of the multi-word term extraction, human effort to validate candidate terms. In contrast, we present two new measures for extracting and ranking muli-word terms from domain-specific corpora, covering the all mentioned problems. In addition we demonstrate how the use of the Web to evaluate the significance of a multi-word term candidate, helps us to outperform precision results obtain on the biomedical GENIA corpus with previous reported measures such as C-value.Mots-clés : Extraction Automatique de Termes, Mesure basée sur le Web, Mesure Linguistique, Mesure Statistique, Traitement Automatique du Langage Biomédical. Introduction Les méthodes d'Extraction Automatique de Termes (EAT) visent à extraire automatiquement des termes techniques à partir d'un corpus. Ces méthodes sont essentielles pour l'acquisition des connaissances d'un domaine pour des tâches telles que la mise à jour de lexique. En effet, les termes techniques sont importants pour mieux comprendre le contenu d'un domaine. Ces termes peuvent être : (i) composés d'un seul mot (généralement simple à extraire), ou (ii) composés de plusieurs mots (difficile à extraire). Notre travail concerne plus spécifiquement l'extraction de termes composés de plusieurs mots. Les méthodes d'EAT impliquent généralement deux étapes principales. La première extrait des candidats en calculant "l'unithood" qui qualifie une chaîne de mots comme une expression valide (Korkontzelos et al., 2008). La deuxième étape calcule le "termhood" qui sert à mesurer la spécificité propre à un domaine. Il existe quelques problèmes connus de l'EAT tels que : (i) l'extraction de termes non pertinents (bruit) ou le nombre réduit de termes pertinents retournés (silence), (ii) l'extraction de termes de plusieurs mots qui ont inévitablement des structures complexes, (iii) l'effort humain dans la validation manuelle des termes candidats, (iv) l'application aux corpus de grande échelle. En réponse à ces problèmes, nous proposons deux nouvelles mesures. La première, appelée LIDFvalue, est fondée sur l'information statistique et linguistique. La mesure LIDF-value permet de mieux prendre en compte l'unithood, en lui adossant un niveau de qualité. Elle traite les problèmes i), ii) et iv). La seconde, appelée WAHI, est une mesure fondée sur le Web traitant les problèmes i), ii) et iii). Dans cet article, nous comparons la qualité des méthodes proposées avec les mesures de référence les plus utilisées. Nous démontrons que l'utilisation de ces deux mesures améliore l'extraction automatique de termes spécifiques d'un domaine, à partir de textes qui n'offrent pas une fiabilité statistique liée aux fréquences. Le reste du papier est organisé comme suit : nous discutons tout d'abord des travaux connexes dans la Section 2. Les deux nouvelles mesures sont ensuite décrites en Section 3. L'évaluation en termes de précision est présentée dans la Section 4 suivie des conclusions en Section 5. État de l'art Plusieurs études récentes se sont concentrées sur l'extraction des termes de plusieurs mots (n-grammes) et d'un seul mot (unigrammes). Les méthodes d'extraction de terme existantes peuvent être divisées en quatre grandes catégories : (i) linguistique, (ii) statistique, (iii) apprentissage automatique, et (iv) hybride. La plupart de ces techniques appartiennent à des approches de fouille de textes. Les techniques existantes fondées sur le Web ont rarement été appliquées à l'EAT, mais comme nous le verrons, ces approches peuvent être adaptées à cet objectif. Méthodes de Fouille de Textes Les méthodes liées à la fouille de textes combinent en général différents types d'approches : linguistique (Gaizauskas et al., 2000;Krauthammer & Nenadic, 2004) Les méthodes hybrides sont principalement linguistiques et statistiques. GlossEx (Kozakov et al., 2004) estime la probabilité du mot dans un corpus de domaine comparée à la probabilité du même mot dans un corpus général. Weirdness (Ahmad et al., 1999) estime que la distribution des mots dans un corpus spécifique est différente de la distribution des mots dans un corpus général. C/NC-value (Frantzi et al., 2000), qui combine l'information statistique et linguistique pour l'extraction de termes de plusieurs mots et des termes imbriqués, est la mesure la plus connue dans le domaine biomédical. Dans (Zhang et al., 2008), les auteurs montrent que C-value a des meilleurs résultats par rapport à d'autres mesures citées ci-dessus. Une autre mesure est F-TFIDF-C (Lossio-Ventura et al., 2014) qui combine une mesure d'EAT (C-value) et une mesure d'EAMC (TF-IDF) pour extraire des termes obtenant des résultats plus satisfaisants que C-value. Aussi, C-value a été appliquée à de nombreuses langues autres que l'anglais, comme le japonais, serbe, slovène, polonais, chinois, espagnol, arabe, et français (Ji et al., 2007;Barron-Cedeno et al., 2009;Lossio-Ventura et al., 2013). Ainsi, nous proposons d'adopter C-value et F-TFIDF-C comme référence pour l'étude comparative au cours de nos expérimentations. Méthodes de Fouille du Web Différentes études de fouille du Web se concentrent sur la similarité et les relations sémantiques. Les mesures d'association de mots peuvent être divisées en trois catégories (Chaudhari et al., 2011) : (i) Co-occurrence qui s'appuient sur les fréquences de co-occurrence de deux mots dans un corpus, (ii) Basées sur la similarité distributionnelle qui caractérisent un mot par la distribution d'autres mots qui l'entourent, et (iii) Basées sur les connaissances, comme les thésaurus, les réseaux sémantiques, ou taxonomies. Dans cet article, nous nous concentrons sur les mesures de co-occurrence, car notre objectif est d'extraire les termes de plusieurs mots et nous proposons de calculer un degré d'association entre les mots qui composent un terme. Les mesures d'association de mots sont utilisées dans plusieurs domaines comme l'écologie, la psychologie, la médecine et le traitement du langage. De telles mesures ont été récemment étudiées dans (Pantel et al., 2009) (Zadeh & Goel, 2013, telles que Dice, Jaccard, Overlap, Cosine. Une autre mesure pour calculer l'association entre les mots utilisant les résultats des moteurs de recherche Web est la Normalized Google Distance (Cilibrasi & Vitanyi, 2007). Elle s'appuie sur le nombre de fois où les mots apparaissent ensemble dans le document indexé par un moteur de recherche. Dans cette étude, nous comparons les résultats de notre mesure fondée sur le Web avec les mesures d'association de référence (Dice, Jaccard, Overlap, Cosine). EXTRACTION AUTOMATIQUE DE TERMES BIOMÉDICAUX De manière similaire aux travaux de la littérature, nous supposons que les termes d'un domaine ont une structure syntaxique similaire. Par conséquent, nous construisons une liste de patrons linguistiques les plus courants selon la structure syntaxique des termes techniques présents dans un dictionnaire. Dans notre cas, il s'agit d'UMLS 1 qui est un ensemble de référence de terminologies biomédicales. Dans un premier temps, nous effectuons l'étiquetage grammatical des termes contenus dans le dictionnaire en utilisant Stanford CoreNLP API (POS tagging) 2 . Ensuite nous calculons la fréquence des structures syntaxiques. Nous sélectionnons les 200 fréquences les plus élevées pour construire la liste de patrons (ou motifs) qui seront pris en considération. Cette liste est pondérée selon la fréquence d'apparition de chaque patron par rapport à l'ensemble des motifs. Un terme candidat est alors retenu s'il appartient à la liste des structures syntaxiques sélectionnées. Le nombre de termes utilisés pour construire cette liste était de 2 300 000. La Figure 1 illustre le calcul de la probabilité des patrons linguistiques. L'objectif de notre mesure, LIDF-value (Linguisitic patterns, IDF, and C-value information) est de calculer le termhood pour chaque terme, en utilisant la probabilité calculée précédemment, également avec l'idf , et C-value de chaque terme. La fréquence inverse de document (idf ) est une mesure indiquant si le terme est commun ou rare dans tous les documents. Il est obtenu en divisant le nombre total de documents par le nombre de documents contenant le terme, puis en prenant le logarithme de ce quotient. La probabilité et l'idf améliorent la pondération des termes de faible fréquence. En outre, la mesure C-value est fondée sur la fréquence des termes. Le but de C-value (Formule 1) est d'améliorer l'extraction de termes imbriqués. Ce critère favorise les termes candidats n'apparaissant pas dans des termes plus longs. Par exemple, dans un corpus spécialisé (ophtalmologie), (Frantzi et al., 2000) ont trouvé le terme non pertinent soft contact cependant le terme plus long soft contact lens est pertinent. C-value(A) =            log 2 (|A|) × f (A) si A / ∈ imbriqué log 2 (|A|) ×   f (A) − 1 |S A | × b∈S A f (b)   sinon(1) Où A est un terme de plusieurs mots, |A| le nombre de mots de A ; f (A) la fréquence du terme A, SA l'ensemble de termes qui contiennent A et |SA| le nombre de termes de SA. Ainsi, C-value utilise soit la fréquence du terme si le terme n'est pas inclus dans d'autres termes (première ligne), ou diminue cette fréquence si le terme apparaît dans d'autres termes (deuxième ligne). Nous avons combiné ces différentes informations statistiques (i.e., probabilité des patrons linguistiques, C-value, idf ) pour proposer une nouvelle mesure globale de ranking appelée LIDF-value (Formule 2). Dans cette formule, A représente un terme de plusieurs mots ; P(A LP ) la probabilité du patron linguistique LP associé au terme A qui a la même structure que le patron linguistique LP , c'est-à-dire le poids du patron linguistique LP calculé précédemment. LIDF -value(A) = P(A LP ) × idf (A) × C-value(A)(2) Ainsi, pour calculer LIDF-value nous exécutons trois étapes, résumées ci-dessous : (1) Étiquetage grammatical : nous effectuons l'étiquetage morpho-syntaxique du corpus, ensuite nous considérons le lemme de chaque mot. (2) Extraction de termes candidats : avant d'appliquer les mesures, nous filtrons notre corpus en utilisant les patrons calculés précédemment. Nous choisissons uniquement les termes qui ont une structure syntaxique présente dans la liste de patrons sélectionnés. (3) Ranking de termes candidats : enfin, nous calculons la valeur de LIDF-value pour chaque terme. Afin d'améliorer le classement des termes pertinents, nous proposons, dans la sous-section suivante, de prendre en compte l'information Web. Une nouvelle mesure de ranking fondée sur le Web : WAHI Des travaux associés à la fouille du Web interrogent les moteurs de recherche pour mesurer l'association entre les mots. Ceci peut être utilisé pour mesurer l'association des mots qui composent un terme (e.g., soft, contact, et lens qui composent le terme pertinent soft contact lens). Dans nos travaux, nous proposons d'associer le critère de Dice avec une mesure d'association appelée W ebR (Lossio-Ventura et al., 2014) (Formule 3). Par exemple pour le terme soft contact lens, le numérateur correspond au nombre de pages Web avec la requête "soft contact lens", et le dénominateur correspond au résultat de la requête soft ET contact ET lens. W ebR(A) = nb("A") nb(A)(3) La mesure W AHI (Web Association based on Hits Information) que nous proposons, combine Dice et W ebR de la manière suivante : W AHI(A) = n × nb("A") n i=1 nb(a i ) × nb("A") nb(A)(4) Résultats Les résultats sont évalués en termes de précision obtenue sur les k premiers termes (P @k) pour les deux mesures présentées dans la section précédente. Résultats de LIDF-value : Le tableau 1 compare les résultats de C-value, F-TFIDF-C, avec notre mesure LIDF-value. Le meilleur résultat est obtenu par LIDF-value pour l'ensemble des valeurs de k. LIDF-value est donc plus performante que les mesures de référence avec un gain en précision de 11 points pour les 100 premiers termes extraits. Ces résultats de précision sont illustrés dans la Figure 2. Résultats des indexes de termes : Nous avons évalué LIDF-value et les mesures de référence avec une séquence de ngrammes de mots (i.e., un n-grammes de mots est un terme de n mots, par exemple human immunodeficiency virus est un 3-grammes de mots). Pour cela, nous construisons un index composé de n-grammes de mots (n ≥ 2). Nous expérimentons la performance de LIDF-value sur les n-grammes de mots en prenant les 1 000 premiers termes. Le Tableau 2 présente la comparaison de la précision pour les 2-grammes, 3-grammes et 4+ grammes de mots. C-value F -T F IDF -C LIDF-valueP@100 EXTRACTION AUTOMATIQUE DE TERMES BIOMÉDICAUX 2-grammes de mots 3-grammes de mots 4+ grammes de mots C-value F -T F IDF -C LIDF-value C-value F -T F IDF -C LIDF-value C-value F -T F IDF -C LIDF- Conclusions et Futurs travaux L'article présente deux mesures pour l'extraction automatique de termes composés de plusieurs mots. La première est une mesure statistique et linguistique, LIDF-value, qui améliore la précision de l'extraction automatique de termes en comparaison avec les mesures classiques. Elle permet de compenser le manque d'information propre à la fréquence avec les valeurs des probabilités des patrons linguistiques et idf. La seconde, WAHI, est une mesure fondée sur le Web et prend comme entrée la liste de termes obtenus avec LIDF-value. Cette mesure permet de réduire l'effort humain conséquent , statistiques (Van Eck et al., 2010) ou fondées sur un apprentissage automatique pour l'extraction et la classification des termes (Newman et al., 2012). Il faut également citer les propositions issues du domaine de l'Extraction Automatique de Mots Clés (EAMC) dont les mesures peuvent être adaptées pour l'extraction de termes d'un corpus (Lossio-Ventura et al., 2013) (Lossio-Ventura et al., 2014). FIGURE 1 : 1Exemple de construction de patrons linguistiques (où NN : nom, IN : préposition, JJ : adjectif, et CD : numéro). 1 . 1http://www.nlm.nih.gov/research/umls 2. http://nlp.stanford.edu/software/corenlp.shtml Où a i est un mot, a i ∈ A et a i = {nom, adjectif, mot etranger}. Nous montrons que les ressources de domaine ouvert, telles que le Web, peuvent être exploitées pour aider l'extraction de termes spécifiques.Dans nos expérimentations, nous utilisons le corpus GENIA 3 , qui est composé de 2 000 titres et des résumés d'articles des journaux issus de Medline, avec plus de 400 000 mots. GENIA contient des expressions linguistiques qui font référence à des entités d'intérêt en biologie moléculaire telles que les protéines, les gènes et les cellules. L'annotation des termes techniques couvre l'identification des entités physiques biologiques ainsi que d'autres termes importants. Afin de mettre en place un protocole de validation automatique et de couvrir les termes médicaux, nous créons un dictionnaire qui contient tous les termes d'UMLS ainsi que tous les termes techniques de GENIA. De cette manière nous pouvons évaluer la précision avec un dictionnaire de référence plus complet.4 Expérimentations 4.1 Corpus et Protocole TABLE 1 : 1Précisionselon le nombre de termes (P @k) FIGURE 2: Précision selon le nombre de termes TABLE 2 : 2Comparaison par les moteurs de recherche. À cette étape, l'objectif est de faire le re-ranking des 1 000 termes améliorant la précision par intervalles. Le Tableau 3 montre la précision obtenue après le re-ranking avec WAHI et les mesures d'association de référence, utilisant les moteurs de recherche Yahoo et Bing. Ce tableau souligne que WAHI est bien adaptée pour l'EAT obtenant des meilleurs résultats de précision que les mesures de référence.de précision des 2-grammes de mots, 3-grammes de mots et 4+ grammes de mots Résultats de WAHI : Notre approche de fouille du Web est appliquée à la fin du processus, avec les 1 000 premiers termes extraits avec les mesures linguistiques et statistiques. La raison principale de cette limitation est le nombre restreint de requêtes autorisées YAHOO BING WAHI Dice Jaccard Cosine Overlap WAHI Dice Jaccard Cosine Overlap P@100 0.900 0.720 0.720 0.76 0.730 0.800 0.740 0.730 0.680 0.650 P@200 0.800 0.775 0.770 0.740 0.765 0.800 0.775 0.775 0.735 0.705 P@300 0.800 0.783 0.780 0.767 0.753 0.800 0.770 0.763 0.740 0.713 P@400 0.800 0.770 0.765 0.770 0.740 0.800 0.765 0.765 0.752 0.712 P@500 0.820 0.764 0.754 0.762 0.738 0.800 0.760 0.762 0.758 0.726 P@600 0.767 0.748 0.740 0.765 0.748 0.817 0.753 0.752 0.753 0.743 P@700 0.786 0.747 0.744 0.747 0.757 0.814 0.7514 0.751 0.733 0.749 P@800 0.775 0.752 0.7463 0.740 0.760 0.775 0.745 0.747 0.741 0.754 P@900 0.756 0.749 0.747 0.749 0.747 0.778 0.747 0.748 0.742 0.748 P@1000 0.746 0.746 0.746 0.746 0.746 0.746 0.746 0.746 0.746 0.746 TABLE 3 : 3Comparaison de précision de WAHI avec YAHOO et BING et les mesures d'associationDiscussion. LIDF-value obtient les meilleurs résultats de précision sur tous les intervalles pour l'extraction des termes et pour l'extraction de n-grammes de mots. Le tableau 4 présente les précisions obtenues par nos deux mesures sur le corpus GENIA. WAHI basée sur Yahoo obtient une meilleure précision (90 %) pour P @100. En comparaison, WAHI basée sur Bing obtient une précision de 80 %. Pour les autres intervalles, le Tableau 4 montre que WAHI fondée sur Bing obtient en général des résultats légèrement meilleurs. La performance de WAHI dépend du moteur de recherche adopté du fait de l'algorithme d'indexation associé. Enfin, le Tableau 4 montre que le re-ranking avec WAHI augmente la précision de LIDF-value.LIDF-value WAHI (Bing) WAHI (Yahoo) P@100 0.840 0.800 0.900 P@200 0.790 0.800 0.800 P@300 0.780 0.800 0.800 P@400 0.757 0.800 0.800 P@500 0.752 0.800 0.820 P@600 0.765 0.817 0.767 P@700 0.761 0.814 0.786 P@800 0.755 0.775 0.775 P@900 0.757 0.778 0.756 P@1000 0.746 0.746 0.746 TABLE 4 : 4Précisions obtenues par LIDF-value et WAHI selon le nombre de terme (P @k) Deux nouvelles mesures pour l'extraction de termes 3.1 Une mesure fondée sur l'information linguistique et statistique : LIDF-valueNotre première contribution consiste à donner une meilleure importance à l'unithood des termes afin de détecter des termes avec une faible fréquence. . http://www.nactem.ac.uk/genia/genia-corpus/term-corpus Nous montrons expérimentalement que LIDF-value offre des meilleurs résultats que les mesures de référence pour l'extraction de n-grammes de mots issus du domaine biomédical sur le corpus GENIA. Par ailleurs, ces résultats sont améliorés par l'utilisation de la mesure WAHI. nécessaire à la validation des termes candidatsnécessaire à la validation des termes candidats. Nous montrons expérimentalement que LIDF-value offre des meilleurs résultats que les mesures de référence pour l'extraction de n-grammes de mots issus du domaine biomédical sur le corpus GENIA. Par ailleurs, ces résultats sont améliorés par l'utilisation de la mesure WAHI. Tout d'abord, nous souhaitons utiliser le Web pour extraire des termes plus longs que ceux actuellement obtenus. De plus, nous projetons de tester cette approche générale sur d'autres domaines, tels que l'écologie et l'agronomie. Enfin, nous visons à expérimenter notre proposition sur des corpus d. Les perspectives à ce travail sont nombreuses. autres langues telles que le français et l'espagnolLes perspectives à ce travail sont nombreuses. Tout d'abord, nous souhaitons utiliser le Web pour extraire des termes plus longs que ceux actuellement obtenus. De plus, nous projetons de tester cette approche générale sur d'autres domaines, tels que l'écologie et l'agronomie. Enfin, nous visons à expérimenter notre proposition sur des corpus d'autres langues telles que le français et l'espagnol. University of surrey participation in trec8 : Weirdness indexing for logical document extrapolation and retrieval (wilder). Ahmad K Gillam L. &amp; Tostevin L, TREC. AHMAD K., GILLAM L. & TOSTEVIN L. (1999). University of surrey participation in trec8 : Weirdness indexing for logical document extrapolation and retrieval (wilder). In TREC. Lexical co-occurrence, statistical significance, and word association. Barron-Cedeno A, Sierra G, Drouin P. &amp; Ananiadou S, Proceedings of the Conference on Empirical Methods in Natural Language Processing. Springer. CHAUDHARI D. L., DAMANI O. 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Apertium-IceNLP: A rule-based Icelandic to English machine translation system

We describe the development of a prototype of an open source rule-based Icelandic→English MT system, based on the Apertium MT framework and IceNLP, a natural language processing toolkit for Icelandic. Our system, Apertium-IceNLP, is the first system in which the whole morphological and tagging component of Apertium is replaced by modules from an external system. Evaluation shows that the word error rate and the positionindependent word error rate for our prototype is 50.6% and 40.8%, respectively. As expected, this is higher than the corresponding error rates in two publicly available MT systems that we used for comparison. Contrary to our expectations, the error rates of our prototype is also higher than the error rates of a comparable system based solely on Apertium modules. Based on error analysis, we conclude that better translation quality may be achieved by replacing only the tagging component of Apertium with the corresponding module in IceNLP, but leaving morphological analysis to Apertium.

Apertium-IceNLP: A rule-based Icelandic to English machine translation system Martha Dís Brandt marthab08@ru.is School of Computer Science Dept. Lleng. i. Sist. Inform. Universitat d'Alacant E Reykjavik University IS-101, 03071Reykjavik, AlacantIceland, Spain Hrafn Loftsson School of Computer Science Dept. Lleng. i. Sist. Inform. Universitat d'Alacant E Reykjavik University IS-101, 03071Reykjavik, AlacantIceland, Spain Hlynur Sigurþórsson hlynurs06@ru.is School of Computer Science Dept. Lleng. i. Sist. Inform. Universitat d'Alacant E Reykjavik University IS-101, 03071Reykjavik, AlacantIceland, Spain Francis M Tyers ftyers@dlsi.ua.es School of Computer Science Dept. Lleng. i. Sist. Inform. Universitat d'Alacant E Reykjavik University IS-101, 03071Reykjavik, AlacantIceland, Spain Apertium-IceNLP: A rule-based Icelandic to English machine translation system We describe the development of a prototype of an open source rule-based Icelandic→English MT system, based on the Apertium MT framework and IceNLP, a natural language processing toolkit for Icelandic. Our system, Apertium-IceNLP, is the first system in which the whole morphological and tagging component of Apertium is replaced by modules from an external system. Evaluation shows that the word error rate and the positionindependent word error rate for our prototype is 50.6% and 40.8%, respectively. As expected, this is higher than the corresponding error rates in two publicly available MT systems that we used for comparison. Contrary to our expectations, the error rates of our prototype is also higher than the error rates of a comparable system based solely on Apertium modules. Based on error analysis, we conclude that better translation quality may be achieved by replacing only the tagging component of Apertium with the corresponding module in IceNLP, but leaving morphological analysis to Apertium. Introduction Over the last decade or two, statistical machine translation (SMT) has gained significant momentum and success, both in academia and industry. SMT uses large parallel corpora, texts that are translations of each other, during training to derive a statistical translation model which is then used to c 2011 European Association for Machine Translation. translate between the source language (SL) and the target language (TL). SMT has many advantages, e.g. it is data-driven, language independent, does not need linguistic experts, and prototypes of new systems can by built quickly and at a low cost. On the other hand, the need for parallel corpora as training data in SMT is also its main disadvantage, because such corpora are not available for a myriad of languages, especially the so-called less-resourced languages, i.e. languages for which few, if any, natural language processing (NLP) resources are available. When there is a lack of parallel corpora, other machine translation (MT) methods, such as rule-based MT, e.g. Apertium (Forcada et al., 2009), may be used to create MT systems. In this paper, we describe the development of a prototype of an open source rule-based Icelandic→English (is-en) MT system based on Apertium and IceNLP, an NLP toolkit for processing and analysing Icelandic texts (Loftsson and Rögnvaldsson, 2007b). A decade ago, the Icelandic language could have been categorised as a less-resourced language. The current situation, however, is much better thanks to the development of IceNLP and various linguistic resources (Rögnvaldsson et al., 2009). On the other hand, no large parallel corpus, in which Icelandic is one of the languages, is freely available. This is the main reason why the work described here was initiated. Our system, Apertium-IceNLP, is the first system in which the whole morphological and tagging component of Apertium is replaced by modules from an external system. Our motivation for developing such a hybrid system was to be able to answer the following research question: Is the translation quality of an is-en shallow-transfer MT system higher when using state-of-the-art Ice-landic NLP modules in the Apertium pipeline as opposed to relying solely on Apertium modules? Evaluation results show that the word error rate (WER) of our prototype is 50.6% and the positionindependent word error rate (PER) is 40.8% 1 . This is higher than the evaluation results of two publicly available MT systems for is-en translation, Google Translate 2 and Tungutorg 3 . This was expected, given the short development time of our system, i.e. 8 man-months. For comparison, we know that Tungutorg has been developed by an individual, Stefán Briem, intermittently over a period of two decades 4 . Contrary to our expectations, the error rates of our hybrid system is also higher than the error rates of an is-en system based solely on Apertium modules. This "pure" Apertium version was developed in parallel with Apertium-IceNLP. Based on our error analysis, we conclude that better translation quality may be achieved by replacing only the tagging component of Apertium with the corresponding module in IceNLP, but leaving morphological analysis to Apertium. We think that our work can be viewed as a guideline for other researchers wanting to develop hybrid MT systems based on Apertium. Apertium The Apertium shallow-transfer MT platform was originally aimed at the Romance languages of the Iberian peninsula, but has also been adapted for other languages, e.g. Welsh (Tyers and Donnelly, 2009) and Scandinavian languages (Nordfalk, 2009). The whole platform, both programs and data, is free and open source and all the software and data for the supported language pairs is available for download from the project website 5 . The Apertium platform consists of the following main modules: • A morphological analyser: Performs tokenisation and morphological analysis which for a given surface form returns all of the possible lexical forms (analyses) of the word. • A part-of-speech (PoS) tagger: The HMMbased PoS tagger, given a sequence of morphologically analysed words, chooses the most likely sequence of PoS tags. • Lexical selection: A lexical selection module based on Constraint Grammar (Karlsson et al., 1995) selects between possible translations of a word based on sentence context. • Lexical transfer: For an unambiguous lexical form in the SL, this module returns the equivalent TL form based on a bilingual dictionary. • Structural transfer: Performs local morphological and syntactic changes to convert the SL into the TL. • A morphological generator: For a given TL lexical form, this module returns the TL surface form. Language pair specifics For each language pair, the Apertium platform needs a monolingual SL dictionary used by the morphological analyser, a bilingual SL-TL dictionary used by the lexical transfer module, a monolingual TL dictionary used by the morphological generator, and transfer rules used by the structural transfer module. The dictionaries and transfer rules specific to the is-en pair will be discussed in Sections 4.2 and 4.3, respectively. The lexical selection module is a new module in the Apertium platform and the is-en pair is the first released pair to make extensive use of it. The module works by selecting a translation based on sentence context. For example, for the ambiguous word bóndi 'farmer' or 'husband', the default translation is left as 'farmer', but a lexical selection rule chooses the translation of 'husband' if a possessive pronoun is modifying it. While the current lexical selection rules have been written by hand, work is ongoing to generate them automatically with machine learning techniques. IceNLP IceNLP is an open source 6 NLP toolkit for processing and analysing Icelandic texts. Currently, the main modules of IceNLP are the following: • A tokeniser. This module performs both word tokenisation and sentence segmentation. • IceMorphy: A morphological analyser (Loftsson, 2008). The program provides the tag profile (the ambiguity class) for known words by looking up words in its dictionary. The dictionary is derived from the Icelandic Frequency Dictionary (IFD) corpus (Pind et al., 1991). The tag profile for unknown words, i.e. words not known to the dictionary, is guessed by applying rules based on morphological suffixes and endings. IceMorphy does not generate word forms, it only carries out analysis. • IceTagger: A linguistic rule-based PoS tagger (Loftsson, 2008). The tagger produces disambiguated morphosyntactic tags from the tagset of the IFD corpus. The tagger uses Ice-Morphy for morphological analysis and applies both local rules and heuristics for disambiguation. • TriTagger: A statistical PoS tagger. This trigram tagger is a re-implemenation of the well-known HMM tagger described by Brants (2000). It is trained on the IFD corpus. • Lemmald: A lemmatiser (Ingason et al., 2008). The method used combines a datadriven method with linguistic knowledge to maximise accuracy. • IceParser: A shallow parser (Loftsson and Rögnvaldsson, 2007a). The parser marks both constituent structure and syntactic functions using a cascade of finite-state transducers. The tagset and the tagging accuracy The IFD corpus consists of about 600,000 tokens and the tagset of about 700 tags. In this tagset, each character in a tag has a particular function. The first character denotes the word class. For each word class there is a predefined number of additional characters (at most six), which describe morphological features, like gender, number and case for nouns; degree and declension for adjectives; voice, mood and tense for verbs, etc. To illustrate, consider the Icelandic word strákarnir 'the boys'. The corresponding IFD tag is nkfng, denoting noun (n), masculine (k), plural (f ), nominative (n), and suffixed definite article (g). Previous work on PoS tagging Icelandic text (Helgadóttir, 2005;Loftsson, 2008;Dredze and Wallenberg, 2008;Loftsson et al., 2009) has shown that the morphological complexity of the Icelandic language, and the relatively small training corpus in relation to the size of the tagset, is to blame for a rather low tagging accuracy (compared to related languages). Taggers that are purely based on machine learning (including HMM trigram taggers) have not been able to produce high accuracy when tagging Icelandic text (with the exception of Dredze and Wallenberg (2008)). The current state-of-the-art tagging accuracy of 92.5% is obtained by applying a hybrid approach, integrating TriTagger into IceTagger ). Apertium-IceNLP We decided to experiment with using IceMorphy, Lemmald, IceTagger and IceParser in the Apertium pipeline. Note that since Apertium is based on a collection of modules that are connected by clean interfaces in a pipeline (following the Unix philosophy (Forcada et al., 2009)) it is relatively easy to replace modules or add new ones. Figure 1 shows the Apertium-IceNLP pipeline. Our motivation for using the above modules is the following: 1. Developing a good morphological analyser for a language is a time-consuming task 7 . Since our system is unidirectional, i.e. is-en but not en-is, we only need to be able to analyse an Icelandic surface form, but do not need to generate an Icelandic surface form from a lexical form (lemma and morphosyntactic tags). We can thus rely on IceMorphy for morphological analysis. 2. As discussed in Section 3.1, research has shown that HMM taggers, like the one included in Apertium, have not been able to achieve high accuracy when tagging Icelandic. Thus, it seems logical to use the stateof-the-art tagger, IceTagger, instead. 3. Morphological analysers in Apertium return a lemma in addition to morphosyntactic tags. To produce a lemma for each word, we can instead rely on the Icelandic lemmatiser, Lemmald. 4. Information about syntactic functions can be of help in the translation process. IceParser, which provides this information, can therefore potentially be used (we have not yet added IceParser to the pipeline). IceNLP enhancements In order to use modules from IceNLP in the Apertium pipeline, various enhancements needed to be carried out in IceNLP. Mappings Various mappings from the output generated by IceTagger to the format expected by the Apertium modules were necessary. All mappings were implemented by a single mapping file with different sections for different purposes. For example, morphosyntactic tags produced by IceTagger needed to be mapped to the tags used by Apertium. The mapping file thus contains entries for each possible tag from the IFD tagset and the corresponding Apertium tags. For example, the following entry in the mapping file shows the mapping for the IFD tag nkfng (see Section 3.1). [TAGMAPPING] ... nkfng <n><m><pl><nom><def> The string "[TAGMAPPING]" above is a section name, whereas <n> stands for noun, <m> for masculine, <pl> for plural, <nom> for nominative, and <def> for definite. Another example of a necessary mapping regards exceptions to tag mappings for particular lemmata. The following entries show that after tag mapping, the tags <vblex><actv> (verb, active voice) for the lemmata vera 'to be' and hafa 'to have' should be replaced by the single tag <vbser> and <vbhaver>, respectively. The reason is that Apertium needs specific tags for these verbs. [LEMMA] ... vera <vblex><actv> <vbser> hafa <vblex><actv> <vbhaver> The last example of a mapping concerns multiword expressions (MWEs). IceTagger tags each word of a MWE, whereas Apertium handles them as a single unit because MWEs cannot be translated word-for-word. Therefore, MWEs need to be listed in the mapping file along with the corresponding Apertium tags. The following entries show two MWEs, að einhverju leyti 'to some extent' and af hverju 'why' along with the corresponding Apertium tags. [MWE] ... að_einhverju_leyti <adv> af_hverju <adv><itg> Instead of producing tags for each component of a MWE, IceTagger searches for MWEs in its input text that match entries in the mapping file and produces the Apertium tag(s) for a particular MWE if a match is found. Daemonising IceNLP The versions of IceMorphy/IceTagger described in (Loftsson, 2008), and Lemmald described in (Ingason et al., 2008), were designed to tag and lemmatise large amounts of Icelandic text, e.g. corpora. When IceTagger starts up, it creates an instance of IceMorphy which in turn loads various dictionaries into memory. Similarily, when Lemmald starts up, it loads its rules into memory. This behaviour is fine when tagging and lemmatising corpora, because, in that case, the startup time is relatively small compared to the time needed to tag and lemmatise. On the other hand, a common usage of a machine translation system is translating a small number of sentences (for example, in online MT services) as opposed to a corpus. Using the modules from IceNLP unmodified as part of the Apertium pipeline would be inefficient in that case because the aforementioned dictionaries and rules would be reloaded every time the language pair is used. Therefore, we added a client-server functionality to IceNLP in order for it to run efficiently as part of the Apertium pipeline. We added two new applications to IceNLP: IceNLPServer and IceNLPClient. IceNLPServer is a server application, which contains an instance of the IceNLP toolkit. Essentially, IceNLPServer is a daemon which runs in the background. When it is started up, all necessary dictionaries and rules are loaded into memory and are kept there while the daemon is running. Therefore, the daemon can serve requests to the modules in IceNLP without any loading delay. IceNLPClient is a console-based client for communicating with IceNLPServer. This application behaves in the same manner as the Apertium modules, i.e. it reads from standard input and writes to standard output. Thus, we have replaced the Apertium tokeniser/morphological analyser/lemmatiser and the PoS tagger with IceNLPClient. The client returns a PoS-tagged version of its input string. To illustrate, when the client is asked to analyse the string Hún er góð 'She is good': echo "Hún er góð" | RunClient.sh it returns: Hún/hún<prn><p3><f><sg><nom>$ er/vera<vbser><pri><p3><sg>$ góð/góður<adj><pst><f><sg><nom><sta>$ This output is consistent with the output generated by the Apertium tagger, i.e. for each word of an input sentence, the lexeme is followed by the lemma followed by the (disambiguated) morphosyntactic tags. The output above is then fed directly into the remainder of the Apertium pipeline, i.e. into lexical selection, lexical transfer, structural transfer, and morphological generation (see Figure 1), to produce the English translation 'She is good'. The bilingual dictionary When this project was initiated, no is-en bilingual dictionary (bidix) was publicly available in electronic format. Our bidix was built in three stages. First, the is-en dictionary was populated with entries spidered from the Internet from Wikipedia, Wiktionary, Freelang, the Cleasby-Vigfusson Old Icelandic dictionary 8 and the Icelandic Word Bank 9 . This provided a starting point of over 5,000 entries in Apertium style XML format which needed to be checked manually for correctness. Also, since lexical selection was not an option in the early stages of the project, only one entry could be used. SL words that had multiple TL translations had to be commented out, based on which translation seemed the most likely option. For example, below we have three options for the SL word fíngerður in the bidix where it could be translated as 'fine', 'petite' or 'subtle', and the latter two options are commented out. <e><p> <l>fíngerður<s n="adj"/></l> <r>fine<s n="adj"/><s n="sint"/></r> </p></e> <!--begin comment <e><p> <l>fíngerður<s n="adj"/></l> <r>petite<s n="adj"/></r> </p></e> <e><p> <l>fíngerður<s n="adj"/></l> <r>subtle<s n="adj"/></r> </p></e> end comment --> Each entry in the bidix is surrounded by element tags <e>...</e> and paragraph tags <p>...</p>. SL words are surrounded by left tags <l>...</l> and TL translations by right tags <r>...</r>. Within the left and right tags the attribute value "adj" denotes that the word is an adjective and the presence of the attribute value "sint" denotes that the adjective's degree of comparison is shown with "-er/-est" endings (e.g. 'fine', 'finer', 'finest'). In the second stage of the bidix development, a bilingual wordlist of about 6,000 SL words with word class and gender was acquired from an individual, Anton Ingason. It required some preprocessing before it could be added to the bidix, e.g. determining which of these new SL words did not already exist in the dictionary, and selecting a default translation in cases where more than one translation was given. Last, we acquired a bilingual wordlist from the dictionary publishing company Forlagið 10 , containing an excerpt of about 18,000 SL words from their Icelandic-English online dictionary. This required similar preprocessing work as described above. Currently, our is-en bidix contains 21,717 SL lemmata and 1,491 additional translations to be used for lexical selection. Transfer rules The syntactic (structural) transfer stage (see Figure 1) in the translator is split into four stages. The first stage (chunker) performs local reordering and chunking. The second (interchunk1) produces chunks of chunks, e.g. chunking relative clauses into noun phrases. The third (interchunk2) performs longer distance reordering, e.g. constituent reordering, and some tense changes. As an example of a tense change, consider: Hann vildi að verðlaunin faeru til þeirra → 'He wanted that the awards went to them' → 'He wanted the awards to go to them'. Finally, the fourth stage (postchunk) does some cleanup operations, and insertion of the indefinite article. There are 78 rules in the first stage, the majority dealing with noun phrases, 3 rules in the second, 26 rules in the third stage and 5 rules in the fourth stage. It is worth noting that the development of the bilingual dictionary and the transfer rules benefit both the Apertium-IceNLP system and the is-en system based solely on Apertium modules. Evaluation Our goal was to evaluate approximately 5,000 words, which corresponds to roughly 10 pages of text, and compare our results to two other publicly available is-en MT systems: Google Translate, an SMT system, and Tungutorg, a proprietary rulebased MT system, developed by an individual. In addition, we sought a comparison to the is-en system based solely on Apertium modules. The test corpus for the evaluation was extracted from a dump of the Icelandic Wikipedia on April 24th 2010, which provided 187,906 lines of SL text. The reason for choosing texts from Wikipedia is that the evaluation material can be distributed, which is not the case for other available corpora of Icelandic. Then, 1,000 lines were randomly selected from the test corpus and the resulting file filtered semiautomatically such that: i) each line had only one complete sentence; ii) each sentence had more than three words; iii) each sentence had zero or one lower case unknown word (we want to test the transfer, not the coverage of the dictionaries); iv) lines that were clearly metadata and traceable to individuals were removed, e.g. user names; v) lines that contained incoherent strings of numbers were removed, e.g. from a table entry; vi) lines containing non-Latin alphabet characters were removed, e.g. if they contained Greek or Arabic font; vii) lines that contained extremely domain specific Table 1: Word error rate (WER) and position-independent word error rate (PER) over the test sentences for the publicly available is-en machine translation systems. and/or archaic words were removed (e.g. words that our human translator did not know how to translate); and viii) repetitive lines, e.g. multiple lines of the same format from a list, were removed. After this filtering process, 397 sentences remained which were then run through the four MT systems. In order to calculate evaluation metrics (see below), each of the four output files had to be post-edited. A bilingual human posteditor reviewed each TL sentence, copied it and then made minimal corrections to the copied sentence so that it would be suitable for dissemination -meaning that the sentence needs to be as close to grammatically correct as possible so that post-editing requires less effort. The translation quality was measured using two metrics: word error rate (WER), and positionindependent word error rate (PER). The WER is the percentage of the TL words that require correction, i.e. substitutions, deletions and insertions. PER is similar to WER except that PER does not penalise correct words in incorrect positions. Both metrics are based on the well known Levenshtein distance and were calculated for each of the sentences using the apertium-eval-translator tool 11 . Metrics based on word error rate were chosen so as to be able to compare the system against other Apertium systems and to assess the usefulness of the system in real settings, i.e. of translating for dissemination. Note that, in our case, the WER and PER scores are computed based on the difference between the system output and a post-edited version of the system output. As can be seen in Table 1, the WER and PER for our Apertium-IceNLP prototype is 50.6% and 40.8%, respectively. This may seem quite high, but looking at the translation quality statistics for some of the other language pairs in Apertium 12 , we see that the WER for Norsk Bokmål-Nynorsk is 17.7%, for Swedish-Danish 30.3%, for Breton-French 38.0%, for Welsh-English 55.7%, and for Basque-Spanish 72.4%. It is worth noting however that each of these evaluations had slightly different requirements for source language sentences. For instance, the Swedish-Danish pair allowed any number of unknown words. We expected that the translation quality of Apertium-IceNLP would be significantly less than both Google Translate and Tungutorg, and the results in Table 1 confirm this expectation. The reason for our expectation was that the development time of our system was relatively short (8 man-months), whereas Tungutorg, for example, has been developed intermittently over a period of two decades. Unexpectedly, the error rates of Apertium-IceNLP is also higher than the error rates of a system based solely on Apertium modules (see row "Apertium" in Table 1). We will discuss reasons for this and future work to improve the translation quality in the next section. Discussion and future work In order to determine where to concentrate efforts towards improving the translation quality of Apertium-IceNLP, some error analysis was carried out on a development data set. This development data was collected from the largest Icelandic online newspaper mbl.is into 1728 SL files and then translated by the system into TL files. Subsequently, 50 files from the pool were randomly selected for manual review and categorisation of errors. The error categories were created along the way, resulting in a total of 6 error categories to identify where it would be most beneficial to make improvements. Analysis of the error categories showed that 60.7% of the errors were due to words missing from the bidix, mostly proper nouns and compound words (see Table 2). This analysis suggests that improvement to the translation quality can be achieved by concentrating on adding proper nouns to the bidix, on the one hand, and resolving compound words, on the other. One possible explanation for the lower error rates for the "pure" Apertium version than the Apertium-IceNLP system is the handling of MWEs. MWEs most often do not translate literally nor even to the same number of words, which can dramatically increase the error rate. The pure version translates unlimited lengths of MWEs as single units and can deal with MWEs that contain inflectional words. In contrast, the length of the MWEs in IceNLP (and consequently also in Apertium-IceNLP) is limited to trigrams and, furthermore, IceNLP cannot deal with inflectional MWEs. The additional work required to get a better translation quality out of the Apertium-IceNLP system than a pure Apertium system raises the question as to whether "less is more", i.e. whether instead of incorporating tokenisation, morphological analysis, lemmatisation and PoS tagging from IceNLP into the Apertium pipeline, it may produce better results to only use IceTagger for PoS tagging but rely on Apertium for the other tasks. As discussed in Section 3.1, IceTagger outperforms an HMM tagger as the one used by the Apertium pipeline. In order to replace only the PoS tagger in the Apertium pipeline, some modifications will have to be made to IceTagger. In addition to the modifications already carried out to make IceTagger return output in Apertium style format (see Section 4.1.1), the tagger will also have to be able to take Apertium style formatted input. More specifically, instead of relying on IceMorphy and Lemmald for morphological analysis and lemmatisation, IceTagger would have to be changed to receive the necessary information from the morphological component of Apertium. Conclusion We have described the development of Apertium-IceNLP, an Icelandic→English (is-en) MT system based on the Apertium platform and IceNLP, an NLP toolkit for Icelandic. Apertium-IceNLP is a hybrid system, the first system in which the whole morphological and tagging component of Aper-tium is replaced by modules from an external system. Our system is a prototype with about 8 manmonths of development work. Evaluation, based on word error rate, shows that our prototype does not perform as well as two other available is-en systems, Google Translate and Tungutorg. This was expected and can mainly be explained by two factors. First, our system has been developed over a short time. Second, our system makes systematic errors that we intend to fix in future work. Contrary to our expectations, the Apertium-IceNLP system also performs worse than the isen system based solely on Apertium modules. We conjectured that this is mainly due to the fact that the Apertium-IceNLP system does not handle MWEs adequately, whereas the handling of MWEs is an integrated part of the Apertium morphological analyser. Therefore, we expect that better translation quality may be achieved by replacing only the tagging component of Apertium with the corresponding module in IceNLP, but leaving morphological analysis to Apertium. This conjecture will be verified in future work. Figure 1 : 1The Apertium-IceNLP pipeline. Table 2 : 2Error categories and corresponding frequencies. See explanations of WER and PER in Section 5. 2 http://translate.google.com 3 http://www.tungutorg.is/ 4 We do not have information on development months for the is-en part of Google Translate. 5 http://www.apertium.org http://icenlp.sourceforge.net Although there exists a morphological database for Icelandic (http://bin.arnastofnun.is), it is unfortunately not available as free/open source software/data. http://www.ling.upenn.edu/~kurisuto/ germanic/oi_cleasbyvigfusson_about.html 9 http://www.ismal.hi.is/ob/index.en.html http://snara.is/ http://wiki.apertium.org/wiki/ Evaluation 12 http://wiki.apertium.org/wiki/ AcknowledgmentsThe work described in this paper has been supported by: i) The Icelandic Research Fund, project "Viable Language Technology beyond English -Icelandic as a test case", grant no. 090662012; and ii) The NILS mobility project (The Abel Predoc Research Grant), coordinated by Universidad Complutense de Madrid. TnT: A statistical part-ofspeech tagger. Thorsten Brants, Proceedings of the 6 th Conference on Applied Natural Language Processing. the 6 th Conference on Applied Natural Language ProcessingSeattle, WA, USABrants, Thorsten. 2000. TnT: A statistical part-of- speech tagger. In Proceedings of the 6 th Conference on Applied Natural Language Processing, Seattle, WA, USA. Icelandic Data Driven Part of Speech Tagging. Mark Dredze, Joel Wallenberg, Proceedings of the 46 th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. the 46 th Annual Meeting of the Association for Computational Linguistics: Human Language TechnologiesColumbus, OH, USADredze, Mark and Joel Wallenberg. 2008. Icelandic Data Driven Part of Speech Tagging. In Proceedings of the 46 th Annual Meeting of the Association for Computational Linguistics: Human Language Tech- nologies, Columbus, OH, USA. The Apertium machine translation platform: Five years on. 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Anton K Ingason, Sigrún Helgadóttir, Advances in Natural Language Processing, 6 th International Conference on NLP. Nordström, B. and A. RanteGothenburgHrafn Loftsson, and Eiríkur Rögnvaldsson. SwedenIngason, Anton K., Sigrún Helgadóttir, Hrafn Lofts- son, and Eiríkur Rögnvaldsson. 2008. A Mixed Method Lemmatization Algorithm Using Hierachy of Linguistic Identities (HOLI). In Nordström, B. and A. Rante, editors, Advances in Natural Lan- guage Processing, 6 th International Conference on NLP, GoTAL 2008, Proceedings, Gothenburg, Swe- den. Constraint Grammar: A Language-Independent System for Parsing Unrestricted Text. Fred Karlsson, Atro Voutilainen, Juha Heikkilä, Arto Anttila, Mouton de GruyterBerlinKarlsson, Fred, Atro Voutilainen, Juha Heikkilä, and Arto Anttila. 1995. Constraint Grammar: A Language-Independent System for Parsing Unre- stricted Text. Mouton de Gruyter, Berlin. IceParser: An Incremental Finite-State Parser for Icelandic. Hrafn Loftsson, Eiríkur Rögnvaldsson, Proceedings of the 16 th Nordic Conference of Computational Linguistics. the 16 th Nordic Conference of Computational LinguisticsTartu, EstoniaLoftsson, Hrafn and Eiríkur Rögnvaldsson. 2007a. IceParser: An Incremental Finite-State Parser for Icelandic. In Proceedings of the 16 th Nordic Con- ference of Computational Linguistics (NoDaLiDa 2007), Tartu, Estonia. IceNLP: A Natural Language Processing Toolkit for Icelandic. Hrafn Loftsson, Eiríkur Rögnvaldsson, Proceedings of Interspeech. InterspeechAntwerp, BelgiumSpeech and language technology for less-resourced languagesLoftsson, Hrafn and Eiríkur Rögnvaldsson. 2007b. IceNLP: A Natural Language Processing Toolkit for Icelandic. In Proceedings of Interspeech 2007, Spe- cial Session: "Speech and language technology for less-resourced languages", Antwerp, Belgium. Improving the PoS tagging accuracy of Icelandic text. Hrafn Loftsson, Ida Kramarczyk, Proceedings of the 17 th Nordic Conference of Computational Linguistics. the 17 th Nordic Conference of Computational LinguisticsOdense, DenmarkSigrún Helgadóttir, and Eiríkur RögnvaldssonLoftsson, Hrafn, Ida Kramarczyk, Sigrún Helgadóttir, and Eiríkur Rögnvaldsson. 2009. Improving the PoS tagging accuracy of Icelandic text. In Proceedings of the 17 th Nordic Conference of Computational Lin- guistics (NoDaLiDa 2009), Odense, Denmark. Tagging Icelandic text: A linguistic rule-based approach. Hrafn Loftsson, Nordic Journal of Linguistics. 311Loftsson, Hrafn. 2008. Tagging Icelandic text: A lin- guistic rule-based approach. Nordic Journal of Lin- guistics, 31(1):47-72. Shallow-transfer rule-based machine translation for Swedish to Danish. 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A Natural Language Instructor for pedestrian navigation based in generation by selection

In this paper we describe a method for developing a virtual instructor for pedestrian navigation based on real interactions between a human instructor and a human pedestrian. A virtual instructor is an agent capable of fulfilling the role of a human instructor, and its goal is to assist a pedestrian in the accomplishment of different tasks within the context of a real city.The instructor decides what to say using a generation by selection algorithm, based on a corpus of real interactions generated within the world of interest. The instructor is able to react to different requests by the pedestrian. It is also aware of the pedestrian position with a certain degree of uncertainty, and it can use different city landmarks to guide him.

A Natural Language Instructor for pedestrian navigation based in generation by selection Association for Computational LinguisticsCopyright Association for Computational LinguisticsApril 26-30 2014. 2014 Santiago Avalos santiagoe.avalos@gmail.com LIIS Group FaMAF Universidad Nacional de Córdoba Córdoba Argentina Luciana Benotti luciana.benotti@gmail.com LIIS Group, FaMAF Universidad Nacional de Córdoba Córdoba Argentina A Natural Language Instructor for pedestrian navigation based in generation by selection Proceedings of the of the EACL 2014 Workshop on Dialogue in Motion (DM) the of the EACL 2014 Workshop on Dialogue in Motion (DM)Gothenburg, SwedenAssociation for Computational LinguisticsApril 26-30 2014. 2014 In this paper we describe a method for developing a virtual instructor for pedestrian navigation based on real interactions between a human instructor and a human pedestrian. A virtual instructor is an agent capable of fulfilling the role of a human instructor, and its goal is to assist a pedestrian in the accomplishment of different tasks within the context of a real city.The instructor decides what to say using a generation by selection algorithm, based on a corpus of real interactions generated within the world of interest. The instructor is able to react to different requests by the pedestrian. It is also aware of the pedestrian position with a certain degree of uncertainty, and it can use different city landmarks to guide him. Introduction and previous work Virtual instructors are conversational agents that help a user perform a task. These agents can be useful for many purposes, such as language learning (Nunan, 2004), training in simulated environments (Kim et al., 2009) and entertainment (Dignum, 2012;Jan et al., 2009). Navigation agents generate verbal route directions for users to go from point A to point B in a given world. The wide variety of techniques to accomplish this task, range from giving complete route directions (all route information in a single instruction), to full interactive dialogue systems which give incremental instructions based on the position of the pedestrian. Although it can recognize pre-established written requests, the instructor presented in this work is not able to interpret utterances from the pedestrian, leaving it unable to generate a full dialogue. The instructor's decisions are based on the pedestrian actual task, his position in the world, and the previous behavior from different human instructors. In order to guide a user while performing a task, an effective instructor must know how to describe what needs to be done in a way that accounts for the nuances of the virtual world and that is enough to engage the trainee or gamer in the activity. There are two main approaches toward automatically producing instructions. One is the selection approach, in which the task is to pick the appropriate output from a corpus of possible outputs. The other is the composition approach, in which the output is dynamically assembled using some composition procedure, e.g. grammar rules. The natural language generation algorithm used in this work is a modified version of the generation by selection method described in (Benotti and Denis, 2011). The advantages of generation by selection are many: it affords the use of complex and humanlike sentences, the system is not bound to use written instructions (it may easily use recorded audio clips, for example), and finally, no rule writing by a dialogue expert or manual annotations is needed. The disadvantage of generation by selection is that the resulting dialogue may not be fully coherent (Shawar and Atwell, 2003;Shawar and Atwell, 2005;Gandhe and Traum, 2007). In previous work, the selection approach to generation has been used in non task-oriented conversational agents such as negotiating agents (Gandhe and Traum, 2007), question answering characters (Leuski et al., 2006) and virtual patients (Kenny et al., 2007). In the work presented in this paper, the conversational agent is task-oriented. In Section 2 we introduce the framework used in the interaction between the navigation agent and the human pedestrians. We discuss the creation of the human interaction corpus and the method for natural language generation in Section 3; And in Section 4 we explain the evaluation methods and the expected results. The GRUVE framework One of the major problems in developing systems that generate navigation instructions for pedestrians is evaluating them with real users in the real world. This evaluations are expensive, time consuming, and need to be carried out not just at the end of the project but also during the development cycle. Consequently, there is a need for a common platform to effectively compare the performances of several verbal navigation systems developed by different teams using a variety of techniques. The GIVE challenge developed a 3D virtual indoor environment for development and evaluation of indoor pedestrian navigation instruction systems . In this framework, users walk through a building with rooms and corridors, and interact with the world by pressing buttons. The user is guided by a navigation system that generates route instructions. The GRUVE framework presented in (Janarthanam et al., 2012) is a web-based environment containing a simulated real world in which users can simulate walking on the streets of real cities whilst interacting with different navigation systems. This system focus on providing a simulated environment where people can look at landmarks and navigate based on spatial and visual instructions provided to them. GRUVE also provides a embedded navigation agent, the Buddy System, which can be used to test the framework. Apart from the virtual environment in which they are based an important difference between GIVE and GRUVE is that, in GRUVE, there is a certain degree of uncertainty about the position of the user. GRUVE presents navigation tasks in a gameworld overlaid on top of the simulated real world. The main task consists of a treasure hunting similar to the one presented in GIVE. In our work, we use a modified version of the original framework, in which the main task has been replaced by a set of navigation tasks. The web-client (see Figure 1) includes an interaction panel that lets the user interact with his navigation system. In addition to user location information, users can also interact with the navigation system using a fixed set or written utterances. The interaction panel provided to the user consists of a GUI panel with buttons and drop-lists which can be used to construct and send requests to the system in form of abstract semantic representations (dialogue actions). The virtual instructor The virtual instructor is a natural language agent that must help users reach a desired destination within the virtual world. Our method for developing an instructor consists of two phases: an annotation phase and a selection phase. In Section 3.1 we describe the annotation phase. This is performed only once, when the instructor is created, and it consists of automatically generating a corpus formed by associations between each instruction and the reaction to it. In Section 3.2 we describe how the utterance selection is performed every time the virtual instructor generates an instruction. Annotation As described in (Benotti and Denis, 2011), the corpus consists in recorded interactions between two people in two different roles: the Direction Giver (DG), who has knowledge of how to perform the task, and creates the instructions, and the Direction Follower (DF), who travels through the environment following those instructions. The representation of the virtual world is given by a graph of nodes, each one representing an intersection between two streets in the city. GRUVE provides a planner that can calculate the optimal path from any starting point to a selected destination (this plan consists in the list of nodes the user must travel to reach the desired destination). As the DF user walks through the environment, he cannot change the world that surrounds him. This simplifies the automatic annotation process, and the logged atoms are: • user position: latitude and longitude, indicating position relative to the world. • user orientation: angle between 0-360, indicating rotation of the point of view. In order to define the reaction associated to each utterance, it is enough to consider the position to which the user arrives after an instruction has been given, and before another one is requested. Nine destinations within the city of Edinburgh were selected to be the tasks to complete (the task is to arrive to each destination, from a common starting point, see Figure 2). Each pair of DG and DF had to complete all tasks and record their progress. For the creation of the corpus, a slightly modified version of the GRUVE wizards-desk was used. This tool is connected to the GRUVE webclient, and allows a human user to act as DF, generating instructions to assist the user in the completion of the task and monitoring his progression. Each instruction generated by a DG was numbered in order, in relation to each task. For example: if the fifth instruction given by the third DG, while performing the second task, was "Go forward and cross the square", then that instruction was numbered as follows: 5.3.2 − "Go f orward and cross the square". This notation was included to maintain the generation order between instructions (as the tasks were given in an arbitrary specific order for each DG). With last-generated, we refer to the instructions that were generated in the last 3 runs of each DG. This notion is needed to evaluate the effect of the increasing knowledge of the city (this metric is explained in Section 4). As discussed in (Benotti and Denis, 2011) misinterpreted instructions and corrections result in clearly inappropriate instruction-reaction associations. Since we want to avoid any manual annotation, but we also want to minimize the quantity of errors inside the corpus, we decided to create a first corpus in which the same person portraits the roles of DG and DF. This allows us to eliminate the ambiguity of the instruction interpretation on the DF side, and eliminates correction instructions (instructions that are of no use for guidance, but were made to correct a previous error from the DG, or a wrong action from the DF). Later on, each instruction in this corpus was performed upon the virtual world by various others users, their reactions compared to the original reaction, and scored. For each task, only the instructions whose score exceeded an acceptance threshold remained in the final corpus. Instruction selection The instruction selection algorithm, displayed in Algorithm 1 consists in finding in the corpus the set of candidate utterances C for the current task plan P, which is the sequence of actions that needs to be executed in the current state of the virtual world in order to complete the task. We use the planner included in GRUVE to create P. We define: C = {U ∈ Corpus | P starts with U.Reaction} In other words, an utterance U belongs to C if the first action of the current plan P exactly matches the reaction associated to the utterance U. Whenever the plan P changes, as a result of the actions of the DF, we call the selection algorithm in order to regenerate the set of candidate utterances C. Algorithm 1 Selection Algorithm C ← ∅ action ← nextAction(currentObjective) for all U tterance U ∈ Corpus do if action = U.Reaction then C ← C ∪ U end if end for All the utterances that pass this test are considered paraphrases and hence suitable in the current context. Given a set of candidate paraphrases, one has to consider two cases: the most frequent case when there are several candidates and the possible case when there is no candidate. • No candidate available: If no instruction is selected because the current plan cannot be matched with any existing reaction, a default, neutral, instruction "go" is uttered. • Multiple candidates available: When multiple paraphrases are available, the agent must select one to transmit to the user. In this case, the algorithm selects one from the set of the last-generated instructions for the task (see Section 3.1). Evaluation and expected results Is this section we present the metrics and evaluation process that will be performed to test the virtual instructor presented in Section 3, which was generated using the dialogue model algorithm introduced in Section 3.2. Objective metrics The objective metrics are summarized below: • Task success: successful runs. • Canceled: runs not finished. • Lost: runs finished but failed. • Time (sec): average for successful runs. • Utterances: average per successful run. With this metrics, we will compare 3 systems: agents A, B and C. Agent A is the GRUVE buddy system, which is provider by the GRUVE Challenge organizers as a baseline. Agent B consists of our virtual instructor, configured to select a random instruction when presented with multiple candidates (see Section 3.1). Agent C is also our virtual instructor, but when presented with several candidates, C selects a candidate who is also part of the last-generated set. As each task was completed in different order by each DG when the corpus was created, it is expected that in every set of candidates, the most late-generated instructions were created with greater knowledge of the city. Subjective metrics The subjective measures will be obtained from responses to a questionnaire given to each user at the end of the evaluation, based partially on the GIVE-2 Challenge questionnaire (Koller et al., 2010). It ask users to rate different statements about the system using a 0 to 10 scale. The questionnaire will include 19 subjective metrics presented below: Q1: The system used words and phrases that were easy to understand. Q2: I had to re-read instructions to understand what I needed to do. Q3: The system gave me useful feedback about my progress. Q4: I was confused about what to do next. Q5: I was confused about which direction to go in. Q6: I had no difficulty with identifying the objects the system described for me. Q7: The system gave me a lot of unnecessary Information. Q8: The system gave me too much information all at once. Q9: The system immediately offered help when I was in trouble. Q10: The system sent instructions too late. Q11: The systems instructions were delivered too early. Q12: The systems instructions were clearly worded. Q13: The systems instructions sounded robotic. Q14: The systems instructions were repetitive. Q15: I lost track of time while solving the overall task. Q16: I enjoyed solving the overall task. Q17: Interacting with the system was really annoying. Q18: The system was very friendly. Q19: I felt I could trust the systems instructions. Metrics Q1 to Q12 assess the effectiveness and reliability of instructions, while metrics Q13 to Q19 are intended to assess the naturalness of the instructions, as well as the immersion and engagement of the interaction. Expected results Based on the results obtained by (Benotti and Denis, 2011) in the GIVE-2 Challenge, we expect a good rate of successful runs for the agent. Furthermore, the most interesting part of the evaluation resides in the comparison between agents B and C. 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An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of Gotthold Ephraim Lessing

We present results from a project on sentiment analysis of drama texts, more concretely the plays of Gotthold Ephraim Lessing. We conducted an annotation study to create a gold standard for a systematic evaluation. The gold standard consists of 200 speeches of Lessing's plays and was manually annotated with sentiment information by five annotators. We use the gold standard data to evaluate the performance of different German sentiment lexicons and processing configurations like lemmatization, the extension of lexicons with historical linguistic variants, and stop words elimination, to explore the influence of these parameters and to find best practices for our domain of application. The best performing configuration accomplishes an accuracy of 70%. We discuss the problems and challenges for sentiment analysis in this area and describe our next steps toward further research.

An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of Gotthold Ephraim Lessing August 25. 2018 Thomas Schmidt thomas.schmidt@ur.de Media Informatics Group Computational Humanities Group Leipzig University Regensburg University 93040, 04109Regensburg, LeipzigGermany, Germany Manuel Burghardt Media Informatics Group Computational Humanities Group Leipzig University Regensburg University 93040, 04109Regensburg, LeipzigGermany, Germany An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of Gotthold Ephraim Lessing Proceedings of Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and LiteratureSanta Fe, New Mexico, USAAugust 25. 2018139 We present results from a project on sentiment analysis of drama texts, more concretely the plays of Gotthold Ephraim Lessing. We conducted an annotation study to create a gold standard for a systematic evaluation. The gold standard consists of 200 speeches of Lessing's plays and was manually annotated with sentiment information by five annotators. We use the gold standard data to evaluate the performance of different German sentiment lexicons and processing configurations like lemmatization, the extension of lexicons with historical linguistic variants, and stop words elimination, to explore the influence of these parameters and to find best practices for our domain of application. The best performing configuration accomplishes an accuracy of 70%. We discuss the problems and challenges for sentiment analysis in this area and describe our next steps toward further research. Introduction As drama provides a number of structural features, such as speakers, acts or stage directions, it can be considered a literary genre that is particularly convenient and accessible for computational approaches. Accordingly, we find a number of quantitative approaches for the analysis of drama in general (cf. Fucks and Lauter, 1965;Solomon, 1971;Ilsemann;2008), but also with a focus on the analysis of emotion (Mohammad, 2011) and sentiment (Nalisnick and Baird, 2013). More concretely, Mohammad (2011) uses the NRC Emotion Lexicon (Mohammad and Turney, 2010) to analyze the distribution and the progression of eight basic emotions in a selection of Shakespeare's plays. Nalisnick and Baird (2013) focus on speaker relations to analyze sentiment in Shakespeare's plays. The goal of our study is to extend these existing approaches to computational sentiment analysis by taking into account historic, German plays. Further, we address some of the limitations of the current research on sentiment analysis in drama (Mohammad, 2011;Nalisnick and Baird, 2013), e.g. the ad hoc usage of sentiment lexicons without any pre-processing steps or other adjustments (Mohammad, 2011;Nalisnick and Baird, 2013). Our main contribution to the field of sentiment analysis for drama is the systematic evaluation of lexicon-based sentiment analysis techniques for the works of the German playwright Gotthold Ephraim Lessing. The evaluation takes into account a number of existing sentiment lexicons for contemporary German language (Võ et al., 2009;Clematide and Klenner, 2010;Mohammad and Turney, 2010;Remus et al., 2010;Waltinger, 2010) and various related NLP techniques, such as German lemmatizers, stop words lists and spelling variant dictionaries. The various combinations of existing lexicons and NLP tools are evaluated against a human annotated subsample, which serves as a gold standard. Related Work: Sentiment Analysis in Literary Studies As literary scholars have been interested in the emotions and feelings expressed in narrative texts for quite some time (cf. Winko, 2003;Mellmann, 2015), it is not surprising that computational sentiment analysis techniques have found their way into the realm of literary studies, for instance with and , who examined the sentiment annotation of sentences in fairy tales. Other examples include Kakkonen and Kakkonen (2011), who used lexicon-based sentiment analysis to visualize and compare the emotions of gothic novels in a graph-based structure. Ashok et al. (2013) found a connection between the distribution of sentiment bearing words and the success of novels. Elsner (2012) included sentiment analysis to examine the plot structure in novels. In the same area, Jockers (2015) authored several blog posts about the use of sentiment analysis for the interpretation and visualization of plot arcs in novels. Reagan et al. (2016) extended Jockers work and use supervised as well as unsupervised learning to identify six core emotional arcs in fiction stories. Jannidis et al. (2016) used the results of lexicon-based sentiment analysis as features to detect "happy endings" in German novels of the 19 th century. Heuser et al. (2016) used crowdsourcing, close reading and lexicon-based sentiment analysis to connect sentiments with locations of London in 19 th century novels and visualize the information on maps of historic London. Kim et al. (2017) used lexical emotion features as part of a bigger feature set to successfully predict the genre of fiction books via machine learning. Buechel et al. (2016) identified the historical language of past centuries as a major challenge for sentiment analysis and therefore constructed a sentiment lexicon for the use case of 18 th and 19 th century German to analyze emotional trends and distributions in different genres. Evaluation Design Corpus In order to investigate the practicability of lexicon-based sentiment analysis techniques for historical German drama, we gathered an experimental corpus of twelve plays by Gotthold Ephraim Lessing, which comprises overall 8,224 speeches. The plays were written between 1747 and 1779. Eight of the dramas are attributed to the genre of comedy while three are tragedies and one is referred to as dramatic poem. The most famous plays of the corpus are "Nathan der Weise" and "Emilia Galotti". The average length of the speeches of the entire corpus is 24.15 words; the median is 13 words, which shows that the corpus consists of many rather short speeches. The longest speech consists of 775 words. Also note that the plays have very different lengths, with the shortest consisting of 183 and the longest of 1,331 speeches. All texts in our corpus are available in XML format and come with structural and speakerrelated information for the drama text 1 . Gold Standard Creation To be able to assess the quality of results from our evaluation study of lexicon-based approaches to sentiment analysis in Lessing's plays, we created a human annotated gold standard for 200 speeches. It is important to note that we were primarily interested in the overall sentiment of a self-contained character speech, as speeches are typically the smallest meaningful unit of analysis in quantitative approaches to the study of drama (cf. Ilsemann, 2008;Wilhelm, Burghardt and Wolff, 2013;Nalisnick and Baird, 2013). To create a representative sample of the 200 speeches, several characteristics of the corpus were taken into consideration: First, we only selected speeches longer than 18 words, which represents -25% of the average word length of speeches of the corpus, as we wanted to eliminate very short speeches that may contain no information at all. In related work, very short text snippets have been reported to be problematic for sentiment annotation, due to the lack of context and content Liu, 2016, p. 10). From the remaining speeches, we randomly selected speeches so that the proportion of speeches per drama in our gold standard represents the proportion per drama of the entire corpus, i.e. there are proportionally more speeches for longer dramas. We reviewed all speeches manually and replaced some speeches that consisted of French and Latin words, since those speeches might be problematic for our German speaking annotators. The final gold standard corpus had an average length of 50.68 words per speech and a median of 38 with the longest speech being 306 words long. Five annotators, all native speakers in German, annotated the 200 speeches. During the annotation process, every speech was presented to the annotators with the preceding and the subsequent speech as contextual information. For the annotation scheme, we used two different approaches, which are oriented toward similar annotation studies (Bosco et al., 2014;Saif et al., 2014;Momtazi, 2012, Takala et al., 2014. First, annotators had to assign each speech to one of six categories: very negative, negative, neutral, mixed, positive, very positive. We refer to this annotation as differentiated polarity. In a second step, participants had to choose a binary polarity: negative or positive. This means, if annotators chose neutral or mixed in the first step, they had to choose a binary polarity based on the overall tendency. With the first annotation, we wanted to gather some basic insights into sentiment distributions. However, several studies have shown that the agreement is very low for differentiated schemes like this (Momtazi, 2012;Takala et al., 2014); therefore, we also presented the binary annotation. Figure 1 illustrates the annotation scheme. Figure 1. Example annotation All five annotators had two weeks of time to conduct the entire annotation of 200 speeches, independently from each other. According to the annotators, the task took around five hours. An analysis of the differentiated annotations shows that the majority of annotations are negative or very negative (47%), while positive or very positive annotations are rather rare (16%). The results of the annotation also show that mixed (23%) and neutral (14%) annotations are a relevant annotation category as well. For the binary annotation, 67% of the annotations are negative and 33% are positive. We analyzed the agreement of the annotations with Krippendorff's α (Krippendorff, 2011) and the average percentage of agreement of all annotator pairs (APA). Table 1 summarizes the results for the agreement of both annotation types. Krippendorff Krippendorff's α and the average percentage of agreement of all annotator pairs point to a low agreement for the differentiated polarity. The degree of agreement is moderate for the binary polarity according to the interpretation of Landis and Koch (1971). Since the degree of agreement is considerably higher for the binary polarity, we only regarded the binary polarity for the construction of our gold standard corpus. We selected the polarity chosen by the majority (>=3) of the annotators as the final value in our gold standard. This approach leads to 61 speeches being annotated as positive and 139 as negative. The entire gold standard corpus with all speeches, the final annotations and all other annotation data are publicly available 2 . Parameters of Evaluation The results of automatic sentiment analysis approaches are influenced by a number of parameters. To find out which configuration of parameters yields the best results for historic plays in German language, we evaluated the following five variables: i) Sentiment lexicon A sentiment lexicon is a list of words annotated with sentiment information. These words are also referred to as sentiment bearing words (SBWs). We identified five general purpose sentiment lexicons for German and evaluated their performance: SentiWortschatz (SentiWS, Remus et al., 2010), the Berlin Affective Word List (BAWL, Võ et al., 2009), German Polarity Clues (GPC; Waltinger, 2010), the German translation of the NRC Emotion Lexicon (NRC, Mohammad and Turney, 2010) and a sentiment lexicon by Clematide and Klenner (2010), further referred to as CK. Note, that all of the sentiment lexicons have different sizes and were created in different ways. They also differ in their overall composition: Some have simple binary polarity annotations, i.e. a word is either positive or negative. We refer to this kind of annotation as dichotomous polarity. Others have additional polarity strengths, which are values on a continuous scale e.g. ranging from -1 (very negative) to +1 (very positive) (SentiWS, CK, BAWL). Most of the lexicons consist of the base forms of words (lemmas), but some are manually appended with inflections of the words (SentiWS, GPC). Besides the lexicons, we also created and evaluated a combination of all five lexicons. To do this, we simplified the basic idea of sentiment lexicon combination by Emerson and Declerk (2014), i.e. we merged all words of all lexicons. If words were annotated ambiguously, we selected the polarity annotation that occurred in the majority of lexicons. For this process, we only regarded the dichotomous polarity of the lexicons. ii) Extension with linguistic variants The development of the aforementioned lexicons is based on modern online lexicons (Võ et al., 2009), corpora of product reviews (Remus et al., 2010), news articles (Clematide and Klenner, 2010) and the usage of crowdsourcing (Mohammad and Turney, 2010). Therefore, the lexicons were rather created to be used for contemporary language than for poetic German language of the 18 th century. Some early studies in this area already identified the problem of historical language for contemporary sentiment lexicons Sprugnoli et al., 2016;Buechel et al., 2016). To examine this problem, we used a tool of the Deutsches Textarchiv (DTA) that produces historical linguistic variants of German words, e.g. different orthographical variants a word had throughout history (Jurish, 2012). The tool also provides historical inflected forms of the words. We used this tool to extend the sentiment lexicons as we gathered all historical linguistic variants for each word of every lexicon and added those words to the lexicon with the same polarity annotation of the base. This procedure increased the size of the lexicons to a large degree, since for every orthographic variant all inflections were added (example: size of BAWL before extension: 2,842; after extension: 75,436). However, one of the dangers of this approach is the possible addition of words that are not really sentiment bearing words, which may skew the polarity calculation. Further, the DTA tool has not yet been evaluated for this specific use case, so the quality of the produced variants is unclear. Hence, we evaluate the performance of the lexicons in their basic form (noExtension) as well as with the extension (dtaExtended). iii) Stop words lists We also analyzed the influence of stop words and frequently occurring words of the corpus on the sentiment calculation. Saif et al. (2014) showed that the elimination of these words can have a positive influence on performance of sentiment analysis in the machine learning context. Stop words and frequent words might skew sentiment calculation of lexicon-based methods as well, since some of the lexicons actually contain stop words. There are also some highly frequent words in our corpus that are listed in many of the lexicons as sentiment bearing words, but are actually overused because of the particular language style of the 18 th century. We use different types of stop words lists to explore the influence of those types of words: • a basic German stop word list of 462 words (upper and lower case; standardList), • the same list extended by the remaining 100 most frequent words of the entire Lessing corpus (extendedList), • and the same list manually filtered by words that are indeed very frequent, but are still sentiment bearing (e.g. Liebe/love; filteredExtendedList). Besides, we also evaluate the condition to use no stop words list at all (noStopWords). iv) Lemmatizers We evaluate lemmatization by using and comparing two lemmatizers for German: the treetagger by Schmidt (1995) and the pattern lemmatizer by De Smedt and Daelemans (2012). Many of the lexicons only include base forms of SBWs, so lemmatization is a necessary step for those lexicons to identify inflections. However, due to the general problems of automatic lemmatization in German (Eger et al., 2016) and the special challenges historical and poetic language pose to automatic lemmatizers, mistakes and problems might occur that distort the detection of SBWs. Besides the general comparison between the two lemmatizers and no lemmatization at all, we also compared the automatic lemmatization with the manually added inflections some lexicons contain and the extension with inflections by the tool of the DTA. v) Case-sensitivity Related studies with lexicon-based methods typically lowercase all words for reasons of processing and normalization (Klinger et al., 2016). We wanted to explore if case affects the evaluation results (caseSensitive vs. caseInSensitive), as several words in German have a change in meaning depending on case, especially with regard to their sentiment. Sentiment Calculation To calculate the sentiment of a speech we employed a simple term counting method, often used with lexicon-based methods (Kennedy and Inkpen, 2006). The number of positive words according to the used configuration of parameters is subtracted by the number of negative words to get a general polarity score. If this score is negative, the speech is regarded as negative, otherwise as positive. If a sentiment lexicon contains polarity strengths for the SBWs, we additionally used these values in a similar way to calculate a sentiment score. Therefore, for these lexicons we calculated and compared two scores: one for dichotomous polarity and one for polarity strengths. Results In this chapter we present results from our evaluation of all possible combinations of the previously described parameters in comparison to the human annotated gold standard data. We used well-established metrics for sentiment analysis evaluation (Gonçalves et al., 2013). Our main metric is accuracy, which is the proportion of the correctly predicted speeches of all speeches. To get a more holistic view of the results, we also looked at recall, precision and F-measures. We furthermore analyzed the metrics for positive and negative speeches separately. Since our gold standard has an overrepresentation of negative speeches, misbehaviors of configurations like a general prediction of negative speeches would otherwise go undetected. We use the random baseline, the majority baseline and agreement measures as benchmarks. Because of the unequal distribution, the random baseline is set to 0.525 and the majority baseline is set to 0.695 for the accuracy. Mozetic et al. (2016) propose the average percentage of agreement of all annotator pairs (APA, 77%) as baseline for sentiment analysis evaluations. We also take this baseline into account when assessing the performance. As we analyzed more than 400 different configurations, we are not able present all evaluation results in detail in this paper. However, an evaluation table of the best configurations ordered by accuracy is available online 3 . Note that we removed configurations from the table that tend to predict almost all speeches as negative and therefore accomplish accuracies close to the majority baseline, but are actually flawed. Figure 2 shows a snippet of the top part of the table. In the following we summarize the main findings of our evaluation study: • The overall best performance is delivered by the SentiWS lexicon and the combined lexicon if the remaining parameters are the same • When a lexicon has polarity strengths, calculation with those always outperform calculations with the dichotomous polarity of the lexicon. Apart from BAWL, the general rule is that lexicons with polarity strengths (SentiWS, CK) in general outperform all other lexicons with only dichotomous polarities (GPC, NRC) • The extension with historical linguistic variants consistently yields the strongest performance boost for all lexicons. The extension with historical inflections is better than just automatic lemmatization. • Stop words lists have differentiated influences. Some lexicons (e.g. GPC) tend to excessive prediction of one polarity class, because of stop words and frequent words. However, this does not always lead to worse accuracies but in-depth word analysis shows incorrect sentiment assignments to words. We therefore recommend the usage of stop word lists when lexicons contain stop words or when stop words are generated by means of additional NLP processes. The best rated lexicons (e.g. SentiWS) are not influenced by stop words at all. • Both lemmatizers perform almost equally good. A detailed analysis of the results shows that both lemmatizers have problems with the historical language of the speeches. However, for lexicons that consist only of base forms, lemmatization leads to a better overall performance. The results for lexicons with manually added inflections show that those inflections work better than the automatic lemmatization. For many lexicons, a combination of both yields better results. • Case-sensitivity does not have an effect on the overall quality of sentiment evaluations in our test corpus. The best overall performance is accomplished with the polarity strengths of the SentiWS lexicon extended with historical linguistic variants, lemmatization via treetagger, no stop words lists and ignoring case-sensitivity. The accuracy for this performance is 0.705 with 141 speeches correctly predicted. This result is over the benchmark of the random and majority baseline but below the average percentage of agreement of the annotator pairs (77%). Discussion and Outlook We made several contributions to the research area of sentiment analysis in historical drama texts, one being the creation of an annotated corpus of drama speeches. However, there are some limitations concerning the annotation study: We identified low to mediocre levels of agreement among the annotators for the polarity annotation. The low level of agreement for annotation schemes with multiple categories is also found in several other research areas (Momtazi, 2012;Takala et al., 2014). The mediocre levels of agreement for the binary annotation are in line with similar research in the field of literary texts and texts with historical language (Sprugnoli et al., 2016). However, compared to other types of text, the agreement for the binary polarity is rather low (e.g. Thet et al., 2010;Prabowo and Thelwall, 2009). Sentiment annotation of literary texts seems to be a rather subjective and challenging task. Our annotators also reported difficulties due to the lack of context and general problems in understanding the poetic and historical language of Lessing. Note that many of the challenges very likely occurred because the annotators were non-experts concerning the drama texts. Some feedback of the annotators also points to the possibility that the used annotation schemes were not sufficient or representative for the application area of sentiment in historical plays. Another limitation is the small size of the corpus: 200 speeches amount to 2% of the speeches of our original Lessing corpus. While such small sample sizes are not uncommon for sentiment annotation of literary texts , they certainly lessen the significance of the results. To address some of the mentioned limitations, we are planning to conduct larger annotation studies with trained experts in the field of Lessing, more speeches and a more sophisticated annotation scheme. Our major contribution is the systematic evaluation of different configurations of lexicon-based sentiment analysis techniques. Many of our findings are important not only for sentiment analysis of German drama texts, but for sentiment analysis of corpora with historical and poetic language in general. We identified SentiWS (Remus et al., 2010) as the best performing lexicon for our corpus. The accuracy of the combined lexicon is overall slightly lower than the top rated SentiWS-configuration. The reason for this might be the extension of SentiWS by many problematic and distorting sentiment bearing words that can be regarded as noise. Furthermore, the transfer of some problems of other lexicons, like the missing of manually added inflections, may be responsible for the decreased performance of the combined lexicon as compared to SentiWS alone. We highly recommend the usage of sentiment lexicons with polarity strengths, since they consistently outperform dichotomous polarity calculations. This proves that for calculation purposes, sentiment bearing words are better represented on a continuous scale. The calculation with polarity strengths seems to better represent human sentiment annotation than the usage of dichotomous values (+1 / -1), as many sentiment bearing words indeed have different intensities that are perceived differently by human annotators and therefore should also be weighted differently for the automatic sentiment calculation. The noticeable and consistent performance boost from the extension of lexicons by historical linguistic variants highlights the linguistic differences between the contemporary language of the lexicons and the historical language of the 18 th century. The creation of sentiment lexicons, especially for the historical language of past centuries, is a beneficial next step and possibilities for historical German are already examined (Buechel et al., 2016). Considering stop words, we highly recommend checking sentiment lexicons for highly frequent words of the corpus, especially for historical and poetic language. The meaning concerning the sentiment of words can differ throughout history (Hamilton et al., 2016) and is also dependent on the linguistic style of a specific author. Therefore, stop words and other highly frequent words might have different sentiment connotations in contemporary and historic German language. It also shows that historic language poses challenges for automatic lemmatization, as it is not as effective as the extension by historical inflections. Overall, we were able to achieve acceptable levels of accuracy with our best performing configuration (70%), considering the basic methods used, the linguistic challenges the corpus poses and the mediocre levels of agreement of the annotators. Furthermore, we did not consider sentiment classes like neutral or mixed, although the annotation study showed that many speeches of the corpus are actually not strictly positive or negative. However, accuracy results in other application areas of sentiment analysis like product reviews or social media are generally higher, e.g. around 90% (Vinodhini and Chandrasekaran, 2012). Besides the historical and poetic language difficulties, common problems of lexicon-based methods like the handling of irony and negations are certainly additional reasons for the mediocre accuracies. Based on our results, we consider the usage of general purpose lexicons alone as not sufficient to achieve acceptable accuracy scores. Using the results of the planned large-scale annotation studies, we will try to create corpora for evaluation and the development of more sophisticated methods of sentiment analysis, such as machine learning and hybrid techniques in order to improve accuracy and integrate other polarity classes as well. We are also aware that more complex emotional categories like anger, trust or surprise are also of interest for sentiment analysis in literary texts Mohammad, 2011). While at the moment resources and best practices to include emotional categories in sentiment analysis are rare (Mohammad and Turney, 2010), we are expecting substantial progress from an ongoing shared task on implicit emotion recognition to gather more insights into this area 4 . Another limitation of the presented results is that we only regarded speeches for sentiment analysis. However to further explore the possibilities and use cases for sentiment analysis on drama texts, we developed a web tool 5 for the exploration of sentiment in Lessing's plays. The tool visualizes the results of the best performing configuration of our evaluation study. Literary scholars can explore sentiment polarity distributions and progressions on several levels, i.e. for the whole play, for single acts, scenes and speeches, but also for individual speakers or for the relationship between two speakers. Further, we also integrated the results of sentiment calculation with the NRC Emotion Lexicon (Mohammad and Turney, 2010), so besides polarity, more complex emotion categories like anger and surprise can be explored as well. As an example, Figure 3 illustrates the polarity progression for Lessing's "Emilia Galotti" throughout the five acts of the play. On the x-axis, every act is represented by a bar. On the y-axis the polarity of the entire act is represented as an absolute value. The tool also enables the analysis of normalized values, e.g. by the length of the text unit. The tool shows that based on our polarity calculation the play starts with a rather positive polarity in the first act, but becomes more and more negative as the play progresses. This tool only represents a first prototype. Meanwhile, we are working close with literary scholars to gather more insights into needs and requirements for the literary analysis of emotion and sentiment in drama texts. By extending our corpus to other authors and eras, we also plan to explore sentiment analysis on drama texts beyond Lessing's plays. Figure 2 . 2Table snippet of the results of the evaluation of all configurations Figure 3 . 3Polarity progression for Emilia Galotti per Act 's α APADifferentiated polarity 0.22 40% Binary polarity 0.47 77% Table 1. Measures of agreement Table 2 2reduces the results to the best configurations of every single sentiment lexicon and shows the corresponding accuracies as well as F-measures for both polarity classes (positive/negative).lexicon exten- sion lemmati- zation stop words case accu- racy F-Posi- tive F-Nega- tive SentiWS (Polarity Strengths) dtaEx- tended treetagger noStopWords caseInSensitive 0.7 0.46 0.79 Combined Lexicon dtaEx- tended treetagger noStopWords caseInSensitive 0.68 0.44 0.77 CK (Polarity Strengths) dtaEx- tended treetagger enhancedFil- teredList caseSensitive 0.6 0.55 0.65 GPC dtaEx- tended pattern enhancedFil- teredList caseSensitive 0.6 0.72 0.42 NRC dtaEx- tended treetagger noStopWords caseInSensitive 0.53 0.44 0.59 BAWL dtaEx- tended treetagger noStopWords caseInSensitive 0.49 0.50 0.46 Table 2. 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Alignement de séquences phonétiques pour une analyse phonologique des erreurs de transcription automatique Camille Dutrey camille.dutrey@lne.fr Laboratoire de Phonétique et Phonologie (LPP) 19 rue des BernardinsParisFrance ( Laboratoire National de Métrologie et d'Essais (LNE) 29 avenue Roger HennequinTrappesFrance ( Martine Adda-Decker Laboratoire de Phonétique et Phonologie (LPP) 19 rue des BernardinsParisFrance ( Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI) Rue John Von NeumannOrsayFrance Naomi Yamaguchi naomi.yamaguchi@univ-paris3.fr Laboratoire de Phonétique et Phonologie (LPP) 19 rue des BernardinsParisFrance ( Alignement de séquences phonétiques pour une analyse phonologique des erreurs de transcription automatique Actes de la conférence conjointe JEP-TALN-RECITAL 2016, volume 1 : JEP 46sequence alignmentdistinctive featuresdynamic programmationautomatic speech recognitiontranscription errors La transcription automatique de la parole obtient aujourd'hui des performances élevées avec des taux d'erreur qui tombent facilement en dessous de 10% pour une parole journalistique. Cependant, pour des conversations plus libres, ils stagnent souvent autour de 20-30%. En français, une grande partie des erreurs sont dues à des confusions entre homophones n'impliquant pas les niveaux acousticophonétique et phonologique. Cependant, de nombreuses erreurs peuvent s'expliquer par des variantes de productions non prévues par le système. Afin de mieux comprendre quels processus phonologiques pourraient expliquer ces variantes spécifiques de la parole spontanée, nous proposons une analyse des erreurs en comparant prononciations attendue (référence) et reconnue (hypothèse) via un alignement phonétique par programmation dynamique. Les distances locales entre paires de phonèmes appariés correspondent au nombre de traits phonétiques disjoints. Nos analyses permettent d'identifier les traits phonétiques les plus fréquemment impliqués dans les erreurs et donnent des pistes pour des interprétations phonologiques.ABSTRACTPhonetic sequences alignment for a phonemic analysis of automatic speech transcription errorsNowadays, word error rates of automatic speech transcription systems tend to fall below 10% for journalistic speech. However, in the case of free conversations, error rates remain much higher, typically around 20-30%. Error sources range from system limitations such as out of vocabulary words to speaker production errors. In French, many errors are due to homophonic words, for which neither acoustic-phonetic nor phonological levels are to blame. An important part may be related to production variants unknown to the system. To investigate which phonological processes might contribute to explain fluent speech specific variants, a phone sequence alignment between reference and hypothesis phone strings was implemented using dynamic programming. Local distances are computed as the total number of disagreeing phonetic features between phone pairs. The resulting analyses highlight the features most frequently involved in recognition errors and provide insight for phonological interpretations of fluent speech variation.MOTS-CLÉS : alignement de séquences, traits distinctifs, programmation dynamique, reconnaissance automatique de la parole, erreurs de transcription. Introduction Dans cette contribution, nous proposons d'analyser les erreurs de transcription automatique de la parole d'un point de vue phonétique. Les erreurs d'un système de transcription sont habituellement comptabilisées au niveau du mot et une confusion entre deux formes fléchies homophones (p. ex. « politique » et « politiques ») compte autant qu'une erreur entre mots très différents (p. ex. « affaire » et « ferveur » dans la suite « l'affaire Woerth » reconnue comme « la ferveur »). Pour cela, nous comparons la chaîne phonétique correspondant aux mots de la transcription automatique (hypothèse ou HYP) à celle provenant des mots de la transcription manuelle (référence ou REF). La comparaison est effectuée dans des zones d'erreur, c'est-à-dire aux endroits où le système de transcription produit des mots différents de ceux attendus par la référence. Pour comparer des chaînes de caractère, une mesure fréquemment utilisée est la distance d'édition ou la distance de Levenshtein (Levenshtein, 1965), qui donne le nombre minimal de caractères qu'il faut supprimer, insérer ou remplacer pour passer d'une chaîne à l'autre. Dans notre cas, les caractères correspondent à des phones. L'alignement de séquences phonétiques a été utilisé pour de nombreuses recherches en phonologie computationnelle (Kondrak, 2000(Kondrak, , 2003, en dialectologie (Heeringa, 2004;Heeringa et al., 2002) ou encore en phonétique clinique (Connolly, 1997). Nous adoptons ici ce type d'approche pour l'analyse d'erreurs issues de systèmes de transcription automatique de la parole. Nous proposons d'adapter la distance de Levenshtein pour mieux tenir compte de la proximité phonético-phonologique entre phonèmes. Par exemple, le /p/ est plus proche du /b/ que du /s/ ou du /a/. Corpus, approche et méthode Nos travaux s'inscrivent dans le cadre plus large de recherches menées sur la caractérisation des erreurs produites par des systèmes de transcription de la parole, notamment au sein du projet ANR VERA 1 (Goryainova et al., 2014;Luzzati et al., 2014;Santiago et al., 2015). L'objectif est d'étudier l'impact de ces erreurs sur des applications plus complexes comme l'indexation, la traduction ou le repérage d'entités nommées à partir de flux audio. D'autres finalités consistent à contribuer à une évaluation plus informative des systèmes de transcription et à mieux rendre compte des aspects linguistiques impliqués dans les erreurs. Nous développons ce dernier volet en focalisant sur l'interface phonétique-phonologie dans les erreurs de transcription des sorties du système de reconnaissance du LIUM (Bougares et al., 2013) avec les données de la campagne ETAPE (Gravier et al., 2012). Dans la suite, nous présentons d'abord le corpus qui fournit les erreurs de transcription, c'est-à-dire les séquences phonétiques (REF vs HYP) à aligner. Nous développons ensuite le choix de traits phonétiques pour décrire les phonèmes du français, utilisés pour le calcul de distance entre paires de phonèmes. Nous précisons que ce calcul de distance se fait uniquement à partir de traits sans prendre en compte les réalisations acoustiques des sons impliqués. Enfin, nous rappelons brièvement l'algorithme de programmation dynamique tel que mis en oeuvre pour notre analyse. Spécification en traits des phonèmes du français La comparaison des phonèmes du français s'appuie sur la construction d'une matrice de traits distinctifs : les traits pris en compte sont uniquement les traits distinctifs en français et sont adaptés de Sagey (1986) Calcul de distances pour la comparaison de paires de phonèmes Nous avons calculé, pour chaque paire de phonèmes (' i , ' j ) du français 3 , une distance phonétique d(i, j) (cf. section 2.4) à partir de la spécification en traits présentée ci-dessus. La distance d(i, j) correspond à la somme de l'opérateur ou-exclusif sur les 13 dimensions des vecteurs correspondant aux phonèmes ' i et ' j (V 'i V 'j ) et donne le nombre de traits phonétiques disjoints. Alors que théoriquement la valeur maximale pourrait être 13 pour un vecteur de dimension 13, il se trouve que les traits sont distribués de telle manière que la distance maximale se limite à 9. La distribution de ces distances est présentée en figure 2, selon le type de paire : voyelle versus voyelle ; consonne versus consonne ; voyelle versus consonne. 36 paires ont une distance nulle : il s'agit de comparaisons de phonèmes identiques, en tenant compte des allophones. Ainsi, les paires /i/-/j/, /y/-/4/ et /u/-/w/ sont également distantes de 0. Mesure de distances phonétiques et alignement de séquences Le programme d'alignement de séquences phonétiques est adapté de l'algorithme de Levenshtein (1965) qui permet de calculer des mesures de distances entre deux chaînes de caractères. Il s'appuie sur la programmation dynamique (Bellman, 1957;Vintsyuk, 1968), dont le principe est rapidement décrit ci-dessous. Soient deux séquences phonétiques : I = ' 1 ' 2 . . . ' I (hypothèse) et J = ' 1 ' 2 . . . ' J(D(i 1, j) + d(i, j) insertion D(i, j 1) + d(i, j) omission D(i 1, j 1) + 2 ⇥ d(i, j) correct ou substitution (1) où d(i, j) = 0 si ' i = ' j . L'arrêt se fait naturellement à (I, J), à la fin des deux chaînes. Pour récupérer l'alignement correspondant à la distance globale minimale, nous avons introduit dans la récurrence une matrice de retour-arrière, qui à chaque point (i, j) garde la mémoire du meilleur point précédent (argmin de l'équation 1). La distance globale, qui permet de caractériser la dissimilarité phonétique entre deux séquences de phonèmes, est ensuite normalisée par le nombre de phonèmes dans la référence. Elle nous permet d'analyser plus finement les erreurs commises par les systèmes de transcription de la parole, notamment grâce à son appui sur des connaissances phonologiques. Dans un premier temps, nous souhaitons limiter nos analyses à des zones d'erreurs que nous jugeons intéressantes d'un point de vue phonétique comme phonologique. Dans ce but, nous rejetons dans la suite celles dont les longueurs sont très différentes entre REF et HYP et pour lesquelles des problèmes de découpage du signal ou de bruit de fond viennent supplanter les facteurs linguistiques. Nous gardons ainsi 11 753 zones d'erreur des données ETAPE, dont les distances globales normalisées se distribuent entre 0 et 5 (cf. figure 4). Pour ces zones d'erreur, l'information de retour-arrière a été utilisée pour récupérer l'alignement de la séquence phonème à phonème. En effet, l'alignement des zones d'erreur au niveau du phonème, comme illustré en figure 3, permet de mieux décrire et analyser les erreurs produites par le système de transcription de la parole d'un point de vue phonétique. Comme le met en exergue l'exemple illustré, cet alignement permet d'obtenir une finesse de localisation et d'identification phonétique des erreurs totalement absente des méthodes classiques qui évaluent les systèmes de reconnaissance automatique de la parole au seul niveau de la séquence de mots. . Dans ce contexte de quasi-absence du de, le modèle de langage impose « forte natalité » plutôt que « fort taux de natalité ». REF fort taux de ) [ f O K t o d ] HYP forte ) [ f O K t @ * ] erreur D D S C C C C S D Analyse des erreurs de transcription automatique La méthode présentée dans cette étude, permettant de calculer des distances entre chaînes phonétiques au-delà des mots et d'aligner ces dernières en tenant compte d'informations phonologiques, peut être mise au service d'une analyse linguistique des erreurs de transcription de la parole. Nous souhaitons ainsi contribuer à l'évaluation des systèmes de transcription de la parole en utilisant ces informations de manière à mieux identifier les variations impactant les phonèmes et le rôle des traits phonétiques. Caractérisation de zones d'erreurs par distance phonétique La figure 4 permet de visualiser la distribution des zones d'erreur en fonction de leur distance phonétique. Une analyse préliminaire qualitative permet de faire l'hypothèse que cette mesure de distance pourrait permettre de catégoriser efficacement les zones d'erreur produites par les systèmes de transcription automatique. Conclusion Nous avons présenté nos travaux sur l'analyse des erreurs produites d'un système de transcription de l'évaluation ETAPE. Afin d'analyser des zones d'erreur (incluant tous les mots erronés entre les deux extrémités bien reconnues), nous avons introduit une nouvelle méthodologie s'appuyant sur la programmation dynamique en traitement automatique et les traits phonétiques. Cette approche vise à aligner non plus les mots en tant que tels, mais leurs séquences phonétiques respectives afin de mieux décrire les erreurs en matière de proximité phonique. L'utilisation des traits met en lumière le rôle de certains traits, eux-mêmes importants dans le système phonologique, dans les erreurs du système de transcription. En particulier, ce résultat apporte des arguments en faveur de la structuration du système phonologique en traits hiérarchisés (Clements, 1985). Les résultats permettent de trier les zones d'erreur suivant une distance phonétique : à distance faible, nous sommes en présence d'erreurs homophones et quasi-homophones. Au sein de ce sous-ensemble, il sera intéressant d'étudier plus finement les processus phonétiques et phonologiques (lénition, assimilation, chute de segments, réductions et divers metaplasmes). Plusieurs améliorations de la procédure d'alignement sont prévues, et notamment : affiner la représentation des traits phonétiques (p. ex. type de spécification) ; améliorer le calcul des distances locales en tenant compte du voisinage phonétique immédiat. Nous avons également pour perspective de développer les analyses à l'interface phonétique-phonologie, de produire des analyses d'erreur en contexte (analyse des triphones) et fonction des frontières de mots/syllabes et de mieux rendre compte des phénomènes de réduction en parole spontanée. Enfin, nous envisageons dans le futur d'ajouter un décodage phonétique afin d'étudier le rôle des traits phonétiques dans les erreurs de reconnaissance automatique hors contraintes lexicales (et hors contraintes de plus haut niveau). FIGURE 2 - 2Distribution des paires de phonèmes en fonction de leur distance locale, avec caractérisation du type de paire comparée (VV = voyelles ; CC = consonnes ; VC = voyelle vs consonne).On peut remarquer que les distances faibles correspondent majoritairement à des paires "homogènes" VV et CC et que la proportion de paires CV augmente au fur et à mesure que la distance augmente.Le tableau 2 recense les paires de phonèmes appartenant aux distances maximales et minimales (hors distances nulles) pour chaque type de paires (VV, CC et VC). On remarque la présence de /p/ et /t/ dans les paires de CV maximalement distantes : ceci peut s'expliquer par le fait que ces phonèmes, particulièrement /t/, sont considérés comme des sons phonologiquement non marqués (Paradis & Prunet, 1991) et sont de ce fait spécifiés par moins de traits que les autres phonèmes. Les distances entre phonèmes sont utilisées dans cette étude comme connaissance linguistique dans le programme d'alignement de séquences phonétiques par programmation dynamique. référence) de longueur I et J respectivement. On impose le départ de l'alignement au début des deux chaînes respectives ce qui se traduit par des conditions d'initialisation D(0, 0) = 0, D(0, j) = 1 pour j > 0, D(i, 0) = 1 pour i > 0. Ensuite la récurrence sur i, j (chaînes partielles de 1 à i et de 1 à j) s'écrit comme suit : FIGURE 3 - 3Extrait de parole transcrite avec comparaison de l'alignement REF versus HYP produit par un système d'évaluation de la transcription automatique (au niveau du mot) et de celui produit par le système d'alignement de séquences phonétiques (C = correct ; D = suppression ; S = substitution). La figure 3 illustre une zone d'erreur de distance globale normalisée égale à 1 pour laquelle l'évaluation classique produit deux omissions de mots et une substitution et qui, d'un point de vue phonétique, se révèle majoritairement correcte. L'erreur commise peut s'expliquer d'un côté par une forte réduction temporelle de l'article « de » dans le contexte « fort taux de natalité » : le schwa est tombé et le [d] se limite à environ 30 ms de barre de voisement avant le [n] FIGURE 4 - 4Caractérisation des zones d'erreur : (a) distribution des zones d'erreur en fonction de la distance globale normalisée entre REF et HYP ; (b) distribution des paires de phonèmes impliquées par type d'erreur subie en fonction de la distance globale normalisée (C = correct ; S = substitution ; D = suppression ; I = insertion). FIGURE 5 - 5Distribution des traits phonétiques selon leur implication dans des types d'erreurs. 2.1 Corpus de parole préparée et spontanée : ETAPE Nous avons travaillé sur un corpus de parole journalistique préparée et spontanée constitué d'émissions radio et télé-diffusées, le corpus ETAPE(Gravier et al., 2012). Nous en avons traité 58 enregistrements, ce qui représente environ 35h de parole pour 339k mots prononcés. La transcription automatique produite par le système du LIUM, comporte 323k mots sur ce sous-ensemble d'ETAPE ; pour une description détaillée de ce système et de sa paramétrisation, se référer àBougares et al. (2013). Afin d'étudier les erreurs de transcription automatique d'un point de vue phonétique, deux étapesFIGURE 1 -Extrait de parole transcrite avec alignement REF versus HYP et indication du type d'erreur assignée par le système pour chaque mot (C = correct ; D = suppression ; S = substitution).préliminaires ont été réalisées : 1. un alignement REF vs HYP au niveau des mots à l'aide du NIST Scoring Toolkit 2 pour obtenir les types d'erreur (correct versus substitution, suppression et insertion) ; 2. une phonétisation des mots réalisée avec le système d'alignement forcé du LIMSI (Gauvain et al., 2003) et un jeu de 33 phonèmes du français : à noter l'absence du /oe/ supplanté par /@/. L'alignement est réalisé sur l'ensemble de l'audio, à la fois pour la référence et l'hypothèse, en utilisant le même dictionnaire de prononciation (dictionnaire du LIUM) que celui utilisé par le système de reconnaissance de la parole. REF « donc le fort taux de natalité » HYP « donc le * * forte natalité » erreur C C D D S C Nous avons extrait du corpus 18 051 zones d'erreurs : une zone d'erreur correspond à une suite ininterrompue de mots erronés entre deux mots non erronés. La figure 1 donne un exemple d'énoncé où la zone d'erreur est marquée en gras. Les données ont été pré-traitées de manière à en exclure les zones d'erreur trop complexes résultant souvent de décalages entre REF et HYP. Cette sélection, qui exclut 13,6 % des zones d'erreur, s'appuie sur des critères liés à la différence de longueur entre la séquence phonétique de REF et celle de HYP. Nous avons également choisi d'écarter les zones d'erreur incluant des phénomènes particuliers de la parole spontanée, qui seront analysés à part : hésitations vocaliques, présence d'amorces de mot, etc. Au final, 16,5 % des zones d'erreur ont ainsi été mises de côté et 13 021 zones d'erreurs sont conservées. TABLE 1 - 1Spécification en traits pour les phonèmes du français utilisée pour le calcul de distance et l'alignement de séquences phonétiques (cons. = consonantique ; cont. = continu ; post. = postérieur). Le trait [consonantique] distingue les consonnes des voyelles et semi-voyelles, et représente le degré de constriction dans le conduit vocal. Les traits de lieu [labial], [coronal] et [dorsal] indiquent le lieu d'articulation des sons ; le trait [postérieur] distingue dans les consonnes [coronal] les sons produits avec l'arrière de la langue de ceux produits avec l'avant de la langue. Le trait [voisé] désigne le voisement des phonèmes. Le trait de mode [sonant] distingue les sonantes des obstruantes ; [continu] indique le passage continu de l'air dans le conduit vocal, et distingue les sonantes et fricatives des occlusives. [nasal] est spécifié pour les sons laissant passer l'air par la cavité nasale. Les traits vocaliques [haut] et [bas] caractérisent l'aperture. Le trait [arrondi] spécifie les (semi-)voyelles produites avec un arrondissement des lèvres. Chaque phonème est ainsi représenté par un vecteur de dimension 13 (V ' ) avec des 0 pour tous les traits non-spécifiés et des 1 pour les traits spécifiés. TABLE 2 - 2Paires de phonèmes impliquées dans les distances minimales et maximales par type (VV = paires de voyelles ; CC = paires de consonnes ; VC = paires de voyelle versus consonne). En effet, les zones présentant une distance normalisée nulle permettent d'identifier des chaînes homophoniques ou quasi-homophoniques, telles « leaders » versus « lits de leur » ou « base » versus « basse », y compris sur des séquences relativement longues, comme « vin de Féternes » versus « vingt-deux faits termes ». ou des prononciations non-canoniques sources de confusion, comme dans « sans sans langue de bois » [sÃsÃlÃg@d@bwa] versus « cinq cent emplois » [sẼksÃÃplwa] (pour cet exemple, la transcription automatique est également mise en difficulté par de la parole superposée). Enfin, les séquences présentant une distance maximale (p. ex. « bon Copé » vs « à la rentrée ») sont souvent particulièrement difficiles à transcrire, avec beaucoup de parole superposée ou de bruit environnant.3.2 Implication des traits phonétiques dans les zones d'erreursL'examen des traits phonétiques impliqués dans les zones d'erreur (cf.figure 5)indique que tous les traits n'ont pas la même importance dans les erreurs. Les traits les mieux reconnus sont les traits [continu], [voisé], [sonant], [consonantique] qui sont les traits les plus fréquents, et qui correspondent à des distinctions fondamentales phonologiques (Clements, 1985). Les traits qui sont plus souvent substitués que bien reconnus sont les traits [arrondi] et [postérieur], qui ne distinguent respectivement qu'une petite partie des voyelles et des consonnes. Quant aux suppressions et aux insertions, elles concernent majoritairement les traits qui sont partagés par de nombreux phonèmes : [continu] et [voisé] (spécifiés pour 27 phonèmes), [sonant] (spécifié pour 21 phonèmes). Ces premières observations semblent indiquer que les traits participent dans les différents types d'erreurs en fonction de leur rôle dans le système phonologique et de leur place dans une séquence de sons. Ces résultats sont bien entendu à approfondir par l'analyse des combinaisons de traits impliqués dans chaque type d'erreur, et par l'étude des séquences de traits dans les suites de phonèmes.Les séquences présentant une distance moyenne (p. ex. 2, distance la plus fréquente du corpus) sont phonétiquement proches tout en présentant, notamment, de nombreuses substitutions sur des paires de type VV ou CC, comme dans « que ce label » [ks@labEl] versus « solennel » [sOlanEl] . http://projet-vera.univ-lemans.fr/.Actes de la conférence conjointe JEP-TALN-RECITAL 2016, volume 1 : JEP . Outil accessible à l'adresse Web suivante : http://www1.icsi.berkeley.edu/Speech/docs/sctk-1.2/sctk.htm.Actes de la conférence conjointe JEP-TALN-RECITAL 2016, volume 1 : JEP . Soit 561 combinaisons ; le nombre de paires correspond au nombre de cases d'une matrice triangulaire hors diagonale (33 ⇥ 32/2) plus la diagonale (33).Actes de la conférence conjointe JEP-TALN-RECITAL 2016, volume 1 : JEP RemerciementsCe travail a été partiellement financé par l'Agence Nationale de la Recherche au titre du projet VERA (ANR-12-BS02-006-01) et du programme Investissements d'Avenir (ANR-10-LABX-0083). Dynamic Programming. Bellman R, Princeton University PressBELLMAN R. (1957). Dynamic Programming. Princeton University Press. LIUM ASR system for ETAPE French evaluation campaign : experiments on system combination using open-source recognizers. Bougares F, P Deléglise, Estève Y. &amp; Rouvier M, 6 th International Conference on Text. Speech and Dialogue (TSD'13BOUGARES F., DELÉGLISE P., ESTÈVE Y. & ROUVIER M. (2013). LIUM ASR system for ETAPE French evaluation campaign : experiments on system combination using open-source recognizers. In 6 th International Conference on Text, Speech and Dialogue (TSD'13). The geometry of phonological features. Phonology, 2. N Clements G, CLEMENTS G. N. (1985). The geometry of phonological features. Phonology, 2, pp. 225-252. Quantifying target-realization differences. Part I : Segments. Connolly J , Clinical Linguistics & Phonetics. 11CONNOLLY J. (1997). Quantifying target-realization differences. Part I : Segments. Clinical Linguistics & Phonetics, 11, pp. 267-287. Conversational telephone speech recognition. J Gauvain, L Lamel, H Schwenk, G Adda, Chen L. &amp; Lefèvre F, International Conference on Acoustics, Speech, and Signal Processing. ICASSP'03GAUVAIN J., LAMEL L., SCHWENK H., ADDA G., CHEN L. & LEFÈVRE F. (2003). Conversational telephone speech recognition. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP'03). Morpho-Syntactic Study of Errors from Speech Recognition System. M Goryainova, C Grouin, &amp; Rosset S, Vasilescu I, LREC'14. GORYAINOVA M., GROUIN C., ROSSET S. & VASILESCU I. (2014). Morpho-Syntactic Study of Errors from Speech Recognition System. In LREC'14, p. 3050-3056. The ETAPE Corpus for the Evaluation of Speech-based TV Content Processing in the French Language. G Gravier, G Adda, N Paulsson, M Carré, Giraudel A. &amp; Galibert O, 8 th International Conference on Language Resources and Evaluation (LREC'12). GRAVIER G., ADDA G., PAULSSON N., CARRÉ M., GIRAUDEL A. & GALIBERT O. (2012). The ETAPE Corpus for the Evaluation of Speech-based TV Content Processing in the French Language. In 8 th International Conference on Language Resources and Evaluation (LREC'12). Measuring Dialect Pronunciation Differences using Levenshtein Distance. Heeringa W, Rijksuniversiteit Groningen, GroningenPhD dissertationHEERINGA W. (2004). Measuring Dialect Pronunciation Differences using Levenshtein Distance. PhD dissertation, Rijksuniversiteit Groningen, Groningen. Validating Dialect Comparison Methods. Heeringa W, J &amp; Nerbonne, Kleiweg P, 24 th Annual Meeting of the Gesellschaft für Klassifikation (GFKL'02). SpringerHEERINGA W., NERBONNE J. & KLEIWEG P. (2002). Validating Dialect Comparison Methods. In 24 th Annual Meeting of the Gesellschaft für Klassifikation (GFKL'02), p. 445-452 : Springer. A New Algorithm for the Alignment of Phonetic Sequences. Kondrak G, 6 th Applied Natural Language Processing Conference (ANLP'00). KONDRAK G. (2000). A New Algorithm for the Alignment of Phonetic Sequences. In 6 th Applied Natural Language Processing Conference (ANLP'00), p. 288-295. Phonetic Alignment and Similarity. Kondrak G, Computers and the Humanities. 373KONDRAK G. (2003). Phonetic Alignment and Similarity. Computers and the Humanities, 37 (3), pp. 273-291. Binary Codes Capable of Correcting Deletions, Insertions and Reversals. V I Levenshtein, Doklady Akademii Nauk SSSR. 163LEVENSHTEIN V. I. (1965). Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Doklady Akademii Nauk SSSR, 163, pp. 845-848. Human annotation of ASR error regions : Is "gravity" a sharable concept for human annotators. Luzzati D, C Grouin, Vasilescu I, Adda-Decker M, E Bilinski, N Camelin, J Kahn, C Lailler, Lamel L. &amp; Rosset S, 9þth International Conference on Language Resources and Evaluation (LREC'14). LUZZATI D., GROUIN C., VASILESCU I., ADDA-DECKER M., BILINSKI E., CAMELIN N., KAHN J., LAILLER C., LAMEL L. & ROSSET S. (2014). Human annotation of ASR error regions : Is "gravity" a sharable concept for human annotators ? In 9þth International Conference on Language Resources and Evaluation (LREC'14), p. 3050-3056. Feature Predictability and Underspecification : Palatal Prosody in Japanese Mimetics. A Mester R, Ito J, Language. 652MESTER R. A. & ITO J. (1989). Feature Predictability and Underspecification : Palatal Prosody in Japanese Mimetics. Language, 65 (2), pp. 258-293. Introduction : Asymmetry and Visibility in Consonant Articulations. Paradis C. &amp; Prunet J.-F , C. PARADIS & J.-F. PRUNETAcademic PressSan DiegoThe Special Status of Coronals : Internal and External EvidencePARADIS C. & PRUNET J.-F. (1991). Introduction : Asymmetry and Visibility in Consonant Articulations. In C. PARADIS & J.-F. PRUNET, Eds., The Special Status of Coronals : Internal and External Evidence, p. 1-28. San Diego : Academic Press. The Representation of Features and Relations in Non-linear Phonology. E C Sagey, Massachusetts Institute of TechnologyPhD thesisSAGEY E. C. (1986). The Representation of Features and Relations in Non-linear Phonology. PhD thesis, Massachusetts Institute of Technology. Towards a Typology of ASR Errors via Syntax-Prosody Mapping. Santiago F, Dutrey C. &amp; Adda-Decker M, Errors by Humans and Machines in multimedia, multimodal and multilingual data processing (ERRARE'15). SANTIAGO F., DUTREY C. & ADDA-DECKER M. (2015). Towards a Typology of ASR Errors via Syntax-Prosody Mapping. In Errors by Humans and Machines in multimedia, multimodal and multilingual data processing (ERRARE'15). Speech Discrimination by Dynamic Programming. Kibernetika, 4. Vintsyuk T, VINTSYUK T. (1968). Speech Discrimination by Dynamic Programming. Kibernetika, 4, pp. 81-88. A Vowel Feature Hierarchy of Contrastive Specification. Walker R, Toronto Working Papers in Linguistics. 122WALKER R. (1993). A Vowel Feature Hierarchy of Contrastive Specification. Toronto Working Papers in Linguistics, 12 (2), pp. 179-198. . Actes De La Conférence Conjointe, Jep-Taln-Recital , JEP1Actes de la conférence conjointe JEP-TALN-RECITAL 2016, volume 1 : JEP

Idioms: Formally Flexible but Semantically Non-transparent

Contrary to popular beliefs, idioms show a high degree of formal flexibility, ranging from word-like idioms to those which are like almost regular phrases. However, we argue that their meanings are not transparent, i.e. they are non-compositional, regardless of their syntactic flexibility. In this paper, firstly, we will introduce a framework to represent their syntactic flexibility, which is developed in Chae (2014), and will observe some consequences of the framework on the lexicon and the set of rules. Secondly, there seem to be some phenomena which can only be handled under the assumption that the component parts of idioms have their own separate meanings. However, we will show that all the phenomena, focusing on the behavior of idiom-internal adjectives, can be accounted for effectively without assuming separate meanings of parts, which confirms the non-transparency of idioms.PACLIC 2946

Idioms: Formally Flexible but Semantically Non-transparent Hee-Rahk Chae hrchae@hufs.ac.kr Hankuk Univ. of Foreign Studies Oedae-ro 81 17035MohyeonYongin GyeonggiKorea Idioms: Formally Flexible but Semantically Non-transparent Contrary to popular beliefs, idioms show a high degree of formal flexibility, ranging from word-like idioms to those which are like almost regular phrases. However, we argue that their meanings are not transparent, i.e. they are non-compositional, regardless of their syntactic flexibility. In this paper, firstly, we will introduce a framework to represent their syntactic flexibility, which is developed in Chae (2014), and will observe some consequences of the framework on the lexicon and the set of rules. Secondly, there seem to be some phenomena which can only be handled under the assumption that the component parts of idioms have their own separate meanings. However, we will show that all the phenomena, focusing on the behavior of idiom-internal adjectives, can be accounted for effectively without assuming separate meanings of parts, which confirms the non-transparency of idioms.PACLIC 2946 Introduction Although idioms are generally assumed to be noncompositional and, hence, non-flexible, it has been well attested that they are not fixed expressions formally. Even one of the most fixed idioms like [kick the bucket] show morphological flexibility in the behavior of the verb kick. Many other idioms show some degree of syntactic flexibility with reference to various types of syntactic behavior. Even the non-compositionality of them has been challenged, especially by those who are working under the framework of cognitive linguistics (cf. Croft &Cruse 2004: Ch. 9, andGibbs 2007). Reflecting this trend, Wasow et al. (1983) and Nunberg et al. (1994), for example, argue that syntactic flexibility is closely related to semantic transparency. In this paper, however, we are going to show that idioms can better be analyzed as semantically non-transparent although they are formally flexible, providing further evidence for the analysis in Chae (2014). Adopting Culicover's (2009) definition of construction, 1 Chae (2014) assumes that all and only idioms are represented as constructions. Under this view, grammar consists of three components: the set of lexical items (i.e. the lexicon), the set of rules and the set of constructions. He introduces some "notations/ conventions," which apply to regular phrase structures, to represent the restrictions operating on idioms. Employing these notations, he provides representations of various types of formal properties of idioms (in English and Korean): from the least flexible ones to the most flexible ones. However, the meanings of idioms are supposed to come from the whole idioms/constructions rather than from their component parts compositionally. In section 2, we will introduce a framework to represent the syntactic flexibility of idioms, which is developed in Chae (2014). We will also observe some consequences of the framework on the lexicon and the set of rules. Then, in section 3, we will examine some phenomena which seem to be handled only by assuming that the component parts of idioms have their own separate meanings. It will be shown, however, that all the phenomena can be accounted for effectively without assuming 1 The definition is as follows (Culicover 2009: 33): "A construction is a syntactically complex expression whose meaning is not entirely predictable from the meanings of its parts and the way they are combined in the structure." separate meanings of parts. We will focus on the behavior of idiom-internal adjectives, which is the most difficult to treat properly under the assumption of semantic non-transparency of idioms. Formal Flexibility Traditionally idioms are classified into two classes: "decomposable idioms"/ "idiomatically combining expressions (ICEs)" and "nondecomposable idioms"/ "idiomatic phrases (IPs)" (Nunberg 1978, Nunberg et al. 1994, Jackendoff 1997, Sag et al. 2002. Jackendoff (1997: 168-169) analyzes the two classes as follows: (1) A decomposable idiom: bury the hatchet (2) A non-decomposable idiom: kick the bucket In the former, which has the meaning of 'reconcile a disagreement' or 'settle a conflict,' the two component parts bury and [the hatchet] are assumed to have their own meanings and are separated from each other syntactically because the NP can be "moved" around. In the latter, no component parts have separate meanings and they are all connected syntactically. Espinal & Mateu (2010: 1397, however, argue that the distinction is "not as clear-cut and uniform as has been assumed." (3) a. i) John laughed his head off. ii) We laughed our heads off. b. Bill cried his eyes out on Wednesday, and he cried them out again on Sunday. c. i) *Whose/which heart did Bill eat out? ii) *His heart, Bill ate out. Wulff (2013: 279) makes it clear that idioms are not to be classified into separate categories: "…, resulting in a 'multi-dimensional continuum' of differently formally and semantically irregular and cognitively entrenched expressions that ultimately blurs the boundaries of idiom types as described in Fillmore et al. (1988) and various other, nonconstructionist idiom typologies …" According to Chae (2014: 495-6), however, Espinal & Mateu's (2010) analysis is not very reasonable, either. They argue all the internal elements of idioms have metaphoric/non-literal meanings and the meanings of the whole idioms can be derived from them compositionally. First of all, it is not clear how the metaphoric meanings of the internal elements can be obtained. Hence, we will need a framework which is formal enough to be computationally useful, and which is flexible enough to handle all the (morphological and syntactic) idiosyncrasies of idioms. For this purpose, Chae (2014) provides a system for the representation of idiomatic constructions. 2 Based on the fact that idiomatic expressions are typical examples showing irregularities on various levels, Chae (2014: 501) introduces four notations/ conventions to indicate lexical and formal restrictions operating on idioms: (4) a. <…>: the phrase is a syntactic "island" (no extraction is allowed). b. /…/: the phrase cannot be further expandable by internal elements. c. {…}: only the lexical items listed inside the brackets are allowed to occur. d. CAPITALIZATION: lexical items in capital letters have to be inflected for their specific forms. The former two are used to restrict (external and internal) syntactic behavior, and the latter two to regulate lexical and morphological behavior. 2 The system was developed on the basis of English idioms. However, its main purpose was to analyze Korean data in such idiom dictionaries as No (2002) and Choi (2014). The system has been proved to be very successful in representing Korean idioms and, hence, would be effective in analyzing idioms in other languages as well. Employing the notations in (4), Chae (2014) provides analyses of various types of idioms (in English and Korean) on the basis of their formal properties: from the least flexible ones to the most flexible ones. Please note that the notations apply to regular phrase structures. Regular properties of idioms are captured by way of these phrase structures and their irregular properties are captured with reference to the notations. We can analyze the [V one's head off] idiom in (3) as follows, under our representational system. (5) A new analysis of [V one's head off] The <…> on the VP indicates that no internal elements can be extracted out of the VP. The /…/ on the NP indicates that the node cannot be expanded further. The {…} under lexical categories indicates that only those lexical items inside it are allowed in the position. Under the N node, two lexical items are listed, which means that any of them is allowed in that position. The position under V is open because it has no {…}. As the lexical items under the Det and N are capitalized, they are required to have specific inflectional forms in actual sentences. Under the present framework, one of the most rigid idioms can be represented as follows (Chae 2014: 505-6): (6) As the XP has both <…> and /…/, no elements inside it can be extracted outward, and it cannot be further expanded internally. In addition, all the lexical items are enclosed with the notation {…}, which has only a single member. The flexibility will increase as more lexical items are capitalized, as more lexical items appear in {…}, and as <…> or /…/ disappears, eventually to become regular/non-idiomatic phrases. We assume that the framework introduced in Chae (2014) is formal enough to be computationally useful and flexible enough to handle various types of formal properties of idioms. The behavior of idioms is regular to the extent that they are represented on phrase structures, and is irregular to the extent that their structures are regulated by the notations in (4). The present framework has the effect of simplifying the main components of the grammar of a language, namely, the lexicon and the rule set. Firstly, the framework makes it possible to reduce the number of lexical items or their senses. For example, [ei(-ka) eps-'be preposterous'] in Korean is a typical idiom. Its literal meaning is something like 'there is no EI.' The "word" ei, although it can be followed by the nominative marker -ka, does not have its own meaning and it can only be used as a part of the idiom. Korean dictionaries list both the idiom [ei(-ka) eps-] and the word ei as separate entries, which is necessary because an adverb like cengmal 'really' can be inserted between ei(-ka) and eps-, [ei(-ka) cengmal eps-'be really preposterous']. Under the present approach, however, ei does not have to be listed as a separate entry and, hence, we can reduce the number of lexical items. We do not need the putative word because the construction representing the idiom is flexible enough to allow regular adverbs in between the two parts of the idiom, as we can see in chapter 3. Next, let us consider how we can simplify the rule set. The expression [… V 1 -tunci … V 2 -tunci kan-ey 'regardless of whether S 1 or S 2 '] is an idiom (No 2002: 268). It can be analyzed as follows: (7) The idiom has not only semantic anomalies but also syntactic anomalies. No other NPs in Korean have the structure of [S 1 S 2 N], in which the head noun has two sentential complements. If we are going to handle the structure with phrase structure rules, we have to posit a rule of the following: [NP → S S N]. This NP is very special in the sense that it has a unique internal structure of [S 1 S 2 N]. In addition, as it can occur only before the postposition -ey, its distribution is severely limited. These facts will render the rule set very complex. Under our approach, however, the construction in (7) is listed in the set of constructions. Then, we do not have to account for the special properties of the idiom with complex phrase structure rules and unmotivated stipulations. Semantic Non-transparency We assume that idioms are syntactically flexible and semantically non-transparent. We have seen that their syntactic flexibility can be handled effectively with the framework introduced in Chae (2014). In this section, we will focus on their semantic non-transparency. This assumption is based on the observation that the component parts of idioms do not have independent meanings and, hence, that the meaning of the whole idiom cannot be obtained from their parts compositionally. We will first review the issue over the compositionality of idiomatic expressions. Then, we will observe those phenomena which have led some scholars to assume that the component parts have separate meanings. Finally, we will develop a framework in which we can handle the phenomena, especially idiom-internal modification, without assuming separate meanings of parts. The Issue: Compositionality There has been a controversy over the issue whether idioms conform to the principle of compositionality or not. From a non-compositional point of view, for example, Nicolas (1995) argues that the parts of an idiom do not have individual meanings. Schenk (1995) argues that there is no relation between the meaning of the whole idiom and the meanings of its parts. In addition, Culicover & Jackendoff (2005: 34) makes it clear that "there is no way to predict the meanings of … from the words" in various types of "lexical VP idioms" (cf. Goldberg 1995). However, many other works stand on the other side: Wasow et al. (1983), Nunberg et al. (1994), Geeraerts (1995), Gibbs (1995), Sag et al. (2002), Espinal & Mateu (2010), and others. One of the most difficult challenges of compositional approaches lies in figuring out the meanings of the parts of an idiom. For example, it is generally assumed that the meanings of spill and beans in the idiom [spill the beans] are 'divulge' and 'information,' respectively. However, it is unlikely that we can get at their meanings, if there are individual meanings, without consulting the meaning of the whole idiom (cf. Geeraerts 1995, Gibbs 2007. Then, we do not have to worry about the meanings of individual parts from the beginning, because the reason we need to know the individual meanings is to compute the meaning of the whole idiomatic meaning. Although there are many cognitive linguistic approaches which seek to obtain the meanings of the parts on the basis of people's conceptual knowledge, they can only provide partial answers, as is hinted in Gibbs (2007: 709, 717). From a computational point of view, partial answers would be largely the same as no answers. Non-compositional approaches may run into difficulties as well, in such cases as the following: i) when a part of the idiom is displaced from its "original" position (cf. (8d)), and ii) when a part is modified (cf. (9-10)). In these cases, it would be very difficult to compute the idiomatic meaning without recourse to the meanings of the individual parts, especially under surface-oriented frameworks (cf. Wasow et al. 1983). Without handling these cases appropriately, a noncompositional approach would not be viable. When an internal element of an idiom is displaced from its original position, as in [the hatchet we want to bury after years of fighting], it would not be easy to capture the idiomatic meaning in surface-oriented frameworks, which do not have "underlying" structures, because parts of the idiom are separated from each other by a syntactic operation. Note that idioms are generally assumed to be word-like fixed expressions in previous non-compositional approaches. However, in our approach, the identity of the idiom can be captured with reference to the construction describing the idiom, which is in the set of constructions. As for idiom-internal modifiers, there are three types to be considered. Firstly, adverbs can occur before verbs inside some idioms. Secondly, adjectives can occur before nouns in a few idioms and function as nominal modifiers. Thirdly, adjectives in some idioms function, surprisingly, as verbal modifiers. More surprisingly, Nicolas (1995) shows that most idiom-internal adjectives function as verbal modifiers, i.e. they have the function of modifying the whole idiom or its predicate. We have reached largely the same conclusion after examining the idioms in two Korean idiom dictionaries: and Choi (2014). Although English does not seem to have examples of the first type, Korean has some. This difference may be due to the word order difference between the two languages: Korean is a head-final language, while English is a head-initial language. As an adverb occurs before the string of V-NP in English, it is not clear whether it modifies V or VP. In addition, regardless of whether it modifies V or VP, the effect is the same. Even when it modifies V, the influence will go over to the whole VP because V is the head of VP. On the other hand, an adverb can occur inside the NP-V string in Korean, which clearly shows that it modifies V. As we saw above, the Korean idiom [ei(-ka) eps-'be preposterous'] can be modified by an internal adverb such as cengmal 'really,' [ei(-ka) cengmal eps-'be really preposterous'] (cf. Chae 2014: 511). When the internal modifiers are adverbs, it is not very surprising that they have the function of modifying the whole idiom, because the modified element, i.e. V, is the head of VP. Any Compositional Phenomena? To begin with, we want to make it clear that we cannot derive the meanings of idioms from their component parts compositionally. It is a wellknown fact that we can guess the meanings of component parts only when we know the meaning of the whole idiom (cf. Gibbs 2007: 709, 717). For example, we cannot usually figure out that the meanings of spill and beans in the idiom [spill the beans] are 'divulge' and 'information,' respectively, unless we know the meaning of the whole idiom, i.e. 'divulge information.' If it is not the case, those who are learning English would predict the meaning of the idiom correctly on the basis of the (literal) meanings of spill and beans, which is very unlikely. If we can only figure out the individual meanings with reference to the meaning of the whole idiom, we do not have to worry about the meanings of individual parts from the beginning. As we all know, we need to know the meanings of individual words to compute the meaning of the whole expression. Despite the problems described above, there has been a tradition which takes it for granted that individual words in idioms have to have their own meanings. Nunberg et al. (1994: 500-3) is one of the forerunners: "modification, quantification, topicalization, ellipsis, and anaphora provide powerful evidence that the pieces of many idioms have identifiable meanings which interact semantically with other" (cf. Wasow et al. 1983;Croft & Cruse 2004: ch. 9, Gibbs 2007 It would be very difficult to account for these data if we do not assume that individual words in idioms have their own meanings. In (a-b), at least formally, a part of the idiom is modified. In (c), a part is quantified. In (d), a part is topicalized. In (ef), an anaphor or a deleted part refers to a part of the idiom concerned. Under traditional approaches, we would not be able to account for the phenomena in (8) appropriately unless we assume that individual words have their own identities. Under the spirit of Chae (2014), however, we can account for the phenomena in (c-f) easily. We are assuming that all and only idioms are represented as constructions and that constructions can represent formal flexibilities of idioms. In our analysis of the idiom in (c), the position of Det/QP is open in the construction concerned. 3 For the constructions in (d-f), the syntactic mechanisms involved, i.e. those responsible for figuring out the antecedents of gaps or anaphora, will identify the relevant entities. For example, [those strings] will be identified as the object NP of pull in (d) and yours will be identified as your goose in (e). Then, the idiom concerned will be identified with reference to the construction describing it, which is in the set of constructions. That is, the relevant construction will be invoked and, hence, its meaning as well, without recourse to the individual words involved. To be more specific about the topicalized example in (8d), it can be analyzed the same way as other topicalized sentences. Just as a regular VP which has a displaced object is analyzed as VP/NP, the idiomatic VP [pull e], which has its own idiomatic meaning and is lacking [those strings], is analyzed as VP/NP. When the missing NP, i.e. the NP value of the SLASH(/) feature, gets licensed, the whole idiom obtains its meaning from the construction concerned. This is not possible in previous non-compositional approaches because idioms are generally assumed to be word-like fixed expressions. The difficulty lies in the analysis of such data as those in (8a-b). As an adjective or a relative clause modifies a noun which is a part of the idiom, there does not seem to be an easy way of accounting for the data without assuming that all the component parts of the idiom have their meanings. However, in the next section, we will see that the data can better be analyzed without such an assumption. Idiom-internal Modification Among the three types of idiom-internal modifiers mentioned in section 3.1, we will consider how we can account for the second and third types, i.e. the behavior of idiom-internal adjectives. We will see that the framework to be developed here can handle the phenomena without assuming separate meanings of idiom parts. This implies that idiominternal modifiers are not part of the idiom. It will be also shown that the meaning of the whole expression can be obtained compositionally from that of the idiom and that of the modifier. With reference to such data as in (8a-b), Nicolas (1995: 233, 239-10) argues that the internal modification in V-NP idioms "is systematically interpretable as modification of the whole idiom." 4 (9) a. [ (11) a. 'settle a conflict in a(n) old/bloody/violent way' b. 'ancestrally reconcile a disagreement' c. 'salaciously divulge information' In the case of the idiom [bury the hatchet], the adjective official leads to an adverbial function: [bury the official hatchet] 'settle/reconcile a conflict/disagreement officially.' On the other hand, as we can see in (10a-b), the adjectives old/bloody/ violent/ancestral induce an adjectival function in the idiom. This shows that the function of an idiom-internal adjective is determined by the interactions between the adjective and the idiom within which the adjective is located. The issue, then, is how we can account for the adverbial and adjectival functions of idiom-internal adjectives without assuming that the component parts of an idiom have separate meanings. As the first step to the solution, let us examine the characteristics of the idiom [bury the hatchet] more closely. When it contains an adjective inside, the adjective can be interpreted either as adjectively or as adverbially. As we can see in (10a-b), such adjectives as old, bloody, violent and ancestral lead to an adjectival reading. In [bury the old hatchet], for example, it is clear that the adjective old combines with the noun hatchet syntactically. As an adjective, it has the right formal properties to be in a position between a determiner and a noun. However, from a semantic point of view, it is not compatible with the literal meaning of hatchet. It is compatible only with the seemingly idiomatic meaning of hatchet, i.e. 'disagreement/conflict.' We have to realize here that there is a mismatch between syntactic and semantic behavior in the combination. That is, the combination is "indirect/abnormal" rather than "direct/normal." In a direct/normal combination, on the other hand, there is no such mismatch between syntactic and semantic behavior. For example, in [the tall man], the adjective tall modifies the noun man not only syntactically but also semantically. The (literal) meaning of man is compatible with that of tall. We have to be very careful not to assume that the meaning of 'disagreement/conflict' is directly related to the hatchet in [bury the hatchet]. It comes from the argument of the meaning of the ONE)], respectively. In the former, [the bucket] has no (direct) reflections on the whole meaning. In the latter, neither [the wool] nor eyes have any direct reflections on the meaning. Hence, when we say that old in [bury the old hatchet] is compatible with "the seemingly idiomatic meaning" of hatchet, we mean that it is compatible with the argument of the whole idiomatic meaning, i.e. CONFLICT, rather than with the idiomatic non-literal meaning of hatchet itself. The indirect nature of the combination of idiominternal adjectives and their host nouns become more evident when the adjectives function as adverbials. In [bury the official hatchet 'settle the conflict officially'], for example, the adjective official combines with the noun hatchet syntactically. 5 However, from a semantic point of view, it neither combines with the literal meaning of hatchet nor the assumed idiomatic meaning of hatchet 'disagreement/conflict.' As it is not compatible even with the argument of the idiomatic meaning CONFLICT, the combination becomes more different from a regular one. As it does not have any adjectival role semantically, it is "coerced" to perform an adverbial role (with the addition of a semantic adverbializer, which can be regarded as a counterpart of the formal -ly ending). 6 Now the adjective can combine with the 5 Although it has an adverbial function, the word official in [bury the official hatchet] is still an adjective. It is an adjective because it shows the same syntactic distribution as regular adjectives. Notice that syntactic categories are primarily determined by syntactic distribution. All idiom-internal elements keep their formal identities in our approach, regardless of their functions. 6 The term "coercion" can be defined as follows (Culicover 2009: 472): "an interpretation that is added to the normal interpretation of a word as a consequence of the syntactic configuration in which it appears." Typical cases of coercion are exemplified in the sentence [the ham sandwich over in the corner wants another coffee], which can be paraphrased as [the person contextually associated with a ham sandwich over in the corner wants another cup of coffee] (Culicover & Jackendoff 2005: 227-8;cf. Nunberg 1979, whole idiomatic meaning or its predicate, i.e. SETTLE. On the basis of the observations above, we conclude that idiom-internal adjectives and their host nouns do not have direct relationships. Although their combinations are regular formally, the adjective does not combine with its host noun semantically. This means that the adjective is not part of the idiom concerned, and more importantly that the component parts of idioms do not have to have their own separate meanings. We can conceptualize the licensing of idiominternal adjectives as follows. Formally, the adjective is licensed as far as it satisfies the morphological and distributional properties required in the position. For example, in [bury the old/official hatchet] the words old and official, as adjectives, satisfy the requirements for being in the position between a determiner and a noun. Under our framework, we just need to leave the NP dominating [the hatchet] not enclosed with /…/, to indicate that this idiom allows an internal adjective. Semantically, the adjective is licensed when it has a meaning which is compatible either with an argument of the whole idiomatic meaning or with the whole meaning or its predicate. In the former case, the adjective leads to an adjectival function. In the latter case, on the other hand, it leads to an adverbial function. As for the semantic licensing of [bury the old hatchet], we have to check first whether the meaning of old, say OLD, is compatible with an argument of the whole idiomatic meaning, i.e. [SETTLE ([ ], CONFLICT)]. As OLD is compatible with the argument CONFLICT, the whole expression has the meaning of 'settle an old conflict.' Now, turning to the semantic licensing of [bury the official hatchet], we need to check the compatibility of OFFICIAL with CONFLICT. As this is not a normal combination, there have to be other possibilities. We are assuming that, at this point, the adjective is coerced to have an adverbial meaning. Then, we need to check whether the coerced adverbial meaning of OFFICIAL, say OFFICIALLY, 7 is compatible with the idiomatic Ward 2004). The underlined parts are coerced interpretations. 7 We can represent the coerced adverbial meaning of the adjective concerned with a pattern of the following (cf. footnote 10): [in the viewpoint/manner/… of being AdjP]. According to Nicolas (1995: 249), "the most commonly available kind of internal modification is … viewpoint modification, … about 85% …" Then, the expression [bury the official hatchet] would be meaning or its predicate, i.e. SETTLE. As this combination is fine, the whole expression can have the meaning of 'settle a conflict officially.' Of course, there would be cases where both types of combinations are possible. In such cases, the expressions concerned would be ambiguous between an adjectival reading and an adverbial reading. We can see a similar phenomenon of indirect combination in some non-idiomatic phrases: 8 (12) a. We [had a quick cup of coffee] before lunch. b. He had to find [a fast road] to get there in time. The underlined adjectives quick and fast are positioned between a determiner and a noun and, hence, they are in the right positions. However, they have meanings which cannot be combined with the meanings of their host nouns. The expressions in the square brackets mean roughly 'drank a cup of coffee quickly' and 'a road where we can drive fast,' respectively. A cup cannot be quick and a road itself, if it is not a moving road, cannot be fast. We can see that the adjectives here are used adverbially (with an appropriate amount of coercion), just like those in idioms. From these examples of indirect combination, 9 we can see that our assumptions about the indirect combination in idioms are not unmotivated. In this section, we have provided a framework to account for the behavior of idiom-internal adjectives without assuming separate meanings of the parts of idioms. We have seen that the meaning of an idiom containing an internal adjective can be obtained from that of the idiom and that of the modifier compositionally. Although the combination is not direct as in regular phrases, it is not random but follows a general pattern 10 of what interpreted as 'settle a conflict in the viewpoint/manner of being official.' 8 The data in (12) were brought up to me by Jeehoon Kim (p.c.). 9 One might assume that [have a cup of coffee] is a kind of idioms, probably due to the "lightness" of the verb have. If so, the combination of this idiom and quick can be accounted for with the same mechanisms as those for idioms. However, [a road] does not have any properties of idioms. 10 According to Culicover & Jackendoff (2005: 228), there is a consensus in the literature that coerced interpretations are "the product of auxiliary principles of interpretation … they contribute material that makes the we call "indirect combination." Hence, we can conclude that idiom-internal adjectives are not part of the idiom. That is, they should not be a part of the idiom concerned. The only thing we need to do with the idiom is to keep the NP containing the host noun not enclosed with /…/. Conclusion In this paper, we have introduced a framework to represent the syntactic flexibility of idioms. Under this framework, we have examined some phenomena which seem to be accounted for only by assuming separate meanings of the parts of idioms. However, we have shown that we can account for all the phenomena without such an assumption. This is accomplished by positing the set of constructions as a major component of grammar and by capturing the indirect nature of the combination between idiom-internal adjectives and their host idioms. Focusing on the behavior of idiom-internal adjectives, we have conceptualized a framework to account for the indirectness in the combination of adjectives and their host idioms. By elucidating the nature of this combination, we are absolved from the almost impossible task, especially from a computational point of view, of assigning separate meanings to the component parts of idioms. Consequently, we came to prove that idioms are formally flexible and semantically non-transparent. If we could not figure out that idiom-internal modifiers are not part of idioms, we would not have reached the conclusion that idioms are not transparent/compositional. d. i) *Bill ate his [own/inner heart] out.ii) *We were laughing our [two heads] off.The examples in (a) and (b) show ICE-like properties. On the other hand, those in (c) and (d) show their IP-like properties. In addition, Those strings, he wouldn't pull for you. e. My goose is cooked, but yours isn't ___. f. Although the FBI kept taps on Jane Fonda, the CIA kept them on Vanesa Redgrave.). (8) a. [kick the filthy habit] b. Pat got the job by [pulling strings that weren't available to anyone else]. c. [touch a couple of nerves] d. make rapid headway] 'progress rapidly' b. [be at a temporary loose end] 'be unoccupied temporarily' c. [pull no strings] 'do not exert influence'In these examples all the idiom-internal adjectives are interpreted as adverbials. It seems to be true that most of the adjectives in idioms have the function of modifying the whole idiom.However, there are some examples where the idiom-internal adjective does not have an adverbial function, including those in (8a-b).(10) a. [bury the old/bloody/violent hatchet] 'settle an old/bloody/violent conflict' b. [bury the ancestral hatchet] 'reconcile an ancestral disagreement' c. [spill the salacious beans] 'divulge the salacious information' In all these examples, the underlined adjectives have the function of modifying some nominal elements of the meanings of the whole idiom. Please note that they do not have the following meanings: If different determiners and/or quantifiers allowed in the idiom result in different meanings, such data could be handled with the mechanisms for idiom-internal adjectives in section 3.3. Nicolas (1995: 244, 249) even argues that he could not find any counter-examples to the adverbial function of adjectives in his chosen corpus of fifty million words. AcknowledgmentsI appreciate the comments from the reviewers of PACLIC 28 and PACLIC 29. I also express my sincere appreciations to Jeehoon Kim and the reviewers of BCGL 8 (The 8 th Brussels Conference on Generative Linguistics: The Grammar of Idioms). This work was supported by the 2015 research fund of Hankuk University of Foreign Studies. A Representational System of Idiomatic Constructions: For the Building of Computational Resources. Hee-Rahk Chae, Linguistic Research. 31Chae, Hee-Rahk. 2014. 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M Espinal, Jaume Teresa, Mateu, Journal of Pragmatics. 42Espinal, M. Teresa, and Jaume Mateu. 2010. On Classes of Idioms and Their Interpretation. Journal of Pragmatics, 42: 1397-1411. Idioms: Structural and Psychological Perspectives. Martin Everaert, Erik-Jan Van Der Linden, Andre Schenk, and Rob SchreuderLawrence Erlbaum Associates, IncEveraert, Martin, Erik-Jan van der Linden, Andre Schenk, and Rob Schreuder, eds. 1995. Idioms: Structural and Psychological Perspectives. Lawrence Erlbaum Associates, Inc. Regularity and Idiomaticity in Grammatical Constructions: The Case of let alone. Charles J Fillmore, Paul Kay, Mary C O&apos;connor, Language. 64Fillmore, Charles J., Paul Kay and Mary C. O'Connor. 1988. Regularity and Idiomaticity in Grammatical Constructions: The Case of let alone. Language, 64: 501-538. Specialization and Reinterpretation in Idioms. Dirk Geeraerts, Everaert et al.Geeraerts, Dirk. 1995. Specialization and Reinterpretation in Idioms. In Everaert et al. (1995). 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A Dictionary of Basic Idioms on Grammatical Principles. written in KoreanNo, Yongkyoon. 2002. A Dictionary of Basic Idioms on Grammatical Principles [written in Korean]. . Hankookmunhwasa, Hankookmunhwasa. The Pragmatics of Reference. Geoffrey Nunberg, BloomingtonIndiana Univ. LinguisticsNunberg, Geoffrey. 1978. The Pragmatics of Reference. Indiana Univ. Linguistics, Bloomington. The Nonuniqueness of Semantic Solutions: Polysemy. Geoffrey Nunberg, Linguistics and Philosophy. 3Nunberg, Geoffrey. 1979. The Nonuniqueness of Semantic Solutions: Polysemy. Linguistics and Philosophy 3: 143-184. . Geoffrey Nunberg, Ivan Sag, Thomas Wasow, Idioms. Language. 70Nunberg, Geoffrey, Ivan Sag, and Thomas Wasow. 1994. Idioms. Language, 70: 491-538. Multiword Expressions: A Pain in the Neck for NLP. Ivan Sag, Timothy Baldwin, Francis Bond, Ann Copestake, Dan Flickinger, Sag, Ivan, Timothy Baldwin, Francis Bond, Ann Copestake, and Dan Flickinger. 2002. 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Automatic Discrimination of Human and Neural Machine Translation: A Study with Multiple Pre-Trained Models and Longer Context

We address the task of automatically distinguishing between human-translated (HT) and machine translated (MT) texts. Following recent work, we fine-tune pretrained language models (LMs) to perform this task. Our work differs in that we use state-of-the-art pre-trained LMs, as well as the test sets of the WMT news shared tasks as training data, to ensure the sentences were not seen during training of the MT system itself. Moreover, we analyse performance for a number of different experimental setups, such as adding translationese data, going beyond the sentencelevel and normalizing punctuation. We show that (i) choosing a state-of-the-art LM can make quite a difference: our best baseline system (DEBERTA) outperforms both BERT and ROBERTA by over 3% accuracy, (ii) adding translationese data is only beneficial if there is not much data available, (iii) considerable improvements can be obtained by classifying at the document-level and (iv) normalizing punctuation and thus avoiding (some) shortcuts has no impact on model performance.

Automatic Discrimination of Human and Neural Machine Translation: A Study with Multiple Pre-Trained Models and Longer Context Tobias Van Der Werff t.n.van.der.werff@student.rug.nl Bernoulli Institute University of Groningen CLCG University of Groningen CLCG University of Groningen Rik Van Noord rikvannoord@gmail.com Bernoulli Institute University of Groningen CLCG University of Groningen CLCG University of Groningen Antonio Toral Bernoulli Institute University of Groningen CLCG University of Groningen CLCG University of Groningen Automatic Discrimination of Human and Neural Machine Translation: A Study with Multiple Pre-Trained Models and Longer Context We address the task of automatically distinguishing between human-translated (HT) and machine translated (MT) texts. Following recent work, we fine-tune pretrained language models (LMs) to perform this task. Our work differs in that we use state-of-the-art pre-trained LMs, as well as the test sets of the WMT news shared tasks as training data, to ensure the sentences were not seen during training of the MT system itself. Moreover, we analyse performance for a number of different experimental setups, such as adding translationese data, going beyond the sentencelevel and normalizing punctuation. We show that (i) choosing a state-of-the-art LM can make quite a difference: our best baseline system (DEBERTA) outperforms both BERT and ROBERTA by over 3% accuracy, (ii) adding translationese data is only beneficial if there is not much data available, (iii) considerable improvements can be obtained by classifying at the document-level and (iv) normalizing punctuation and thus avoiding (some) shortcuts has no impact on model performance. Introduction Generally speaking, translations are either performed manually by a human, or performed automatically by a machine translation (MT) system. There exist many use cases in Natural Language Processing in which working with a humantranslated text is not a problem, as they are usually of high quality, but in which we would like to filter out automatically translated texts. For example, consider training an MT system on a parallel corpus crawled from the Internet: we would preferably only keep the high-quality human-translated sentences. In this paper, we will address this task of discriminating between human-translated (HT) and machine-translated texts automatically. Studies that have analysed MT outputs and HTs comparatively have found evidence of systematic differences between the two (Ahrenberg, 2017; Vanmassenhove et al., 2019;Toral, 2019). These outcomes provide indications that an automatic classifier should in principle be able to discriminate between these two classes, at least to some extent. There is previous related work in this direction (Arase and Zhou, 2013;Aharoni et al., 2014;Li et al., 2015), but they used Statistical Machine Translation (SMT) systems to get the translations, while the introduction of Neural Machine Translation (NMT) has considerably improved general translation quality and has led to more natural translations (Toral and Sánchez-Cartagena, 2017). Arguably, the discrimination between MT and HT is therefore more difficult with NMT systems than it was with previous paradigms to MT. We follow two recent publications that have attempted to distinguish NMT outputs from HTs (Bhardwaj et al., 2020;Fu and Nederhof, 2021) and work with MT outputs generated by state-of-the-art online NMT systems. Additionally, we also build a classifier by fine-tuning a pre-trained language model (LM), given the fact that this approach obtains state-of-the-art performance in many text-based classification tasks. The main differences with previous work are: • We experiment with state-of-the-art LMs, instead of only using BERT-and ROBERTAbased LMs; • We empirically check the performance impact of adding translationese training data; • We go beyond sentence-level by training and testing our best system on the documentlevel; • We analyse the impact of punctuation shortcuts by normalizing the input texts; • We use the test sets of WMT news shared task as our data sets, to ensure reproducibility and that the MT system did not see the translations during its training. The rest of the paper is organised as follows. Section 2 outlines previous work on the topic. Section 3 details our methodology, focusing on the data sets, classifiers and evaluation metrics. Subsequently, Section 4 presents our experiments and their results. These are complemented by a discussion and further analyses, in Section 5. Finally, Section 6 presents our conclusions and suggestions for future work. All our data, code and results is publicly available at https://github.com/ tobiasvanderwerff/HT-vs-MT Related Work Analyses Previous work has dealt with finding systematic and qualitative differences between HT and MT. Ahrenberg (2017) compared manually an NMT system and a HT for one text in the translation direction English-to-Swedish. They found that the translation by NMT was closer to the source and exhibited a more restricted repertoire of translation procedures than the HT. Related, an automatic analysis by Vanmassenhove et al. (2019) found that translations by NMT systems exhibit less lexical diversity than HTs. A contemporary automatic analysis corroborated the finding about less lexical diversity and concluded also that MT led to translation that had lower lexical density, were more normalised and had more interference from the source language (Toral, 2019). SMT vs HT classification Given these findings, it is no surprise that automatic classification to discriminate between MT and HT has indeed been attempted in the past. Most of this work targets SMT since it predates the introduction of NMT and uses a variety of approaches. For example, Arase and Zhou (2013) relied on fluency features, while Aharoni et al. (2014) used part-of-speech tags and function words, and Li et al. (2015) parse trees, density and out-of-vocabulary words. Their methods reach quite high accuracies, though indeed rely on SMT systems, which are of considerable lower quality than the current NMT ones. NMT vs HT classification To the best of our knowledge only two publications have tackled this classification with the state-of-the-art paradigm, NMT (Bhardwaj et al., 2020;Fu and Nederhof, 2021). We now outline these two publications and place our work with respect to them. Bhardwaj et al. (2020) work on automatically determining if a French sentence is HT or MT, with the source sentences in English. They test a variety of pre-trained language models, either multilingual -XLM-R (Conneau et al., 2020) and mBERT (Devlin et al., 2019a)-or monolingual for French: CamemBERT (Martin et al., 2020) and FlauBERT . Moreover, they test their trained models across different domains and MT systems used during training. They find that pre-trained LMs can perform this task quite well, with accuracies of over 75% for both in-domain and cross-domain evaluation. Our work follows theirs quite closely, though there are a few important differences. First, we use publicly available WMT data, while they use a large private data set, which unfortunately limits reproducibility. Second, we analyze the impact of punctuation-type "shortcuts", while it is unclear to what extent this gets done in Bhardwaj et al. (2020). 1 Third, we also test our model on the document-level, instead of just the sentence-level. Fu and Nederhof (2021) work on the WMT18 news commentary data set for translating Czech, German and Russian into English. By fine-tuning BERT they obtain an accuracy of 78% on all languages. However, they use training sets from WMT18, making it highly likely that Google Translate (which they use to get the translations) has seen these sentences during training. 2 This means that the MT outputs they get are likely of higher quality than it would be the case in a real-world scenario, and thus closer to HT, which would make the task unrealistically harder for the classifiers. On the other hand, an accuracy of 78% is quite high on this challenging task, so perhaps this is not the case. This accuracy might even be suspiciously high: it could be that the model overfit on the Google Translations, or that the data contains artifacts that the model uses as a shortcut. Original vs MT Finally, there are three related works that attempt to discriminate between MT and original texts written in a given language, rather than human translations as is our focus. Method Data We will experiment with the test sets from the WMT news shared tasks. 3 We choose this data set mainly for these four reasons: (i) it is publicly available so it guarantees reproducibility; (ii) it has the translation direction annotated, hence we can inspect the impact of having original text or human-translated text (i.e. translationese) in the source side; (iii) the data sets are also available at the document-level, meaning we can train and evaluate systems that go beyond sentencelevel; (iv) these sets are commonly used as test sets, so it is unlikely that they are used as training data in online MT systems, which we use in our experiments. We will use the German-English data sets, and will focus on the translation direction German-to-English. This language pair has been present the longest at WMT's news shared task, from 2008 till the present day. Hence, it is the language pair 361 0 15 0 WMT09 432 448 17 21 WMT10 500 505 15 22 WMT11 601 598 16 18 WMT12 611 604 14 18 WMT13 500 500 7 9 WMT14 1,500 Translationese For each of these sets, roughly half of the data was originally written in our source language (German) and human-translated to our target language (English), while the other half was originally written in our target language (English) and translated to our source language (German) by a human translator. We thus make a distinction between text that originates from text written in the source language (German), and text that originates from a previous translation (i.e. English to German). We will refer to the latter as translationese. Data set # SNT O # SNT T # DOC O # DOC T WMT08 Half of the data can thus be considered a different category: the source sentences are actually not original, but a translation, which means that the machine-translated output will actually be an automatic translation of a human translation, instead of an automatic translation of original text. In that part of the data, the texts in the HT category are not human translations of original text, but the original texts themselves. Since this data might exhibit different characteristics, given that the translation direction is the inverse, we only use the sentences and documents that were originally written in German for our dev and test sets (indicated with O in Table 1). Moreover, we empirically evaluate in Section 4 whether removing the extra translationese data from the training set is actually beneficial for the classifier. MT Since we are interested in contrasting HT vs state-of-the-art NMT, we automatically translate the sentences using a general-purpose and widely used online MT system, DeepL. 4 We translate from German to British English, 5 specifically. We use this MT system for the majority of our experiments, though we do experiment with crosssystem classification by testing on data that was translated with other MT systems, such as Google Translate, using their paid API. 6 We manually went through a subset of the translations by both DeepL and Google Translate and indeed found them to be of high quality. To be clear, in our experiments, the machine translations actually double the size of the train, dev and test sets as indicated in Table 1. For each German source sentence, the data set now contains a human translation (HT, taken from WMT) and a machine translated variant (MT, from DeepL or Google), which are labelled as such. As an example, if we train on both the original and translationese sentence-level data of WMT08-17, we actually train on 8, 242 · 2 + 8, 593 · 2 = 33, 670 instances. Note that this also prevents a bias in topic or domain towards either HT or MT. Ceiling To get a sense of what the upper ceiling performance of this task will be, we check the number of cases where the machine translation is the exact same as the human translation. For DeepL, this happened for 3.0% of the WMT08-17 training set sentences, 3.1% of the dev set and 3.9% of the test set. For Google, the percentages are 2.4%, 2.0% and 3.5%, respectively. 7 Of course, in practice, it is likely impossible to get anywhere near this ceiling, as the MT system also sometimes offers arguably better translations (see Section 5 for examples). 4 https://www.deepl.com/translator -used in November 2021. 5 DeepL forces the user to choose a variety of English (either British or American). This implies that the MT output could be expected to be (mostly) British English while the HT is a mix of both varieties. Hence, one could argue that variety is an aspect that could be picked up by the classifier. We also use Google Translate, which does not allow the user to select an English variety. 6 We noticed that the free Python library googletrans had clearly inferior translations. The paid APIs for Google and DeepL obtain COMET (Rei et al., 2020) scores of 59.9 and 61.9, respectively, while the googletrans library obtains 21.0. 7 If we apply a bit more fuzzy matching by only keeping ascii letters and numbers for each sentence, the percentages go up by around 0.5%. Parameter Range Learning rate 5 × 10 −6 , 10 −5 , 3 × 10 −5 Batch size {32, 64} Warmup {0.06} Label smoothing {0.0, 0.1, 0.2} Dropout {0.0, 0.1} Classifiers SVM We will experiment with a number of different classifiers. As a baseline model, we use a linear SVM with unigrams and bigrams as features trained with scikit-learn (Pedregosa et al., 2011), for which the data is tokenized with Spacy. 8 The use of a SVM is mainly to find out how far we can get by just looking at the superficial lexical level. It also allows us to identify whether the classifier uses any shortcuts, i.e. features that are not necessarily indicative of a human or machine translation, but due to artifacts in the data sets, which can still be picked up as such by our models. An example of this is punctuation, which was mentioned in previous work (Bhardwaj et al., 2020). MT systems might normalize uncommon punctuation, 9 while human translators might opt for simply copying the originally specified punctuation in the source sentence (e.g. quotations, dashes). We analyse the importance of normalization in Section 5. Fine-tuning LMs Second, we will experiment with fine-tuning pre-trained language models. 10 Fu and Nederhof (2021) only used BERT (Devlin et al., 2019b) and Bhardwaj et al. (2020) used a set of BERT-and ROBERTA-based LMs, but there exist newer pre-trained LMs that generally obtain better performance. We will empirically decide the best model for this task, by experimenting with a number of well-established LMs: BERT (Devlin et al., 2019b), RoBERTa (Liu et al., 2019), DeBERTa (He et al., 2021b;He et al., 2021a), XLNet (Yang et al., 2019), BART (Lewis et al., 2020) and Longformer (Beltagy et al., 2020). For all these models, we only tune the batch size and learning rate. The Acc. best model from these experiments is then tuned further (on the dev set). We tune a single parameter at a time and do not perform a full grid search due to efficiency and environmental reasons. Hyperparameter settings and range of values experimented with are shown in Table 2. Evaluation We evaluate the models looking at the accuracy and F1-score. When standard deviation is reported, we averaged over three runs. For brevity, we only report accuracy scores, as we found them to correlate highly with the Fscores. We include additional metrics, such as the F-scores, on our GitHub repository. Experiments SVM The SVM classifier was trained on the training set WMT08-17 O (i.e. part of the data set with original source side), where the MT output was generated with DeepL. It obtained an accuracy of 57.8 on dev and 54.9 on the test set. This is in line with what would be expected: there is some signal at the lexical level, but other than that the task is quite difficult for a simple SVM classifier. Finding the best LM As previously indicated, we experimented with a number of pre-trained LMs. For efficiency reasons, we perform these experiments with a subset of the training data (WMT14-17 O , i.e. with only translations from original text). The results are shown in Table 3. We find the best performance by using the DeBERTa-v3 model, which quite clearly outperformed the other LMs. We obtain a 6.7 point absolute increase in accuracy over BERT (61.9 to 68.6), the LM used by Fu and Nederhof (2021)), and a 3.7 point increase over the second best performing model, BART-large. We tune some of the remaining hyperparameters further (see Table 2) and obtain an accuracy of 68.9. We will use this model in our next experiments. (Ng et al., 2019) 62.6 ± 1.9 57.7 ± 1.8 RWTH (Rosendahl et al., 2019) 61.9 ± 1.5 58.3 ± 1.8 PROMT (Molchanov, 2019) 50.3 ± 0.9 52.1 ± 3.3 online-X 57.5 ± 1.1 56.6 ± 3.4 Table 4: Test set scores (all in %) for training and testing our best DEBERTA across different MT-systems (DeepL and Google) and 4 WMT19 submissions. online-X refers to an anonymous online MT system evaluated at WMT19. Cross-system performance A robust classifier that discriminates between HT and MT should not only recognize MT output that is produced by a particular MT system (the one the classifier is trained on), but should also work across different MT systems. Therefore, we test our DeepL-trained classifier on the translations of Google Translate (instead of DeepL) and vice versa. In this experiment we train the classifier on all the training data (i.e. WMT08-17 O+T ) and evaluate on the test set. In Table 4, we find that this cross-system evaluation leads to quite a drop in accuracy: 2.3% for DeepL and even 8.6% for Google. It seems that the classifier does not just pick up general features that discriminate between HTs and NMT outputs, but also MT-system specific features that do not always transfer to other MT systems. In addition, we test both classifiers on a set of MT systems submitted to WMT19. We pick the two top and two bottom submissions according to the human evaluation (Barrault et al., 2019). The motivation is to find out how the classifiers perform on MT outputs of different levels of translation quality. We also notice a considerable drop in performance here. Interestingly, the classifiers perform best on the high-quality translations of FAIR and RWTH (81.6 and 81.5 human judgment scores at WMT19, respectively), and perform considerably worse on the two bottom-ranked WMT19 systems (71.8 and 69.7 human judgment scores). It seems that the classifier does not learn to recognize lower-quality MT outputs if it only saw higherquality ones during training. This inability to deal with lower-quality MT when trained only on high-quality MT seems counterintuitive and was quite surprising to us. After all, the difference between high-quality MT and human translation tends to be more subtle than in the case of low-quality MT. However, WMT08-17 T 63.7 ± 0.8 59.5 ± 0.3 Table 5: Dev and test scores for training our best DEBERTA model on either WMT14-17 or WMT08-17 translated with DeepL, compared with training on the same data sets but not adding the translationese data (T ) and only using T . the learned features most useful for distinguishing high-quality MT from HT are likely different in nature than the features that are most useful for distinguishing low-quality MT from HT (e.g., simple lexical features versus features related to word ordering). From this perspective, feeding low-quality MT to a system trained on highquality MT can be seen as an instance of out-ofdistribution data that is not modelled well during the training stage. Nevertheless, this featural discrepancy could likely be resolved by supplying additional examples of low-quality MT to the classifier at training time. Removing translationese data In our previous experiment we used the full training data (i.e. WMT08-17 O+T ). However, most of the WMT data sets only consist for 50% of sentences that were originally written in German; the other half were originally written in English (see Section 3.1). We ask the question whether this additional data (which we refer to as translationese) is actually beneficial to the classifier. On the one hand, it is in fact a different category than human translations from original text. On the other, its usage allows us to double the amount of training data (see Table 1). In Table 5 we show that the extra data helps if there is not much training data available (WMT14-17), but that this effect disappears once we increase the amount of training data (WMT08-17). In fact, the translationese data seems to be clearly of lower quality (for this task), since a model trained on only this data (WMT08-17 T ), which is of the same size as the WMT08-17 O experiments, results in quite a drop in accuracy (59.5 vs 66.3 on the test set). We have also experimented with pretraining on WMT08-17 O+T and then fine-tuning on WMT08-17 O . Our initial results were mixed, but we plan on investigating this in future work. Beyond sentence-level In many practical usecases, we actually have access to full documents, and thus do not have to restrict ourselves to looking at just sentences. This could lead to better performance, since certain problems of NMT systems only come to light in a multi-sentence setting (Frankenberg-Garcia, 2021). Since WMT also contains document-level information, we can simply use the same data set as before. Due to the number of instances being very low at document level (see Table 1), and to the fact that the addition of translationese data showed to be beneficial with limited amounts of training data (see Table 5), we use all the data available for our document-level experiments, i.e. WMT08-17 O+T . We have four document-level classifiers: (i) a SVM, similar to the one used in our sentence-level experiments, but for which each training instance is a document; (ii) majority voting atop our best sentence-level classifier, DEBERTA, i.e. we aggregate its sentence-level predictions for each document by taking the majority class; (iii) DEBERTA fine-tuned on the document-level data, truncated to 512 tokens; and (iv) Longformer (Beltagy et al., 2020) fine-tuned on the document-level data, as this LM was designed to handle documents. For document-level training, we use gradient accumulation and mixed precision to avoid out-ofmemory errors. Additionally, we truncate the input to 512 subword tokens for the DEBERTA model. For the dev and test set, this means discarding 11% and 2% of the tokens per document on average, respectively. 11 A potential approach for dealing with longer context without resorting to truncation is to use a sliding window strategy, which we aim to explore in future work. The results are presented in Table 6. First, we observe that the document-level baselines obtain, as expected, better accuracies than their sentencelevel counterparts (e.g. 60.7 vs 54.9 for SVM and 72.5 vs 66.1 for DEBERTA on test). Second, we observe large differences between dev and test, as well as large standard deviations. The instability of the results could be due, to some extent, to the low number of instances in these data sets (138 and 290, as shown in Table 1). Moreover, the test set is likely harder in general than the dev set, since it on average has fewer sentences per document (13.8 vs 21.7). Table 6: Accuracies of training and evaluating on documentlevel DeepL and Google data. For DEBERTA, we try two versions: a sentence-level model applied to each sentence in a document followed by majority classification (mc), and a model trained on full documents (truncated to 512 tokens). Discussion & Analysis Thus far we have reported results in terms of an automatic evaluation metric: classification accuracy. Now we would like to delve deeper by conducting analyses that allow us to obtain further insights. To this end, we exploit the fact that the SVM classifier outputs the most discriminative features for each class: HT and MT. Punctuation Normalization In this first analysis we looked at the best features of the SVM to find out whether there is an obvious indication of "shortcuts" that the pre-trained language models can take. The best features for both HT and MT are shown in Table 8. For comparison, we also show the best features after applying Moses' (Koehn et al., 2007) punctuation normalization, 12 which is commonly used as a preprocessing step when training MT systems. Indeed, there are punctuation-level features that by all accounts should not be indicative of either class, but still show up as such. The backtick (`) and dash symbol (-) show up as the best unigram features indicating HT, but are not present after the punctuation is normalized. Now, to be clear, one might make a case of still including these features in HT vs MT experiments. After all, if this is how MT sentences can be spotted, why should we not consider them? On the other hand, the shortcuts that work for this particular data set and MT system (DeepL) might not work for texts in different domains or texts that are translated by different MT systems. Moreover, the shortcuts might obscure an analysis of the more interesting differences between human and machine translated texts. In any case, we want to determine the impact of punctuation-level shortcuts by comparing the original scores versus the scores of our classifiers trained on punctuation-normalized texts. The results of our baseline and best sentence-and document-level systems with and without normalization are shown in Table 7. We observe that, even if the two best unigram features were initially punctuation, normalizing does not affect performance in a major way. There is even a small increase in performance for and Longformer, though likely not significant. Unigram Analysis In our second analysis we manually went through the data set to analyse the 10 most indicative unigram features for MT (before normalization). 13 Interestingly, some are due to errors by the human translator: the MT system correctly used schoolyard instead of the split school yard, and it also used the correct name Olympiakos Piraeus instead of the incorrect Olypiacos Piraeus (typo in the first word). Some are indeed due to a different (and likely better) lexical choice by the human translator, though the translation is not necessarily wrong: competing gang instead of rival gang, espionage scandal instead of spy affair, judging panel instead of jury and radiation instead of rays. Finally, the feature disclosure looks to be an error on the MT side. It occurs a number of times in the machinetranslated version of a news article discussing Wikileaks, in which the human translator chose the correct Wikileaks publication instead of Wikileaks disclosure and whistleblower activists instead of disclosure activists. For the best unigrams indicative of HT, there are some signs of simplification by the MT system. It never uses nearly or anticipate, instead generally opting for almost and expected. Similarly, human translators sometimes used U.S. to refer to the United States, while the MT system always uses US. The fact that we used British English for the DeepL translations might also play a role: program is indicative for HT since the MT system generally used programme. Conclusions In this paper we trained classifiers to automatically distinguish between human and machine translations for German-to-English. Our classifiers are built by pre-training state-of-the-art language models. We use the test sets of the WMT shared tasks, to ensure that the machine translation systems we use (DeepL and Google) did not see the data already during training. Throughout a number of experiments, we show that: (i) the task is quite challenging, as our best sentence-level systems obtain around 65% accuracy, (ii) using translationese data during training is only beneficial if there is limited data available, (iii) the accuracy drops considerably when performing cross MT-system evaluating, (iv) accuracy improves when performing the task on the document-level and (v) normalizing punctuation (and thus avoiding certain shortcuts) does not have an impact on model performance. In future work, we aim to do a number of things. For one, we want to experiment with both translation directions and different source languages instead of just German. Second, we want to perform cross-domain experiments (as in Bhardwaj et al. (2020)), as we currently only looked at news texts. 14 Third, we want to look at the effect of the source language: does a monolingual model that is trained on English translations from German still work on translations into English from different source languages? This can shed on light on the question in what sense general source language-independent features that discriminate between HT and MT are actually identified by the model. Fourth, we plan to also use the source sentence, with a multilingual pre-trained LM, following Bhardwaj et al. (2020). This additional information is expected to lead to better results. While the source sentence is not always available, there are real-world cases in which it is, e.g. filtering crawled parallel corpora. Fifth, we would like to expand the task to a 3-way classification, as in the least restrictive scenario, given a text in a language, it could be either originally written in that language, human translated from another language or machine translated from another language. Nguyen-Son et al. (2019a) tackles this by matching similar words within paragraphs and subsequently estimating paragraph-level coherence. Nguyen-Son et al. (2019b) approaches this task by roundtrip translating original and machine-translated texts and subsequently using the similarities between the original texts and their round-trip translated versions. Nguyen-Son et al. (2021) extends the former work improving the detection of MT even if a different system is used. Table 1 : 1Statisticsof the data sets. # SNT stands for number of sentences, # DOC for number of documents, O for number of sentences or documents in which the source side is original, while T stands for translationese. WMT08-17 and WMT14- 17 indicate the sizes of the two training sets used. with the most test data available. We use 2008 to 2017 as training, 2018 as dev and 2019 as test. Full statistics are shown in Table 1. Table 2 : 2Hyperparameter range and final values (bold) for our final DEBERTA models. Hyperparameters not included are left at their default value. Table 3 : 3Bestdevelopment set results (all in %) for MT vs HT classification for a number of pre-trained LMs. On the test set, DEBERTA-v3-large (optim) obtains an accuracy of 66.1. Table 7 : 7Test set accuracies of training and evaluating on sentence-level and document-level data on either the original or normalized (by Moses) input texts, translated with DeepL. Table 8 : 8Best features (1-gram and 2-gram models) in the SVM classifier per class, before and after normalizing punctuation. © 2022 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND. They do apply 12 conservative regular expressions, but, as there is no code available, it is unclear what these are and what impact this had on their results. 2 This likely does not apply toBhardwaj et al. (2020), as they use a private data set. For example, https://www.statmt.org/wmt20/ translation-task.html https://spacy.io/ 9 The normalisation of the punctuation as a pre-processing step when training an MT system is a widespread technique, so that e.g. «, », ′′ , " and " are all converted to e.g. ′′ . 10 Implemented using HuggingFace(Wolf et al., 2020). The median subword token count in the HT document-level data is 376, with a minimum of 47 and maximum of 3,254. Of course, since we only look at unigrams here, and the performance of the sentence-level SVM is not very high anyway, all these features have in common that they do not necessarily generalize to other domains or MT-systems. Note that this domain has a real-world application: the detection of fake news, given the fact that MT could be use to spread such news in other languages (Bonet-Jover, 2020). AcknowledgementsThe authors received funding from the European Union's Connecting Europe Facility 2014-2020 -CEF Telecom, under Grant Agreement No. INEA/CEF/ICT/A2020/2278341 (MaCoCu). This communication reflects only the authors' views. The Agency is not responsible for any use that may be made of the information it contains. We thank the Center for Information Technology of the University of Groningen for providing access to the Peregrine high performance computing cluster. Finally, we thank all our MaCoCu colleagues for their valuable feedback throughout the project. Automatic detection of machine translated text and translation quality estimation. 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Automatic Verb Extraction from Historical Swedish Texts

Even though historical texts reveal a lot of interesting information on culture and social structure in the past, information access is limited and in most cases the only way to find the information you are looking for is to manually go through large volumes of text, searching for interesting text segments. In this paper we will explore the idea of facilitating this timeconsuming manual effort, using existing natural language processing techniques. Attention is focused on automatically identifying verbs in early modern Swedish texts (1550-1800).The results indicate that it is possible to identify linguistic categories such as verbs in texts from this period with a high level of precision and recall, using morphological tools developed for present-day Swedish, if the text is normalised into a more modern spelling before the morphological tools are applied.

Automatic Verb Extraction from Historical Swedish Texts Association for Computational LinguisticsCopyright Association for Computational LinguisticsJune 2011. 2011 Eva Pettersson eva.pettersson@lingfil.uu.se Department of Linguistics and Philology Department of Linguistics and Philology Uppsala University Swedish National Graduate School of Language Technology Uppsala University Joakim Nivre joakim.nivre@lingfil.uu.se Department of Linguistics and Philology Department of Linguistics and Philology Uppsala University Swedish National Graduate School of Language Technology Uppsala University Automatic Verb Extraction from Historical Swedish Texts Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and HumanitiesPortland, OR, USA, 24Association for Computational LinguisticsJune 2011. 2011 Even though historical texts reveal a lot of interesting information on culture and social structure in the past, information access is limited and in most cases the only way to find the information you are looking for is to manually go through large volumes of text, searching for interesting text segments. In this paper we will explore the idea of facilitating this timeconsuming manual effort, using existing natural language processing techniques. Attention is focused on automatically identifying verbs in early modern Swedish texts (1550-1800).The results indicate that it is possible to identify linguistic categories such as verbs in texts from this period with a high level of precision and recall, using morphological tools developed for present-day Swedish, if the text is normalised into a more modern spelling before the morphological tools are applied. Introduction Historical texts constitute a rich source of data for researchers interested in for example culture and social structure over time. It is however a very timeconsuming task to manually search for relevant passages in the texts available. It is likely that language technology could substantially reduce the manual effort involved and thus the time needed to access this information, by automatically suggesting sections that may be of interest to the task at hand. The interesting text segments could be identified using for example semantic features or morphological and syntactic cues in the text. This would however require natural language processing tools capable of handling historical texts, which are in many respects different from contemporary written language, concerning both spelling and syntax. Ideally, one would of course like to have tools developed specifically for the time period of interest, and emerging efforts to develop resources and tools for historical languages are therefore welcome. Despite these efforts, however, it is unlikely that we will have anything close to complete coverage of different time periods even for a single language within the foreseeable future. In this paper, we will therefore instead examine the possibility of improving information access in historical texts by adapting language technology tools developed for contemporary written language. The work has been carried out in close cooperation with historians who are interested in what men and women did for a living in the early modern Swedish society (1550-1800). We will hence focus on identifying linguistic categories in Swedish texts from this period. The encouraging results show that you may successfully analyse historical texts using NLP tools developed for contemporary language, if analysis is preceded by an orthographic normalisation step. Section 2 presents related work and characteristics of historical Swedish texts. The extraction method is defined in section 3. In section 4 the experiments are described, while the results are presented in section 5. Section 6 describes how the verb extraction tool is used in ongoing historical research. Finally, conclusions are drawn in section 7. Background Related Work There are still not many studies performed on natural language processing of historical texts. Pennacchiotti and Zanzotto (2008) used contemporary dictionaries and analysis tools to analyse Italian texts from the period 1200-1881. The results showed that the dictionary only covered approximately 27% of the words in the oldest text, as compared to 62.5% of the words in a contemporary Italian newspaper text. The morphological analyser used in the study reached an accuracy of 0.48 (as compared to 0.91 for modern text), while the part-of-speech tagger yielded an accuracy of 0.54 (as compared to 0.97 for modern text). Rocio et al. (1999) used a grammar of contemporary Portuguese to syntactically annotate medieval Portuguese texts. To adapt the parser to the medieval language, a lexical analyser was added including a dictionary and inflectional rules for medieval Portuguese. This combination proved to be successful for partial parsing of medieval Portuguese texts, even though there were some problems with grammar limitations, dictionary incompleteness and insufficient part-of-speech tagging. Oravecz et al. (2010) tried a semi-automatic approach to create an annotated corpus of texts from the Old Hungarian period. The annotation was performed in three steps: 1) sentence segmentation and tokenisation, 2) standardisation/normalisation, and 3) morphological analysis and disambiguation. They concluded that normalisation is of vital importance to the performance of the morphological analyser. For the Swedish language, Borin et al. (2007) proposed a named-entity recognition system adapted to Swedish literature from the 19th century. The system recognises Person Names, Locations, Organisations, Artifacts (food/wine products, vehicles etc), Work&Art (names of novels, sculptures etc), Events (religious, cultural etc), Measure/Numerical expressions and Temporal expressions. The named entity recognition system was evaluated on texts from the Swedish Literature Bank without any adaptation, showing problems with spelling variation, inflectional differences, unknown names and structural issues (such as hyphens splitting a single name into several entities). 1 Normalising the texts before applying the named entity recognition system made the f-score figures increase from 78.1% to 89.5%. All the results presented in this section indicate that existing natural language processing tools are not applicable to historical texts without adaptation of the tools, or the source text. Characteristics of Historical Swedish Texts Texts from the early modern Swedish period (1550-1800) differ from present-day Swedish texts both concerning orthography and syntax. Inflectional differences include a richer verb paradigm in historical texts as compared to contemporary Swedish. The Swedish language was also strongly influenced by other languages. Evidence of this is the placement of the finite verb at the end of relative clauses in a German-like fashion not usually found in Swedish texts, as in ...om man i hächtelse sitter as compared to om man sitter i häkte ("...if you in custody are" vs "...if you are in custody"). Examples of the various orthographic differences are the duplication of long vowels in words such as saak (sak "thing") and stoor (stor "big/large"), the use of of fv instead of v, as inöfver (över "over"), and gh and dh instead of the present-day g and d, as in någhon (någon "somebody") and fadhren (fadern "the father") (Bergman, 1995). Furthermore, the lack of spelling conventions causes the spelling to vary highly between different writers and text genres, and even within the same text. There is also great language variation in texts from different parts of the period. Verb Extraction In the following we will focus on identifying verbs in historical Swedish texts from the period 1550-1800. The study has been carried out in cooperation with historians who are interested in finding out what men and women did for a living in the early modern Swedish society. One way to do this would be to search for occupational titles occurring in the text. This is however not sufficient since many people, especially women, had no occupational title. Occupational titles are also vague, and may include several subcategories of work. In the material already (manually) analysed by the historians, occupation is often described as a verb with a direct object. Hence, automatically extracting and displaying the verbs in a text could help the historians in the process of finding relevant text segments. The verb extraction process developed for this purpose is performed in maximally five steps, as illustrated in figure 1. The first step is tokenisation. Each token is then optionally matched against dictionaries covering historical Swedish. Words not found in the historical dictionaries are normalised to a more modern spelling before being processed by the morphological analyser. Finally, the tagger disambiguates words with several interpretations, yielding a list of all the verb candidates in the text. In the experiments, we will examine what steps are essential, and how they are combined to yield the best results. Tokenisation Tokenisation is performed using an in-house standard tokeniser. The result of the tokenisation is a text segmented into one token per line, with a blank line marking the start of a new sentence. Historical Dictionaries After tokenisation, the tokens are optionally matched against two historical dictionaries distributed by The Swedish Language Bank: 2 • The Medieval Lexical Database A dictionary describing Medieval Swedish, containing approximately 54 000 entries from the following three books: -K.F. Söderwalls OrdbokÖfver svenska medeltids-språket, vol I-III (Söderwall, 1918) -K.F. Söderwalls OrdbokÖfver svenska medeltids-språket, vol IV-V (Söderwall, 1973) -C.J. Schlyters Ordbok till Samlingen af Sweriges Gamla Lagar (Schlyter, 1877) • Dalin's Dictionary A dictionary covering 19th Century Swedish, created from the printed version of Ordbok fver svenska språket, vol I-II by Dalin (1855). The dictionary contains approximately 64 000 entries. The dictionaries cover medieval Swedish and 19th century Swedish respectively. We are actually interested in the time period in between these two periods, but it is assumed that these dictionaries are close enough to cover words found in the early modern period as well. It should further be noticed that the electronically available versions of the dictionaries are still in an early stage of development. This means that coverage varies between different word classes, and verbs are not covered to the same extent as for example nouns. Words with an irregular inflection (which is often the case for frequently occurring verbs) also pose a problem in the current dictionaries. Normalisation Rules Since both the morphological analyser and the tagger used in the experiments are developed for handling modern Swedish written language, running a text with the old Swedish spelling preserved presumably means that these tools will fail to assign correct analyses in many cases. Therefore, the text is optionally transformed into a more modern spelling, before running the document through the analysis tools. The normalisation procedure differs slightly for morphological analysis as compared to tagging. There are mainly two reasons why the same set of normalisation rules may not be optimally used both for the morphological analyser and for the tagger. First, since the tagger (unlike the morphological analyser) is context sensitive, the normalisation rules developed for the tagger need to be designed to also normalise words surrounding verbs, such as nouns, determiners, etc. For the morphological analyser, the main focus in formulating the rules has been on handling verb forms. Secondly, to avoid being limited to a small set of rules, an incremental normalisation procedure has been used for the morphological analyser in order to maximise recall without sacrificing precision. In this incremental process, normalisation rules are applied one by one, and the less confident rules are only applied to words not identified by the morphological analyser in the previous Figure 1: Overview of the verb extraction experiment normalisation step. The tagger on the other hand is robust, always yielding a tag for each token, even in cases where the word form is not present in the dictionary. Thus, the idea of running the normalisation rules in an incremental manner is not an option for the tagger. The total set of normalisation rules used for the morphological analyser is 39 rules, while 29 rules were defined for the tagger. The rules are inspired by (but not limited to) some of the changes in the reformed Swedish spelling introduced in 1906 (Bergman, 1995). As a complement to the rules based on the spelling reform, a number of empirically designed rules were formulated, based on the development corpus described in section 4.1. The empirical rules include the rewriting of verbal endings (e.g. begäradebegärde "requested" and utvisteutvisade "deported"), transforming double consonants into a single consonant (vetta -veta "know", prövassprövas "be tried") and vice versa (upsteg -uppsteg "rose/ascended", vistevisste "knew"). Morphological Analysis and Tagging SALDO is an electronically available lexical resource developed for present-day written Swedish. It is based on Svenskt AssociationsLexikon (SAL), a semantic dictionary compiled by Lönngren (1992). The first version of the SALDO dictionary was released in 2008 and comprises 72 396 lexemes. Inflectional information conforms to the definitions in Nationalencyklopedins ordbok (1995), Svenska Akademiens ordlistaöver svenska språket (2006) and Svenska Akademiens grammatik (1999). Apart from single word entries, the SALDO dictionary also contains approximately 2 000 multi-word units, including 1 100 verbs, mainly particle verbs (Borin et al., 2008). In the experiments we will use SALDO version 2.0, released in 2010 with a number of words added, resulting in a dictionary comprising approximately 100 000 entries. When running the SALDO morphological analyser alone, a token is always considered to be a verb if there is a verb interpretation present in the dictionary, regardless of context. For example, the word för will always be analysed both as a verb (bring) and as a preposition (for), even though in most cases the prepositional interpretation is the correct one. When running the maximum five steps in the verb extraction procedure, the tagger will disambiguate in cases where the morphological analyser has produced both a verb interpretation and a non-verb interpretation. The tagger used in this study is Hun-POS (Halácsy et al., 2007), a free and open source reimplementation of the HMM-based TnT-tagger by Brants (2000). Megyesi (2008) showed that the HunPOS tagger trained on the Stockholm-Umeå Corpus (Gustafson-Capková and Hartmann, 2006) is one of the best performing taggers for Swedish texts. Experiments This section describes the experimental setup including data preparation and experiments. Data Preparation A subset of Per Larssons dombok, a selection of court records from 1638, was used as a basis for developing the automatic verb extraction tool. This text consists of 11 439 tokens in total, and was printed by Edling (1937). The initial 984 tokens of the text were used as development data, i.e. words used when formulating the normalisation rules, whereas the rest of the text was used solely for evaluation. A gold standard for evaluation was created, by manually annotating all the verbs in the text. For the verb annotation to be as accurate as possible, the same text was annotated by two persons independently, and the results analysed and compared until consensus was reached. The resulting gold standard includes 2 093 verbs in total. Experiment 1: Normalisation Rules In the first experiment we will compare morphological analysis results before and after applying normalisation rules. To investigate what results could optimally be expected from the morphological analysis, SALDO was also run on present-day Swedish text, i.e. the Stockholm-Umeå Corpus (SUC). SUC is a balanced corpus consisting of a number of different text types representative of the Swedish language in the 1990s. The corpus consists of approximately one million tokens, distributed among 500 texts with approximately 2 000 tokens in each text. Each word in the corpus is manually annotated with part of speech, lemma and a number of morphological features (Gustafson-Capková and Hartmann, 2006). Experiment 2: Morphological Analysis and Tagging In the second experiment we will focus on the combination of morphological analysis and tagging, based on the following settings: morph A token is always considered to be a verb if the morphological analysis contains a verb interpretation. tag A token is always considered to be a verb if it has been analysed as a verb by the tagger. morph or tag A token is considered to be a verb if there is a morphological verb analysis or if it has been analysed as a verb by the tagger. morph and tag A token is considered to be a verb if there is a morphological verb analysis and it has been tagged as a verb. To further refine the combination of morphological analysis and tagging, a more fine-grained disambiguation method was introduced, where the tagger is only used in contexts where the morphological analyser has failed to provide an unambiguous interpretation: morph + tag A token is considered to be a verb if it has been unambiguously analysed as a verb by SALDO. Likewise a token is considered not to be a verb, if it has been given one or more analyses from SALDO, where none of the analyses is a verb interpretation. If the token has been given both a verb analysis and a non-verb analysis by SALDO, the tagger gets to decide. The tagger also decides for words not found in SALDO. Experiment 3: Historical Dictionaries In the third experiment, the historical dictionaries are added, using the following combinations: medieval A token is considered to be a verb if it has been unambiguously analysed as a verb by the medieval dictionary. Likewise a token is considered not to be a verb, if it has been given one or more analyses from the medieval dictionary, where none of the analyses is a verb interpretation. If the token has been given both a verb analysis and a non-verb analysis by the medieval dictionary, or if the token is not found in the dictionary, the token is processed by the morphological analyser and the tagger as described in setting morph + tag. 19c A token is considered to be a verb if it has been unambiguously analysed as a verb by the 19th century dictionary. Likewise a token is considered not to be a verb, if it has been given one or more analyses from the 19th century dictionary, where none of the analyses is a verb interpretation. If the token has been given both a verb analysis and a non-verb analysis by the 19th century dictionary, or if the token is not found in the dictionary, the token is processed by the morphological analyser and the tagger as described in setting morph + tag. medieval + 19c A token is considered to be a verb if it has been unambiguously analysed as a verb by the medieval dictionary. Likewise a token is considered not to be a verb, if it has been given one or more analyses from the medieval dictionary, where none of the analyses is a verb interpretation. If the token has been given both a verb analysis and a non-verb analysis by the medieval dictionary, or if the token is not found in the dictionary, the token is matched against the 19th century dictionary before being processed by the morphological analyser and the tagger as described in setting morph + tag. 19c + medieval A token is considered to be a verb if it has been unambiguously analysed as a verb by the 19th century dictionary. Likewise a token is considered not to be a verb, if it has been given one or more analyses from the 19th century dictionary, where none of the analyses is a verb interpretation. If the token has been given both a verb analysis and a non-verb analysis by the 19th century dictionary, or if the token is not found in the dictionary, the token is matched against the medieval dictionary before being processed by the morphological analyser and the tagger as described in setting morph + tag. Results Normalisation Rules Running the SALDO morphological analyser on the test text with the old Swedish spelling preserved, meant that only 30% of the words were analysed at all. Applying the normalisation rules before the morphological analysis is performed, drastically increases recall. After only 5 rules have been applied, recall is increased by 11 percentage units, and adding another 5 rules increases recall by another 26 percentage units. All in all, recall increases from 30% for unnormalised text to 83% after all normalisation rules have been applied, whereas precision increases from 54% to 66%, as illustrated in table 1. Recall is still significantly higher for contemporary Swedish texts than for the historical text (99% as compared to 83% with the best normalisation settings). Nevertheless, the rapid increase in recall when applying the normalisation rules is very promising, and it is yet to be explored how good results it is possible to reach if including more normalisation rules. As could be expected, the tagger yields higher precision than the morphological anlayser, due to the fact that the morphological analyser renders all analyses for a word form given in the dictionary, regardless of context. The results of combining the morphological analyser and the tagger are also quite expected. In the case where a token is considered to be a verb if there is a morphological verb analysis or it has been analysed as a verb by the tagger, a very high level of recall (92%) is achieved at the expense of low precision, whereas the opposite is true for the case where a token is considered to be a verb if there is a morphological verb analysis and it has been tagged as a verb. Using the tagger for disambiguation only in ambiguous cases yields the best results. It should be noted that using the morphand-tag setting results in the same f-score as the disambiguation setting. However, the disambiguation setting performs better in terms of recall, which is of importance to the historians in the project at hand. Another advantage of using the disambiguation setting is that the difference between precision and recall is less. Morphological Analysis and Tagging Historical Dictionaries The results of using the historical dictionaries are presented in table 3. Adding the historical dictionaries did not improve the verb analysis results; actually the opposite is true. Studying the results of the analyses from the medieval dictionary, one may notice that only two verb analyses have been found when applied to the test text, and both of them are erroneous in this context (in both cases the word lass "load" as in the phrase 6 lass höö "6 loads of hay"). Furthermore, the medieval dictionary produces quite a lot of nonverb analyses for commonly occurring verbs, for example skola (noun: "shool", verb: "should/shall"), kunna ("can/could"), kom ("come"), finna ("find") and vara (noun: "goods", verb: "be"). Another rea-son for the less encouraging results seems to be that most of the words actually found and analysed correctly are words that are correctly analysed by the contemporary tools as well, such as i ("in"), man ("man/you"), sin ("his/her/its"), honom ("him") and in ("into"). As for the 19th century dictionary, the same problems apply. For example, a number of frequent verb forms are analysed as non-verbs (e.g. skall "should/shall" and ligger "lies"). There are also non-verbs repeatedly analysed as verbs, such as stadgar ("regulations") and egne ("own"). As was the case for the medieval dictionary, most of the words analysed correctly by the 19th century dictionary are commonly occuring words that would have been correctly analysed by the morphological analyser and/or the tagger as well, for example och ("and"), men ("but") and när ("when"). Support for Historical Research In the ongoing Gender and Work project at the Department of History, Uppsala University, historians are interested in what men and women did for a living in the early modern Swedish Society. 3 Information on this is registered and made available for research in a database, most often in the form of a verb and its object(s). The automatic verb extraction tool was developed in close cooperation with the Gender and Work participants, with the aim of reducing the manual effort involved in finding the relevant information to enter into the database. The verb extraction tool was integrated in a prototypical graphical user interface, enabling the historians to run the system on historical texts of their choice. The interface provides facilities for uploading files, generating a list of all the verbs in the file, displaying verb concordances for interesting verbs, and displaying the verb in a larger context. Figure 2 illustrates the graphical user interface, displaying concordances for the verb anklaga ("accuse"). The historians found the interface useful and are interested in integrating the tool in the Gender and Work database. Further development of the verb extraction tool is now partly funded by the Gender and Work project. Conclusion Today historians and other researchers working on older texts have to manually go through large volumes of text when searching for information on for example culture or social structure in historical times. In this paper we have shown that this time-consuming manual effort could be significantly reduced using contemporary natural language processing tools to display only those text segments that may be of interest to the researcher. We have described the development of a tool that automatically identifies verbs in historical Swedish texts using morphological analysis and tagging, and a prototypical graphical user interface, integrating this tool. The results indicate that it is possible to retrieve verbs in Swedish texts from the 17th century with 82% precision and 88% recall, using morphological tools for contemporary Swedish, if the text is normalised into a more modern spelling before the morphological tools are applied (recall may be increased to 92% if a lower precision is accepted). Adding electronically available dictionaries cov-ering medieval Swedish and 19th century Swedish respectively to the verb extraction tool, did not improve the results as compared to using only contemporary NLP tools. This seems to be partly due to the dictionaries still being in an early stage of development, where lexical coverage is unevenly spread among different word classes, and frequent, irregularly inflected word forms are not covered. It would therefore be interesting to study the results of the historical dictionary lookup, when the dictionaries are more mature. Since the present extraction tool has been evaluated on one single text, it would also be interesting to explore how these extraction methods should be adapted to handle language variation in texts from different genres and time periods. Due to the lack of spelling conventions, it would also be interesting to see how the extraction process performs on texts from the same period and genre, but written by different authors. Future work also includes experiments on identifying linguistic categories other than verbs. Figure 2 : 2Concordances displayed for the verb anklaga ("accuse") in the graphical user interface. Table 2 2presents the results of combining the SALDO morphological analyser and the HunPOS tagger, using the settings described in section 4.3. Precision Recall f-score morph 0.66 0.83 0.74 tag 0.81 0.86 0.83 morph or tag 0.61 0.92 0.74 morph and tag 0.92 0.80 0.85 morph + tag 0.82 0.88 0.85 Table 2 : 2Results for normalised text, combining mor- phological analysis and tagging. morph = morphological analysis using SALDO. tag = tagging using HunPOS. Table 3 : 3Results for normalised text, combining histor- ical dictionaries and contemporary analysis tools. me- dieval = Medieval Lexical Database. 19c = Dalin's Dic- tionary. morph = morphological analysis using SALDO. tag = tagging using HunPOS. http://litteraturbanken.se/ http://spraakbanken.gu.se/Ö http://gaw.hist.uu.se/ Kortfattad svensk språkhistoria. Gösta Bergman, Prisma Magnum. Stockholm5th ed.Gösta Bergman. 1995. Kortfattad svensk språkhistoria. Prisma Magnum, 5th ed., Stockholm. SALDO 1.0 (Svenskt associationslexikon version 2). Språkbanken. Lars Borin, Markus Forsberg, Lennart Lönngren, University of GothenburgLars Borin, Markus Forsberg, and Lennart Lönngren. 2008. SALDO 1.0 (Svenskt associationslexikon ver- sion 2). Språkbanken, University of Gothenburg. Naming the Past: Named Entity and Anomacy Recognition in 19th Century Swedish Literature). Lars Borin, Dimitrios Kokkinakis, Leif-Jöran , Proceedings of the Workshop on Language Technology for Cultural Heritage Data (LaT-eCH 2007). the Workshop on Language Technology for Cultural Heritage Data (LaT-eCH 2007)Prague, Czech RepublicOlssonLars Borin, Dimitrios Kokkinakis, and Leif-Jöran Ols- son. 2007. Naming the Past: Named Entity and Anomacy Recognition in 19th Century Swedish Lit- erature). In: Proceedings of the Workshop on Lan- guage Technology for Cultural Heritage Data (LaT- eCH 2007), pages 1-8. Prague, Czech Republic. Nationalencyklopedins ordbok. Bra Böcker. Bra Böcker, HöganäsBra Böcker. 1995. Nationalencyklopedins ordbok. Bra Böcker, Höganäs. TnT -A Statistical Part-of-Speech Tagger. Thorsten Brants, Proceedings of the 6th Applied Natural Language Processing Conference (ANLP-00). the 6th Applied Natural Language Processing Conference (ANLP-00)Seattle, Washington, USAThorsten Brants. 2000. TnT -A Statistical Part-of- Speech Tagger. In: Proceedings of the 6th Applied Natural Language Processing Conference (ANLP-00), Seattle, Washington, USA. OrdbokÖfver svenska språket. Vol I-II. Anders Fredrik Dalin, StockholmAnders Fredrik Dalin. 1850-1855. OrdbokÖfver sven- ska språket. Vol I-II. Stockholm. Uppländska domböcker. jämte inledning, förklaringar och register utgivna genom Nils Edling. UppsalaNils Edling. 1937. Uppländska domböcker. jämte in- ledning, förklaringar och register utgivna genom Nils Edling. Uppsala. Manual of the Stockholm Umeå Corpus version 2.0. Description of the content of the SUC 2.0 distribution, including the unfinished documentation by Gunnel Källgren. Sofia Gustafson, - Capková, Britt Hartmann, Sofia Gustafson-Capková and Britt Hartmann. December 2006. Manual of the Stockholm Umeå Corpus version 2.0. Description of the content of the SUC 2.0 dis- tribution, including the unfinished documentation by Gunnel Källgren. HunPos -an open source trigram tagger. Péter Halácsy, András Kornai, Csaba Oravecz, Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion. the 45th Annual Meeting of the Association for Computational Linguistics CompanionPrague, Czech RepublicAssociation for Computational LinguisticsPéter Halácsy, András Kornai, and Csaba Oravecz 2007. HunPos -an open source trigram tagger. In: Pro- ceedings of the 45th Annual Meeting of the Associ- ation for Computational Linguistics Companion Vol- ume Proceedings of the Demo and Poster Sessions, pages 209-212. Association for Computational Lin- guistics, Prague, Czech Republic. Svenskt associationslexikon, del I-IV. Department of Linguistics and Philology. Lennart Lönngren, Uppsala UniversityLennart Lönngren. 1992. Svenskt associationslexikon, del I-IV. Department of Linguistics and Philology, Uppsala University. The Open Source Tagger HunPoS for Swedish. Department of Linguistics and Philology. B Beáta, Megyesi, Uppsala UniversityBeáta B. Megyesi. 2008. The Open Source Tagger HunPoS for Swedish. Department of Linguistics and Philology, Uppsala University. Semi-automatic Normalization of Old Hungarian Codices. Csaba Oravecz, Bálint Sass, Eszter Simon, 55- 59. 16Proceedings of the ECAI 2010 Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities. the ECAI 2010 Workshop on Language Technology for Cultural Heritage, Social Sciences, and HumanitiesLisbon, PortugalFaculty of Science, University of LisbonCsaba Oravecz, Bálint Sass, and Eszter Simon 2010. Semi-automatic Normalization of Old Hungarian Codices. In: Proceedings of the ECAI 2010 Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH 2010). Pages 55- 59. 16 August, 2010 Faculty of Science, University of Lisbon Lisbon, Portugal. Marco Pennacchiotti, Fabio Massimo Zanzotto, Natural Language Processing Across Time: An Empirical Investigation on Italian. Aarne Ranta and Bengt NordströmBerlin HeidelbergSpringer-Verlag5221Advances in Natural Language ProcessingMarco Pennacchiotti and Fabio Massimo Zanzotto 2008. Natural Language Processing Across Time: An Em- pirical Investigation on Italian. In: Aarne Ranta and Bengt Nordström (Eds.): Advances in Natural Lan- guage Processing. GoTAL 2008, LNAI Volume 5221, pages 371-382. Springer-Verlag Berlin Heidelberg. Automated Creation of a Partially Syntactically Annotated Corpus of Medieval Portuguese Using Contemporary Portuguese Resources. Vitor Rocio, Amado Mário, José Gabriel Alves, Maria Francisca Lopes, Graça Xavier, Vicente, Proceedings of the ATALA workshop on Treebanks. the ATALA workshop on TreebanksParis, FranceVitor Rocio, Mário Amado Alves, José Gabriel Lopes, Maria Francisca Xavier, and Graça Vicente. 1999. Automated Creation of a Partially Syntactically Anno- tated Corpus of Medieval Portuguese Using Contem- porary Portuguese Resources. In: Proceedings of the ATALA workshop on Treebanks, Paris, France. Ordbok till Samlingen af Sweriges Gamla Lagar. Carl Johan Schlyter, LundCarl Johan Schlyter. 1877. Ordbok till Samlingen af Sweriges Gamla Lagar. Lund. Svenska Akademiens ordlistaöver svenska språket. Svenska Akademien, Norstedts Akademiska Förlag. Svenska Akademien. 2006. Svenska Akademiens or- dlistaöver svenska språket. Norstedts Akademiska Förlag, Stockholm. . Knut Fredrik Söderwall ; I-Iii Lund, OrdbokÖfver svenska medeltids-språket. Knut Fredrik Söderwall. 1884-1918. OrdbokÖfver svenska medeltids-språket, vol I-III. Lund. . Knut Fredrik Söderwall ; Iv-V Lund, OrdbokÖfver svenska medeltids-språket. Knut Fredrik Söderwall. 1953-1973. OrdbokÖfver svenska medeltids-språket, vol IV-V. Lund. Svenska Akademiens grammatik. Norstedts Ordbok. Ulf Teleman, Staffan Hellberg, Erik Andersson, StockholmUlf Teleman, Staffan Hellberg, and Erik Andersson. 1999. Svenska Akademiens grammatik. Norstedts Or- dbok, Stockholm.

Briefly Noted Opinion Mining and Sentiment Analysis

Briefly Noted Opinion Mining and Sentiment Analysis 2008 Bo Pang Yahoo! Research Cornell University Boston Lillian Lee Yahoo! Research Cornell University Boston Briefly Noted Opinion Mining and Sentiment Analysis Foundations and Trends in Information Retrieval 21-22008: Now Publishers, 2008, x+137 pp; paperbound, ISBN 978-1-60198-150-9, $99.00, €99.00, £79.00 [Originally published as Over the last decade or so there has been growing interest in research on computationally analyzing opinions, feelings, and subjective evaluation in text. This burgeoning body of work, variously called "sentiment analysis," "opinion mining," and "subjectivity analysis," addresses such problems as distinguishing objective from subjective propositions, characterizing positive and negative evaluations, determining the sources of different opinions expressed in a document, and summarizing writers' judgments over a large corpus of texts. Potential applications include Web mining for consumer and political opinion summarization, business and government intelligence analysis, and improving text analysis applications such as information retrieval, question answering, and text summarization. In this well-written book, Pang and Lee survey the current state of the art in opinion mining and sentiment analysis, broadly construed, with the goal of fitting this diverse research area into a unified framework. After a brief introduction to the area (Chapter 1) and survey of application areas (Chapter 2), the authors present their view of the central challenges that unify this research area in Chapter 3, largely by contrasting it with "traditional," "fact-based" text analysis. The book then surveys the full range of extant approaches, dividing them into sentiment classification and extraction (Chapter 4), and opinion summarization (Chapter 5). This survey is quite thorough as regards computational work in the area, though it lacks detailed reference to relevant linguistics research such as in the study of modality (Nuyts 2001;Kärkkäinen 2003), cognitive linguistics (Stein and Wright 1995;Langacker 2002), and appraisal theory (Martin and White 2005). This lacuna is justified, however, by the (perhaps unfortunate) fact that little computational work to date relates to this literature. 1 A distinctive and valuable feature of the book is the inclusion of material on the relationship between subjective language and its social and economic impact (Chapter 6). This discussion helps to place the technical work in its larger context, pointing towards opportunities and risks in its application in various situations. Also particularly valuable is Chapter 7, on publicly available resources, which includes much useful information about available data sets, relevant competitive evaluations, and tutorials/bibliographies in the area. Although much of this information is likely to become outdated, the authors also maintain a companion Web site 2 which presumably will feature updates to this resource list. The book provides a useful resource for application developers as well as for researchers, though some readers might have benefited from a more extensive discussion of real-world applications and how various techniques can be used as components of larger systems. Overall, this slim and entertaining volume is an excellent and timely survey of an exciting and growing research area within computational linguistics. (1985) and their influence on the development of the notion of subjectivity, but it would have been nice to see a broader discussion relating the computational state of the art to linguistic theory. 2 www.cs.cornell.edu/home/llee/opinionmining-sentiment-analysis-survey.html. -Shlomo Argamon, Illinois Institute of Technology References Banfield, Ann. 1982. Unspeakable Sentences: Narration and Representation in the Language of Fiction. Routledge & Kegan Paul Books. Kärkkäinen, Elise. 2003. Epistemic Stance in English Conversation: A Description of Its Interactional Functions, With a Focus on I Think. John Benjamins Publishing Company. To be fair, there is a brief discussion in Chapter 1 of Banfield's work (1982) and Quirk et al.'s notion of private states1 Deixis and subjectivity. Grounding: The Epistemic Footing of Deixis and Reference. Ronald W Langacker, Langacker, Ronald W. 2002. Deixis and subjectivity. Grounding: The Epistemic Footing of Deixis and Reference, pages 1-28. The Language of Evaluation: Appraisal in English. James R Martin, R R Peter, White, Palgrave MacmillanMartin, James R. and Peter R. R. White. 2005. The Language of Evaluation: Appraisal in English. Palgrave Macmillan. Subjectivity as an evidential dimension in epistemic modal expressions. Nuyts, Journal of Pragmatics. 333Nuyts, Jan. 2001. Subjectivity as an evidential dimension in epistemic modal expressions. Journal of Pragmatics, 33(3):383-400. A Comprehensive Grammar of the English Language. Randolph Quirk, Sidney Greenbaum, Geoffrey Leech, Jan Svartvik, Longman, London and New YorkQuirk, Randolph, Sidney Greenbaum, Geoffrey Leech, and Jan Svartvik. 1985. A Comprehensive Grammar of the English Language. Longman, London and New York. Subjectivity and Subjectivisation: Linguistic Perspectives. Dieter Stein, Susan Wright, Cambridge University PressCambridge, UKStein, Dieter and Susan Wright, editors. 1995. Subjectivity and Subjectivisation: Linguistic Perspectives. Cambridge University Press, Cambridge, UK.

Extracting Gene Regulation Networks Using Linear-Chain Conditional Random Fields and Rules

Published literature in molecular genetics may collectively provide much information on gene regulation networks. Dedicated computational approaches are required to sip through large volumes of text and infer gene interactions. We propose a novel sieve-based relation extraction system that uses linear-chain conditional random fields and rules. Also, we introduce a new skip-mention data representation to enable distant relation extraction using first-order models. To account for a variety of relation types, multiple models are inferred. The system was applied to the BioNLP 2013 Gene Regulation Network Shared Task. Our approach was ranked first of five, with a slot error rate of 0.73.

Extracting Gene Regulation Networks Using Linear-Chain Conditional Random Fields and Rules August 9 Slavkožitnik † ‡ Marinkažitnik Faculty of Computer and Information Science University of Ljubljana Tržaška cesta 25, Dunajska cesta 152SI-1000, SI-1000Ljubljana, Ljubljana Blaž Zupan Faculty of Computer and Information Science University of Ljubljana Tržaška cesta 25, Dunajska cesta 152SI-1000, SI-1000Ljubljana, Ljubljana Marko Bajec Faculty of Computer and Information Science University of Ljubljana Tržaška cesta 25, Dunajska cesta 152SI-1000, SI-1000Ljubljana, Ljubljana Extracting Gene Regulation Networks Using Linear-Chain Conditional Random Fields and Rules Proceedings of the BioNLP Shared Task 2013 Workshop the BioNLP Shared Task 2013 WorkshopSofia, BulgariaAugust 9 Published literature in molecular genetics may collectively provide much information on gene regulation networks. Dedicated computational approaches are required to sip through large volumes of text and infer gene interactions. We propose a novel sieve-based relation extraction system that uses linear-chain conditional random fields and rules. Also, we introduce a new skip-mention data representation to enable distant relation extraction using first-order models. To account for a variety of relation types, multiple models are inferred. The system was applied to the BioNLP 2013 Gene Regulation Network Shared Task. Our approach was ranked first of five, with a slot error rate of 0.73. Introduction In recent years we have witnessed an increasing number of studies that use comprehensive PubMed literature as an additional source of information. Millions of biomedical abstracts and thousands of phenotype and gene descriptions reside in online article databases. These represent an enormous amount of knowledge that can be mined with dedicated natural language processing techniques. However, extensive biological insight is often required to develop text mining techniques that can be readily used by biomedical experts. Profiling biomedical research literature was among the first approaches in diseasegene prediction and is now becoming invaluable to researchers (Piro and Di Cunto, 2012;Moreau and Tranchevent, 2012). Information from publication repositories was often merged with other databases. Successful examples of such integration include an OMIM database on human genes and genetic phenotypes (Amberger et al., 2011), GeneRIF function annotation database (Osborne et al., 2006), Gene Ontology (Ashburner et al., 2000) and clinical information about drugs in the DailyMed database (Polen et al., 2008). Biomedical literature mining is a powerful way to identify promising candidate genes for which abundant knowledge might already be available. Relation extraction (Sarawagi, 2008) can identify semantic relationships between entities from text and is one of the key information extraction tasks. Because of the abundance of publications in molecular biology computational methods are required to convert text into structured data. Early relation extraction systems typically used hand-crafted rules to extract a small set of relation types (Brin, 1999). Later, machine learning methods were adapted to support the task and were trained over a set of predefined relation types. In cases where no tagged data is available, some unsupervised techniques offer the extraction of relation descriptors based on syntactic text properties (Bach and Badaskar, 2007). Current state-of-theart systems achieve best results by combining both machine learning and rule-based approaches (Xu et al., 2012). Information on gene interactions are scattered in data resources such as PubMed. The reconstruction of gene regulatory networks is a longstanding but fundamental challenge that can improve our understanding of cellular processes and molecular interactions (Sauka-Spengler and Bronner-Fraser, 2008). In this study we aimed at extracting a gene regulatory network of the popular model organism the Bacillus subtilis. Specifically, we focused on the sporulation function, a type of cellular differentiation and a well-studied cellular function in B. subtilis. We describe the method that we used for our participation in the BioNLP 2013 Gene Regulation Network (GRN) Shared Task (Bossy et al., 2013). The goal of the task was to retrieve the genic interactions. The participants were provided with manually annotated sentences from research literature that contain entities, events and genic interactions. Entities are sequences of text that identify objects, such as genes, proteins and regulons. Events and relations are described by type, two associated entities and direction between the two entities. The participants were asked to predict relations of interaction type in the test data set. The submitted network of interactions was compared to the reference network and evaluated with Slot Error Rate (SER) (Makhoul et al., 1999) SER = (S + I + D)/N that measures the fraction of incorrect predictions as the sum of relation substitutions (S), insertions (I) and deletions (D) relative to the number of reference relations (N). We begin with a description of related work and the background of relation extraction. We then present our extension of linear-chain conditional random fields (CRF) with skip-mentions (Sec. 3). Then we explain our sieve-based system architecture (Sec. 4), which is the complete pipeline of data processing that includes data preparation, linear-chain CRF and rule based relation detection and data cleaning. Finally, we describe the results at BioNLP 2013 GRN Shared Task (Sec. 6). Related Work The majority of work on relation extraction focuses on binary relations between two entities. Most often, the proposed systems are evaluated against social relations in ACE benchmark data sets (Bunescu and Mooney, 2005;Wang et al., 2006). There the task is to identify pairs of entities and assign them a relation type. A number of machine learning techniques have been used for relation extraction, such as sequence classifiers, including HMM (Freitag and McCallum, 2000), CRF (Lafferty et al., 2001) and MEMM (Kambhatla, 2004), and binary classifiers. The latter most oftem employ SVM (Van Landeghem et al., 2012). The ACE 2004 data set (Mitchell et al., 2005) contains two-tier hierarchical relation types. Thus, a relation can have another relation as an attribute and second level relation must have only atomic attributes. Therefore, two-tier relation hierarchies have the maximum height of two. Wang et al. (2006) employed a one-against-one SVM classifier to predict relations in ACE 2004 data set using semantic features from WordNet (Miller, 1995). The BioNLP 2013 GRN Shared Task aims to de-tect three-tier hierarchical relations. These relations describe interactions that can have events or other interactions as attributes. In contrast to pairwise approach of Wang et al. (2006), we extract relations with sequence classifiers and rules. The same relation in text can be expressed in many forms. Machine-learning approaches can resolve this heterogeneity by training models on large data sets using a large number of feature functions. Text-based features can be constructed through application of feature functions. An approach to overcome low coverage of different relation forms was proposed by Garcia and Gamallo (2011). They introduced a lexico-syntactic pattern-based feature functions that identify dependency heads and extracts relations. Their approach was evaluated over two relation types in two languages and achieved good results. In our study we use rules to account for the heterogeneity of relation representation. Generally, when trying to solve a relation extraction task, data sets are tagged using the IOB (inside-outside-beginning) notation (Ramshaw and Marcus, 1995), such that the first word of the relation is tagged as B-REL, other consecutive words within it as I-REL and all others as O. The segment of text that best describes a predefined relation between two entities is called a relation descriptor. Li et al. (2011) trained a linearchain CRF to uncover these descriptors. They also transformed subject and object mentions of the relations into dedicated values that enabled them to correctly predict relation direction. Additionally, they represented the whole relation descriptor as a single word to use long-range features with a first-order model. We use a similar model but propose a new way of token sequence transformation which discovers the exact relation and not only the descriptor. Banko and Etzioni (2008) used linear models for the extraction of open relations (i.e. extraction of general relation descriptors without any knowledge about specific target relation type). They first characterized the type of relation appearance in the text according to lexical and syntactic patterns and then trained a CRF using these data along with synonym detection (Yates and Etzioni, 2007). Their method is useful when a few relations in a massive corpus are unknown. However, if higher levels of recall are desired, traditional relation extraction is a better fit. In this study we therefore propose a completely super-vised relation extraction method. Methods for biomedical relation extraction have been tested within several large evaluation initiatives. The Learning language in logic (LLL) challenge on genic interaction extraction (Nédellec, 2005) is similar to the BioNLP 2013 GRN Shared Task, which contains a subset of the LLL data set enriched with additional annotations. Giuliano et al. (2006) solved the task using an SVM classifier with a specialized local and global context kernel. The local kernel uses only mentionrelated features such as word, lemma and part-ofspeech tag, while the global context kernel compares words that appear on the left, between and on the right of two candidate mentions. To detect relations, they select only documents containing at least two mentions and generate n k training examples, where n is the number of all mentions in a document and k is number of mentions that form a relation (i.e. two). They then predict three class values according to direction (subjectobject, object-subject, no relation). Our approach also uses context features and syntactic features of neighbouring tokens. The direction of relations predicted in our model is arbitrary and it is further determined using rules. The BioNLP 2011 REL Supporting Shared Task addressed the extraction of entity relations. The winning TESS system (Van Landeghem et al., 2012) used SVMs in a pipeline to detect entity nodes, predict relations and perform some postprocessing steps. They predict relations among every two mention pairs in a sentence. Their study concluded that the term detection module has a strong impact on the relation extraction module. In our case, protein and entity mentions (i.e. mentions representing genes) had already been identified, and we therefore focused mainly on extraction of events, relations and event modification mentions. Conditional Random Fields with Skip-Mentions Conditional random fields (CRF) (Lafferty et al., 2001) is a discriminative model that estimates joint distribution p(y|x) over the target sequence y, conditioned on the observed sequence x. The following example shows an observed sequence x where mentions are printed in bold: "Transcription of cheV initiates from a sigma D-dependent promoter element both in vivo and in vitro, and expression of a cheV-lacZ fusion is completely dependent on sigD." 1 Corresponding sequences x P OS , x P ARSE , x LEM M A contain part-of-speech tags, parse tree tokens and lemmas for each word, respectively. Different feature functions f j (Fig. 2), employed by CRF, use these sequences in order to model the target sequence y, which also corresponds to tokens in x. Feature function modelling is an essential part when training CRF. Selection of feature functions contributes the most to an increase of precision and recall when training CRF classifiers. Usually these are given as templates and the final features are generated by scanning the entire training data set. The feature functions used in our model are described in Sec. 3.1. CRF training finds a weight vector w that predicts the best possible (i.e. the most probable) sequenceŷ given x. Hence, y = arg max y p(y|x, w),(1) where the conditional distribution equals p(y|x, w) = exp( m j=1 w j n i=1 f j (y, x, i)) C(x, w) . (2) Here, n is the length of the observed sequence x, m is the number of feature functions and C(x, w) is a normalization constant computed over all possible y. We do not consider the normalization constant because we are not interested in exact target sequence probabilities. We select only the target sequence that is ranked first. Figure 1: The structure of a linear-chain CRF model. It shows an observable sequence x and target sequence y containing n tokens. The structure of a linear-chain CRF (LCRF) model or any other more general graphical model is defined by references to the target sequence labels within the feature functions. Fig. 1 shows the function f(y, x, i): if (y i−1 == O and y i == GENE and x i−1 == transcribes) then return 1 else return 0 Figure 2: An example of a feature function. It checks if the previous label was Other, the current is Gene and the previous word was "transcribes", returns 1, otherwise 0. structure of the LCRF. Note that the i-th factor can depend only on the current and the previous sequence labels y i and y i−1 . LCRF can be efficiently trained, whereas exact inference of weights in CRF with arbitrary structure is intractable due to an exponential number of partial sequences. Thus, approximate approaches must be adopted. Data Representation The goal of our task is to identify relations between two selected mentions. If we process the input sequences as is, we cannot model the dependencies between two consecutive mentions because there can be many other tokens in between. From an excerpt of the example in the previous section, "cheV initiates from a sigmaD", we can observe the limitation of modelling just two consecutive tokens. With this type of labelling it is hard to extract the relationships using a first-order model. Also, we are not interested in identifying relation descriptors (i.e. segments of text that best describe a pre-defined relation); therefore, we generate new sequences containing only mentions. Mentions are also the only tokens that can be an attribute of a relation. In Fig. 3 we show the transformation of our example into a mention sequence. The observable sequence x contains sorted entity mentions that are annotated. These annotations were part of the training corpus. The target sequence y is tagged with the none symbol (i.e. O) or the name of the relationship (e.g. Interaction.Requirement). Each relationship target token represents a relationship between the current and the previous observable mention. The mention sequence as demonstrated in Fig. 3 does not model the relationships that exist between distant mentions. For example, the mentions cheV and promoter are related by a Promoter of relation, which cannot be identified using only LCRF. Linear model can only detect dependencies between two consecutive mentions. To model such relationships on different distances we generate appropriate skip-mention sequences. The notion of skip-mention stands for the number of other mentions between two consecutive mentions which are not included in a specific skip-mention sequence. Thus, to model relationships between every second mention, we generate two one skipmention sequences for each sentence. A one skipmention sequence identifies the Promoter of relation, shown in Fig. 4. Figure 4: A mention sequence with one skipmention. This is one out of two generated mention sequences with one skip-mention. The other consists of tokens sigmaD and cheV. For every s skip-mention number, we generate s + 1 mention sequences of length n s . After these sequences are generated, we train one LCRF model per each skip-mention number. Model training and inference of predictions can be done in parallel due to the sequence independence. Analogously, we generate model-specific skip-mention sequences for inference and get target labellings as a result. We extract the identified relations between the two mentions and represent them as an undirected graph. Fig. 5 shows the distribution of distances be- tween the relation mention attributes (i.e. agents and targets) in the BioNLP 2013 GRN training and development data set. The attribute mention data consists of all entity mentions and events. We observe that most of relations connect attributes on distances of two and three mentions. To get our final predictions we train CRF models on zero to ten skip-mention sequences. We use the same unigram and bigram feature function set for all models. These include the following: • target label distribution, • mention type (e.g. Gene, Protein) and observable values (e.g., sigma D) of mention distance 4 around current mention, • context features using bag-of-words matching on the left, between and on the right side of mentions, • hearst concurrence features (Bansal and Klein, 2012), • token distance between mentions, • parse tree depth and path between mentions, • previous and next lemmas and part-of-speech tags. Data Analysis Pipeline We propose a pipeline system combining multiple processing sieves. Each sieve is an independent data processing component. The system consists of eight sieves, where the first two sieves prepare data for relation extraction, main sieves consist of linear-chain CRF and rule-based relation detection, and the last sieve cleans the output data. Full implementation is publicly available (https://bitbucket.org/szitnik/iobie). We use CRF-Suite (http://www.chokkan.org/software/crfsuite) for faster CRF training and inference. First, we transform the input data into a format appropriate for our processing and enrich the data with lemmas, parse trees and part-of-speech tags. We then identify additional action mentions which act as event attributes (see Sec. 4.3). Next, we employ the CRF models to detect events. We treat events as a relation type. The main relation processing sieves detect relations. We designed several processing sieves, which support different relation attribute types and hierarchies. We also employ rules at each step to properly set the agent and target attributes. In the last relation processing sieve, we perform rule-based relation extraction to detect high precision relations and boost the recall. In the last step we clean the extracted results and export the data. The proposed system sieves are executed in the following order: In the description of the sieves in the following sections, we use general relation terms, naming the relation attributes as subject and object, as shown in Fig. 6. subject object relation Figure 6: General relation representation. Preprocessing Sieve The preprocessing sieve includes data import, sentence detection and text tokenization. Additionally, we enrich the data using part-of-speech tags, parse trees (http://opennlp.apache.org) and lemmas (Juršic et al., 2010). Mention Processing Sieve The entity mentions consist of Protein, Gene-Family, ProteinFamily, ProteinComplex, Poly-meraseComplex, Gene, Operon, mRNA, Site, Regulon and Promoter types. Action mentions (e.g. inhibits, co-transcribes) are automatically detected as they are needed as event attributes for the event extraction. We therefore select all lemmas of the action mentions from the training data and detect new mentions from the test data set by comparing lemma values. Event Processing Sieve The general definition of an event is described as a change on the state of a bio-molecule or biomolecules (e.g. "expression of a cheV-lacZ fusion is completely dependent on sigD"). We represent events as a special case of relationship and name them "EVENT". In the training data, the event subject types are Protein, GeneFamily, PolymeraseComplex, Gene, Operon, mRNA, Site, Regulon and Promoter types, while the objects are always of the action type (e.g. "expression"), which we discover in the previous sieve. After identifying event relations using the linear-chain CRF approach, we apply a rule that sets the action mention as an object and the gene as a subject attribute for every extracted event. Relations Processing Sieves According to the task relation properties (i.e. different subject and object types), we extract relations in three phases (iv, v, vi). This enables us to extract hierarchical relations (i.e. relation contains another relation as subject or object) and achieve higher precision. All sieves use the proposed linear-chain CRF-based extraction. The processing sieves use specific relation properties and are executed as follows: (iv) First, we extract relations that contain only entity mentions as attributes (e.g. "Transcription of cheV initiates from a sigmaD" resolves into the relation sigmaD → Interaction.Transcription → cheV). (v) In the second stage, we extract relations that contain at least one event as their attribute. Prior to execution we transform events into their mention form. Mentions generated from events consist of two tokens. They are taken from the event attributes and the new event mention is included into the list of existing mentions. Its order within the list is determined by the index of the lowest mention token. Next, relations are identified following the same principle as in the first step. (vi) According to an evaluation peculiarity of the challenge, the goal is to extract possible interactions between genes. Thus, when a relation between a gene G1 and an event E should be extracted, the GRN network is the same as if the method identifies a relation between a gene G1 and gene G2, if G2 is the object of event E. We exploit this notion by generating training data to learn relation extraction only between B. subtilis genes. During this step we use an external resource of all known genes of the bacteria retrieved from the NCBI 2 . The training and development data sets include seven relation instances that have a relation as an attribute. We omitted this type of hierarchy extraction due to the small number of data instances and execution of relation extraction between genes. There are also four negative relation instances. The BioNLP task focuses on positive relations, so there would be no increase in performance if negative relations were extracted. Therefore, we extract only positive relations. According to the data set, we could simply add a separate sieve which would extract negations by using manually defined rules. Words that explicitly define these negations are not, whereas, neither and nor. Rule-Based Relations Processing Sieve The last step of relation processing uses rules that extract relations with high precision. General rules consist of the following four methods: • The method that checks all consequent mention triplets that contain exactly one action mention. As input we set the index of the action mention within the triplet, its matching regular expression and target relation. • The method that processes every two consequent B. subtilis entity mentions. It takes a regular expression, which must match the text between the mentions, and a target relation. • The third method is a modification of the previous method that supports having a list of entity mentions on the left or the right side. For example, this method extracts two relations in the following example: "rsfA is under the control of both sigma(F) and sigma(G)". • The last method is a variation of the second method, which removes subsentences between the two mentions prior to relation extraction. For example, the method is able to extract distant relation from the following example: "sigma(F) factor turns on about 48 genes, including the gene for RsfA, and the gene for sigma(G)". This is sigma(F) → Interaction.Activation → sigma(G). We extract the Interaction relations using regular expression and specific keywords for the transcription types (e.g. keywords transcrib, directs transcription, under control of), inhibition (keywords repress, inactivate, inhibits, negatively regulated by), activation (e.g. keywords governed by, activated by, essential to activation, turns on), requirement (e.g. keyword require) and binding (e.g. keywords binds to, -binding). Notice that in biomedical literature, a multitude of expressions are often used to describe the same type of genetic interaction. For instance, researchers might prefer using the expression to repress over to inactivate or to inhibit. Thus, we exploit these synsets to improve the predictive accuracy of the model. Data Cleaning Sieve The last sieve involves data cleaning. This consists of removing relation loops and eliminating redundancy. A relation is considered a loop if its attribute mentions represent the same entity (i.e. mentions corefer). For instance, sentence "... sigma D element, while cheV-lacZ depends on sigD ..." contains mentions sigma D and sigD, which cannot form a relationship because they represent the same gene. By removing loops we reduce the number of insertions. Removal of redundant relations does not affect the final score. Table 1 shows statistics of data sets used in our study. For the test data set we do not have tagged data and therefore cannot show the detailed evaluation analysis for each sieve. Each data set consists of sentences extracted from PubMed abstracts on the topic of the gene regulation network of the sporulation of B. subtilis. The sentences in both the training and the development data sets are manually annotated with entity mentions, events and relations. Real mentions in Table 1 are the mentions that refer to genes or other structures, while action mentions refer to event attributes (e.g. transcription). Our task is to extract Interaction relations of the types regulation, inhibition, activation, requirement, binding and transcription for which the extraction algorithm is also evaluated. Data in BioNLP 2013 GRN Challenge The extraction task in GRN Challenge is twofold: given annotated mentions, a participant needs to identify a relation and then determine the role of relation attributes (i.e. subject or object) within the previously identified relation. Only predictions that match the reference relations by both relation type and its attributes are considered as a match. 6 Results and Discussion We tested our system on the data from BioNLP 2013 GRN Shared Task using the leave one out cross validation on the training data and achieved a SER of 0.756, with 4 substitutions, 81 deletions, 14 insertions and 46 matches, given 131 reference relations. The relatively high number of deletions in these results might be due to ambiguities in the data. We identified the following number of extracted relations in the relation extraction sieves (Sec. 4): (iii) 91 events, (iv) 130 relations between mentions only, (v) 27 relations between an event and a mention, (vi) 39 relations between entity mentions, and (vii) 44 relations using only rules. Our approach consists of multiple submodules, each designed for a specific relation attribute type (e.g. either both attributes are mentions, or an event and a mention, or both are genes). Also, the total sum of extracted relations exceeds the number of final predicted relations, which is a consequence of their extraction in multiple sieves. Duplicates and loops were removed in the data cleaning sieve. The challenge test data set contains 290 mentions across 67 sentences. To detect relations in the test data, we trained our models on the joint development and training data. At the time of submission we did not use the gene relations processing sieve (see Sec. 4) because it had not yet been implemented. The results of the participants in the challenge are shown in Table 2. According to the official SER measure, our system (U. of Ljubljana) was ranked first. The other four competing systems were K. U. Leuven (Provoost and Moens, 2013), TEES-2.1 (Björne and Salakoski, 2013), IRISA-TexMex (Claveau, 2013) and EVEX (Hakala et al., 2013). Partici- pants aimed at a low number of substitutions, deletions and insertions, while increasing the number of matches. We got the least number of substitutions and fairly good results in the other three indicators, which gave the best final score. Fig. 7 shows the predicted gene regulation network with the relations that our system extracted from test data. This network does not exactly match our submission due to minor algorithm modifications after the submission deadline. Participant Conclusion We have proposed a sieve-based system for relation extraction from text. The system is based on linear-chain conditional random fields (LCRF) and domain-specific rules. In order to support the extraction of relations between distant mentions, we propose an approach called skip-mention linear chain CRF, which extends LCRF by varying the number of skipped mentions to form mention sequences. In contrast to common relation extraction approaches, we inferred a separate model for each relation type. We applied the proposed system to the BioNLP 2013 Gene Regulation Network Shared Task. The task was to reconstruct the gene regulation network of sporulation in the model organism B. subtilis. Our approach scored best among this year's submissions. Figure 3 : 3A mention sequence with zero skipmentions. This continues our example from Sec. 3. Figure 5 : 5Distribution of distances between two mentions connected with a relation. 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Personalized Extractive Summarization Using an Ising Machine Towards Real-time Generation of Efficient and Coherent Dialogue Scenarios

We propose a personalized dialogue scenario generation system which transmits efficient and coherent information with a real-time extractive summarization method optimized by an Ising machine. The summarization problem is formulated as a quadratic unconstraint binary optimization (QUBO) problem, which extracts sentences that maximize the sum of the degree of user's interest in the sentences of documents with the discourse structure of each document and the total utterance time as constraints. To evaluate the proposed method, we constructed a news article corpus with annotations of the discourse structure, users' profiles, and interests in sentences and topics. The experimental results confirmed that a Digital Annealer, which is a simulated annealing-based Ising machine, can solve our QUBO model in a practical time without violating the constraints using this dataset.

Personalized Extractive Summarization Using an Ising Machine Towards Real-time Generation of Efficient and Coherent Dialogue Scenarios November 10, 2021 Hiroaki Takatsu takatsu@pcl.cs.waseda.ac.jpkashikawa Waseda University TokyoJapan Takahiro Kashikawa FUJITSU LIMITED KanagawaJapan Koichi Kimura k.kimura@jp.fujitsu.com FUJITSU LIMITED KanagawaJapan Ryota Ando ando@naigaipc.co.jpmatsuyama@pcl.cs.waseda.ac.jp Naigai Pressclipping Bureau LtdTokyoJapan Yoichi Matsuyama Waseda University TokyoJapan Personalized Extractive Summarization Using an Ising Machine Towards Real-time Generation of Efficient and Coherent Dialogue Scenarios Proceedings of the Third Workshop on Natural Language Processing for Conversational AI the Third Workshop on Natural Language Processing for Conversational AINovember 10, 202116 We propose a personalized dialogue scenario generation system which transmits efficient and coherent information with a real-time extractive summarization method optimized by an Ising machine. The summarization problem is formulated as a quadratic unconstraint binary optimization (QUBO) problem, which extracts sentences that maximize the sum of the degree of user's interest in the sentences of documents with the discourse structure of each document and the total utterance time as constraints. To evaluate the proposed method, we constructed a news article corpus with annotations of the discourse structure, users' profiles, and interests in sentences and topics. The experimental results confirmed that a Digital Annealer, which is a simulated annealing-based Ising machine, can solve our QUBO model in a practical time without violating the constraints using this dataset. Introduction As mobile personal assistants and smart speakers become ubiquitous, the demand for dialogue-based media technologies has increased since they allow users to consume a fair amount of information via a dialogue form in daily life situations. Dialoguebased media is more restrictive than textual media. For example, when listening to an ordinary smart speaker, users can not skip unnecessary information or skim only for necessary information. Thus, it is crucial for future dialogue-based media to extract and efficiently transmit information that the users are particularly interested in without excess or deficiencies. In addition, the dialogue scenarios generated based on the extracted information should be coherent to aid in the proper understanding. Generating such efficient and coherent scenarios personalized for each user generally takes more time as the information source size and the number of target users increase. Moreover, the nature of conversational experiences requires personalization in real time. In this paper, we propose a personalized extractive summarization method formulated as a combinatorial optimization problem to generate efficient and coherent dialogue scenarios and demonstrate that an Ising machine can solve the problem at high speeds. As a realistic application of the proposed personalized summarization method for a spoken dialogue system, we consider a news delivery task (Takatsu et al., 2018). This news dialogue system proceeds the dialogue according to a primary plan to explain the summary of the news article and subsidiary plans to transmit supplementary information though question answering. As long as the user is listening passively, the system transmits the content of the primary plan. The personalized primary plan generation problem can be formulated as follows: From N documents with different topics, sentences that may be of interest to the user are extracted based on the discourse structure of each document. Then the contents are transmitted by voice within T seconds. Specifically, this problem can be formulated as an integer linear programming (ILP) problem, which extracts sentences that maximize the sum of the degree of user's interest in the sentences of documents with the discourse structure of each document and the total utterance time T as constraints. Because this ILP problem is NP-hard, it takes an enormous amount of time to find an optimal solution using the branch-and-cut method (Mitchell, 2002;Padberg and Rinaldi, 1991) as the problem scale becomes large. In recent years, non-von Neumann computers called Ising machines have been attracting attention as they can solve combinatorial optimization problems and obtain quasi-optimal solutions instantly (Sao et al., 2019). Ising machines can solve combi-natorial optimization problems represented by an Ising model or a quadratic unconstrained binary optimization (QUBO) model (Lucas, 2014;Glover et al., 2019). In this paper, we propose a QUBO model that generates an efficient and coherent personalized summary for each user. Additionally, we verify that our QUBO model can be solved by a Digital Annealer (Aramon et al., 2019;Matsubara et al., 2020), which is a simulated annealing-based Ising machine, in a practical time without violating the constraints using the constructed dataset. The contributions of this paper are three-fold: • The ILP and QUBO models for the personalized summary generation are formulated in terms of efficient and coherent information transmission. • To evaluate the effectiveness of the proposed method, we construct a Japanese news article corpus with annotations of the discourse structure, users' profiles, and interests in sentences and topics. • Experiments demonstrate that a Digital Annealer, which is a simulated annealing-based Ising machine, can solve our QUBO model in a practical time without violating the constraints. The rest of this paper is organized as follows. Section 2 discusses the related work. Section 3 overviews the annotations of the discourse structure and interest data collection. Section 4 details the proposed method. Section 5 describes the Digital Annealer. Section 6 evaluates the performance of the proposed method. Section 7 provides the conclusions and prospects. 2 Related work 2.1 Discourse structure corpus Typical datasets for discourse structure analysis are RST Discourse Treebank (Carlson et al., 2001), Discourse Graphbank (Wolf and Gibson, 2005), and Penn Discourse Treebank (Prasad et al., 2008). RST Discourse Treebank is a dataset constructed based on rhetorical structure theory (Mann and Thompson, 1988). Some studies have annotated discourse relations to Japanese documents. Kaneko and Bekki (2014) annotated the temporal and causal relations for segments obtained by decomposing the sentences of the balanced corpus of contemporary written Japanese (Maekawa et al., 2014) based on segmented discourse representation theory (Asher and Lascarides, 2003). Kawahara et al. (2014) proposed a method to annotate discourse relations for the first three sentences of web documents in various domains using crowdsourcing. They showed that discourse relations can be annotated in many documents over a short amount of time. Kishimoto et al. (2018) confirmed that making improvements such as adding language tests to the annotation criteria of Kawahara et al. (2014) can improve the annotation quality. When applying discourse structure analysis results to tasks such as document summarization (Hirao et al., 2013;Yoshida et al., 2014;Kikuchi et al., 2014;Hirao et al., 2015) or dialogue (Feng et al., 2019), a dependency structure, which directly expresses the parent-child relationship between discourse units, is preferable to a phrase structure such as a rhetorical structure tree. Although methods have been proposed to convert a rhetorical structure tree into a discourse dependency tree (Li et al., 2014;Hirao et al., 2013), the generated trees depends on the conversion algorithm . Yang and Li (2018) proposed a method to manually annotate the dependency structure and discourse relations between elementary discourse units for abstracts of scientific papers, and then constructed SciDTB. In this study, we construct a dataset suitable to build summarization or dialogue systems that transmit personalized information while considering the coherence based on the discourse structure. Experts annotated the inter-sentence dependencies, discourse relations, and chunks, which are highly cohesive sets of sentences, for Japanese news articles. Users' profiles and interests in the sentences and topics of news articles were collected via crowdsourcing. Personalized summarization As people's interests and preferences diversify, the demand for personalized summarization technology has increased (Sappelli et al., 2018). Summaries are classified as generic or user-focused, based on whether they are specific to a particular user (Mani and Bloedorn, 1998). Unlike generic summaries generated by extracting important information from the text, user-focused summaries are generated based not only on important information but also on the user's interests and preferences. Most user-focused summarization methods rank sentences using a score calculated considering user's characteristics and subsequently generate a summary by extracting higher-ranked sentences (Díaz and Gervás, 2007;Yan et al., 2011;Hu et al., 2012). However, such conventional user-focused methods tend to generate incoherent summaries. Generic summarization methods, which consider the discourse structure of documents, have been proposed to maintain coherence (Kikuchi et al., 2014;Hirao et al., 2015;Xu et al., 2020). To achieve both personalization and coherence simultaneously, we propose ILP and QUBO models to extract sentences based on the degree of user's interest and generate a personalized summary for each user while maintaining coherence based on the discourse structure. Datasets We constructed a news article corpus with annotations of the discourse structure, users' profiles, and interests in sentences and topics. Experts annotated the inter-sentence dependencies, discourse relations, and chunks for the Japanese news articles. Users' profiles and interests in the sentences and topics of news articles were collected via crowdsourcing. Discourse structure dataset Two web news clipping experts annotated the dependencies, discourse relations, and chunks for 1,200 Japanese news articles. Each article contained between 15-25 sentences. The articles were divided into six genres: sports, technology, economy, international, society, and local. In each genre, we manually selected 200 articles to minimize topic overlap. The annotation work was performed in the order of dependencies, discourse relations, and chunks. The discourse unit was a sentence, which represents a character string separated by an ideographic full stop. Dependency annotation The conditions in which sentence j can be specified as the parent of sentence i are as follows: • In the original text, sentence j appears before sentence i. • The flow of the story is natural when reading from the root node in order according to the tree structure and reading sentence i after sentence j. • The information from the root node to sentence j is the minimum information necessary to understand sentence i. • If it is possible to start reading from sentence i, the parent of sentence i is the root node. Discourse relation annotation A discourse relation classifies the type of semantic relationship between the child sentence and the parent sentence. We defined the following as discourse relations: Start, Result, Cause, Background, Correspondence, Contrast, Topic Change, Example, Conclusion, and Supplement. An annotation judgment was made while confirming whether both the definition of the discourse relation and the dialogue criterion were met. The dialogue criterion is a judgment based on whether the response is natural according to the discourse relation. For example, the annotators checked whether it was appropriate to present a child sentence as an answer to a question asking the cause, such as "Why?" after the parent sentence. Chunk annotation A chunk is a highly cohesive set of sentences. If a parent sentence should be presented with a child sentence, it is regarded as a chunk. A hard chunk occurs when the child sentence provides information essential to understand the content of the parent sentence. Examples include when the parent sentence contains a comment and the child sentence contains the speaker's information or when a procedure is explained over multiple sentences. A soft chunk occurs when the child sentence is useful to prevent a biased understanding of the content of the parent sentence, although it does not necessarily contain essential information to understand the parent sentence itself. An example is explaining the situation in two countries related to a subject, where the parent sentence contains one explanation and the child sentence contains another. Interest dataset Participants were recruited via crowdsourcing. They were asked to answer a profile questionnaire and an interest questionnaire. We used 1,200 news articles, which were the same as those used in the discourse structure dataset. We collected the questionnaire results of 2,507 participants. Each participant received six articles, one from each genre. The six articles were distributed so that the total number of sentences was as even as possible across participants. Each article was reviewed by at least 11 participants. Profile questionnaire The profile questionnaire collected the following information: gender, age, residential prefecture, occupation type, industry type, hobbies, frequency of checking news (daily, 4-6 days a week, 1-3 days a week, or 0 days a week), typical time of day news is checked (morning, afternoon, early evening, or night), methods to access the news (video, audio, or text), tools used to check the news (TV, newspaper, smartphone, etc.), newspapers, websites, and applications used to check the news (Nihon Keizai Shimbun, LINE NEWS, SNS, etc.), whether a fee was paid to check the news, news genre actively checked (economy, sports, etc.), and the degree of interest in each news genre (not interested at all, not interested, not interested if anything, interested if anything, interested, or very interested). Interest questionnaire Participants read the text of the news article and indicated their degree of interest in the content of each sentence. Finally, they indicated their degree of interest in the topic of the article. The degree of interest was indicated on six levels: 1, not interested at all; 2, not interested; 3, not interested if anything; 4, interested if anything; 5, interested; or 6, very interested. Methods We propose an integer linear programming (ILP) model and a quadratic unconstraint binary optimization (QUBO) model for the personalized summary generation in terms of efficient and coherent information transmission. Integer linear programming model We considered a summarization problem, which extracts sentences that user u may be interested in from the selected N documents and then transmits them by voice within T seconds. The summary must be of interest to the user, coherent, and not redundant. Therefore, we formulated the summarization problem as an integer linear programming problem in which the objective function is defined by the balance between a high degree of interest in the sentences and a low degree of similarity between the sentences with the discourse structure as constraints. This is expressed as max. k∈D u N i<j∈S k b u ki b u kj (1 − r kij ) y kij(1)s.t. ∀k, i, j : x ki ∈ {0, 1}, y kij ∈ {0, 1} k∈D u N i∈S k t ki x ki ≤ T (2) ∀k < l : i∈S k x ki − i∈S l x li ≤ L (3) ∀k, i : j = f k (i) , x ki ≤ x kj (4) ∀k, m, i ∈ C km : j∈C km x kj = |C km | × x ki (5) ∀k, i, j : y kij − x ki ≤ 0 (6) ∀k, i, j : y kij − x kj ≤ 0 (7) ∀k, i, j : x ki + x kj − y kij ≤ 1(8) Table 1 explains each variable. Here, the i-th sentence of the k-th document is expressed as s ki . r kij represents the cosine similarity between the bag-of-words constituting s ki and s kj . Equation 2 is a constraint restricting the utterance time of the summary to T seconds or less. Equation 3 is a constraint restricting the bias of the number of extracting sentences between documents to L sentences or less. Equation 4 is a constraint in which the parent s kj of s ki in the discourse dependency tree must be extracted when s ki is extracted. Equation 5 is a constraint requiring other sentences in the chunk to be extracted when extracting s ki in a chunk. Equations 6-8 are constraints that set y kij = 1 when s ki and s kj are selected. The maximum bias in the number of extracting sentences between documents L is calculated by the following formulas, which are based on the maximum summary length T , the number of documents N , and the average utterance time of the sentencest. L = n √ N + 0.5 (9) n = T t × N(10) n represents the expected number of sentences to be extracted from one document. L is the value obtained by dividingn by the square root of the number of documents and rounding the result. Quadratic unconstrained binary optimization model To solve the combinatorial optimization problem with an Ising machine, the problem must be formulated with the Ising model or the quadratic unconstraint binary optimization (QUBO) model (Lucas, 2014;Glover et al., 2019 H = H 0 + λ 1 H 1 + λ 2 H 2 + λ 3 H 3 + λ 4 H 4 (11) H 0 = − k∈D u N i<j∈S k b u ki b u kj (1 − r kij ) x ki x kj (12) H 1 =   T − k∈D u N i∈S k t ki x ki − log 2 (T −1) n=0 2 n y n    2(13)H 2 = k<l∈D u N   L −   i∈S k x ki − i∈S l x li   − log 2 (L−1) n=0 2 n z n    2(14)H 3 = x ki − x ki x kj=f k (i) 2(15)H 4 = k∈D u N m∈C k i∈C km   j∈C km x kj − |C km | × x ki 2(16) where y n and z n are slack variables introduced to convert inequality constraints into equality constraints, λ 1 , λ 2 , λ 3 , and λ 4 are the weight coefficients for each constraint. QUBO models are solved by a simulated annealing-based method (Aramon et al., 2019) or parallel tempering (known as replica-exchange Monte Carlo) (Matsubara et al., 2020). In these methods, multiple solution candidates can be obtained by annealing in parallel with different initial values. However, these methods do not guarantee that constraint violations will not occur. Therefore, the solution candidate with the lowest constraint violation score and the highest score of the objective function is adopted. The constraint violation score E total is calculated as E total = E dep + E chunk + E bias + E time (17) E bias = max L − L, 0(18)E time = max T − T, 0(19) where E dep is the number of dependency errors, E chunk is the number of chunk errors,L is the maximum bias in the number of extracted sentences between documents,T is the total utterance time of the extracted sentences. Digital Annealer Quantum computing technologies are categorized into two types: quantum gate computers (Arute et al., 2019;Gyongyosi, 2020) and Ising machines. Quantum gate computers are for universal computing, whereas Ising machines specialize in searching for solutions of combinatorial optimization problems. Ising machines can be subdivided into two categories: quantum annealing machines (Johnson et al., 2011;Bunyk et al., 2014;Maezawa et al., 2019) and simulated annealing machines (Yamaoka et al., 2016;Okuyama et al., 2017;Aramon et al., 2019;Matsubara et al., 2020). Quantum annealing machines search solutions using quantum bits, which are made of quantum devices such as a superconducting circuit. By contrast, simulated annealing machines use a digital circuit. Digital Annealer (Aramon et al., 2019;Matsubara et al., 2020) is a type of simulated annealing machines with a new digital circuit architecture, which is designed to solve combinatorial optimization problems efficiently. This study uses a Digital Annealing Unit (DAU) of the second generation Digital Annealer. The DAU has an annealing mode (Aramon et al., 2019) or parallel tempering mode (Matsubara et al., 2020) to solve QUBO models. It can handle up to 4,096 binary variables with 64-bit precision or as many as 8,192 binary variables with 16-bit precision. Hereinafter, the DAU in the annealing mode is referred to as DAU-AM, and the DAU in the parallel tempering mode is referred to as DAU-PTM. Experiments Using the constructed dataset, we evaluated the performance of the personalized summarization method for dialogue scenario planning. The ILP model was solved by the branch-and-cut method 1 (Mitchell, 2002;Padberg and Rinaldi, 1991) on the CPU. Hereinafter, this method is referred to as CPU-CBC. We used CPU-CBC as a benchmark and compared the performance of CPU-CBC, DAU-AM and DAU-PTM. Experimental setup We used 2,117 participants data which the answer time of the six articles was between 5 and 30 minutes. The performance was evaluated for two cases. The first transmitted N = 6 articles in T = 450 seconds and the second transmitted the top N = 3 articles with high interest in the topic in T = 270 seconds. Sentences in the news articles was synthesized by AITalk 4.1 2 to calculate the duration of speech. The maximum summary length T was calculated as T = T − (N − 1) × (q d − q s ), where T denotes the total utterance time of the primary plan, q s denotes the pause between sentences, and q d denotes the pause between documents. Here, q s = 1 second and q d = 3 seconds. The value obtained by adding q s to the playback time of the synthesized audio file was set as t ki . The PULP_CBC_CMD solver of the PuLP 3 , which is a Python library for linear programming optimization, was used to solve the ILP model. Python and PuLP versions were 3.7.6 and 2.4, respectively. The parameter for the number of threads of PULP_CBC_CMD was set to 30. The execution time of the solving function was measured on the Google Compute Engine 4 with the following settings: OS, Ubuntu 18.04; CPU, Xeon (2.20 GHz, 1 https://projects.coin-or.org/Cbc 2 https://www.ai-j.jp/product/ voiceplus/manual/ 3 https://coin-or.github.io/pulp/ 4 https://cloud.google.com/compute/?hl= en Figure 1 shows the number of problems for a given number of sentences when N = 3. These are the top three articles with the highest degree of interest in the topic among the six articles distributed to each participant. Figure 2 shows the number of problems for a given number of sentences when N = 6. Since the six articles were distributed so that the total number of sentences was as even as possible across participants, the variation in the number of sentences was small. The DAU must set the number of bits parameter from 1, 024, 2,048, 4,096, or 8,192, depending on the problem size. In the experimental setting of N = 3, T = 270, the 2,090 QUBO problems were less than 2,048 bits and the 27 QUBO problems were less than 4,096. On the other hand, in the experimental setting of N = 6, T = 450, the 2,017 QUBO problems were less than 4,096 bits and the 100 QUBO problems were less than 8,192. In the latter experiment, these 100 participants data were excluded because the calculation precision of the DAU decreased when the problem size exceeds 4,096 bits. The number of replicas in DAU-PTM and the number of runs of annealing in DAU-AM were 128. Since the performance of the DAU mainly depended on λ and the number of searches in one annealing (#iteration), these parameters were adjusted to prevent constraint violations. Evaluation metrics We used EoIT β (efficiency of information transmission) (Takatsu et al., 2021) as the evaluation metric. When C is the coverage of sentences annotated as "very interested," "interested," or "interested if anything," and E is the exclusion rate of the sentences annotated as "not interested at all," "not interested," or "not interested if anything," EoIT β is defined based on the weighted F-measure (Chinchor, 1992) as EoIT β = 1 + β 2 × C × E β 2 × C + E(20) When β = 2, the exclusion rate is twice as important as the coverage. Compared to textual media, which allows readers to read at their own pace, dialogue-based media does not allow users to skip unnecessary information or skim necessary information while listening. Consequently, we assumed that the exclusion rate is more important than the coverage in information transmission by spoken dialogue and set β = 2. Experimental results Tables 2 The processing time of CPU-CBC became longer as the number of sentences increased. Even if the number of sentences was the same, the processing time varied widely. On the other hand, the processing time of DAU-AM and DAU-PTM changed according to the size of the QUBO problems and the number of iterations, regardless of the number of sentences. At N = 6, the size of all QUBO problems was less than 4,096, and the processing time of DAU was almost constant regardless of the number of sentences. At N = 3, the size of the QUBO problems was less than 2,048 when the number of sentences was 59 or less, and less than 4,096 when the number of sentences was 63 or more. On the other hand, when the number of sentences was between 60 and 62, problems with less than 2,048 and problems with less than 4,096 were mixed due to the number of chunks. Although the DAU is inferior to CPU-CBC in the EoIT, its processing time is considerably shorter than CPU-CBC. In addition, the processing time of the DAU can be estimated in advance based on the size of the QUBO problems and the number of iterations. Discussion The DAU does not guarantee that constraint violations will not occur. Although the DAU did not induce constraint violations in the parameter settings shown in the Tables 2 and 3, constraint violations occurred when the number of iterations was too small or the value of λ was inappropriate. In the case that delivers six articles consisting of 15 to 25 sentences, one DAU can generate personalized summaries on the scale of 100,000 users within 6 hours since about 0.2 seconds per person was necessary to generate a summary. In other words, an application with 100,000 users can prepare personalized summaries of the previous night's news by the next morning. The second generation Digital Annealer can only handle problems up to 8,192 bits. However, a third generation Digital Annealer, which can solve problems on the scale of 100,000 bits, is currently under development (Nakayama et al., 2021). Spoken dialogue systems capable of extracting and presenting personalized information for each user from a huge amount of data in real time will be developed in the near future. Conclusion This paper proposed a quadratic unconstraint binary optimization (QUBO) model for the real-time personalized summary generation in terms of effi-cient and coherent information transmission. The model extracts sentences that maximize the sum of the degree of user's interest in the sentences of documents with the discourse structure of each document and total utterance time as constraints. To evaluate the effectiveness of the proposed method, we constructed a Japanese news article corpus with annotations of the discourse structure, users' profiles, and interests in sentences and topics. Experiments demonstrated that our QUBO model could be solved by a Digital Annealer, which is a simulated annealing-based Ising machine, in a practical time without violating the constraints using the dataset. In the future, we will verify the effect of personalized scenarios on the spoken dialogue system. Definition of the discourse relations Start means that the parent of the sentence is the root node. Result means that the child sentence is the result of the parent sentence. Cause means that the child sentence is the cause of the parent sentence and includes event origin, the basis of the author's claim, and the reason. Background means that the parent sentence states facts or events, and the child sentence provides the background or premise. Correspondence means that the child sentence answers the question in the parent sentence. It also includes countermeasures or examinations of problems or issues. Contrast means that the parent sentence and the child sentence have a contrasting relationship. Topic Change means that the topic of the parent sentence changes in the child sentence, which includes a subtopic level change. Example means that the child sentence provides an instance to support the statement in the parent sentence. Conclusion means that the child sentence is a summary or conclusion of the story up to the parent sentence. Supplement means that the child sentence provides details or supplementary information about what is stated in the parent sentence. In a broad sense, although the above discourse relations are also included in supplement, herein, Supplement covers the inter-sentence relations that are not included in the aforementioned discourse relations. Quality of the discourse annotations A one-month training period was established, and discussions were held until the annotation criteria of the two annotators matched. To validate the inter-rater reliability, the two annotators annotated the same 34 articles after the training period. The Cohen's kappa of dependencies, discourse relations, and chunks were 0.960, 0.943, and 0.895, respectively. To calculate kappa of the discourse relations, the comparison was limited to the inter-sentence dependencies in which the parent sentence matched. To calculate kappa of the chunks, we set the label of the sentence selected as the hard chunk, soft chunk, and other to "1, 2, and 0," respectively. Then we compared the labels between sentences. Given the high inter-rater reliability, we concluded that the two annotators could cover different assignments separately. Statistics of the dataset Figure 6: Tree depth distribution, which is the maximum number of sentences from the root node to the leaf nodes for each article. Average tree depth per article is 6.5 sentences. Figure 7: Tree width distribution, which is the number of leaf nodes for each article. Average tree width per article is 7.5 sentences. Figure 1 : 3 Figure 2 : 132Number of problems for a given number of sentences when N = Number of problems for a given number of sentences when N = 6 32 cores); Memory, 64 GB. 3 : 3Information transmission efficiency of the summaries (N = 6, T = 450) and 3 show the results of N = 3, T = 270 and N = 6, T = 450, respectively. The values of the evaluation metrics and processing times represent the average. DAU-AM had a shorter processing time than DAU-PTM for the same number of iterations. However, in terms of the EoIT, DAU-PTM was higher than DAU-AM. The EoIT of DAU-PTM improved as the number of iterations increased, but the processing time also increased. On the other hand, increasing the number of iterations did not improve the EoIT of DAU-AM.Figures 3 and 4 show the distributions of processing time for each number of sentences in the experimental settings of N = 3, T = 270 and N = 6, T = 450, respectively. The number of iterations for DAU-AM and DAU-PTM was 1,000. Figure 3 :Figure 4 : 34Processing time for each number of sentences (N = 3, T = 270) Processing time for each number of sentences (N = 6, T = 450) Figure 5 : 5Annotation example of the discourse structure. [ * ] indicates the sentence position in the original text. Dependency annotation, discourse relation annotation, and chunk annotation criteria are described in 3.1.1, 3.1.2, and 3.1.3, respectively. Figure 8 : 8Frequencies of discourse relations in the dataset: Start = 1,221; Result = 400; Cause = 691; Background = 1,343; Correspondence = 851; Contrast = 638; Topic Change = 220; Example = 709; Conclusion = 920; and Supplement = 14,609. Figure 9 : 9Frequencies of chunks in the dataset. There are 231 hard chunks and 699 soft chunks. Average number of sentences per chunk is 2.15. Figure 10 : 10Percentage of each interest level calculated from the questionnaire results, which includes 15,042 articles and 268,509 sentences. Figure 11 : 11Average interest level of the sentences for each depth of the discourse dependency tree. Table 1 : 1Variable definitions in the interesting sentence extraction methodx ki Whether sentence s ki is selected y kij Whether both s ki and s kj are selected b u ki Degree of user u's interest in s ki r kij Similarity between s ki and s kj IDs of the selected N documents for user u S k Sentence IDs contained in document d k C km Sentence IDs contained in chunk m of d kt ki Utterance time of s ki (seconds) T Maximum summary length (seconds) L Maximum bias in the number of extracting sentences between documents f k (i) Function that returns the parent ID of s ki D u N 1}, respectively. Ising machines search for the lowest energy state of the Ising model or the QUBO model. Here, we formulated the QUBO model based on the above ILP model. The energy function (or Hamiltonian) H of the QUBO model is defined as). The energy functions of the Ising model and the QUBO model are described by the quadratic form of spin variables {−1, +1} and binary variables {0, Table 2 : 2Information transmission efficiency of the summaries (N = 3, T = 270)Parameters of the Digital Annealer Efficiency of information transmission Processing λ1 λ2 λ3 λ4 #iteration Coverage Exclusion rate EoIT1 EoIT2 time (sec) CPU-CBC (30 threads) - - - - - 0.687 0.726 0.672 0.696 18.9 DAU-AM 10 2 10 6 10 10 10 10 3 0.638 0.612 0.584 0.593 0.0570 DAU-PTM 10 2 10 5 10 9 10 10 3 0.656 0.637 0.608 0.618 0.245 10 4 0.669 0.661 0.627 0.639 1.76 Table framework for interactive personalized summarization. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 1342-1351. An Yang and Sujian Li. 2018. SciDTB: Discourse dependency treebank for scientific abstracts. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pages 444-449. Yasuhisa Yoshida, Jun Suzuki, Tsutomu Hirao, and Masaaki Nagata. 2014. Dependency-based discourse parser for single-document summarization. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pages 1834-1839. Table 4 : 4Example answers of the interest questionnaire. Interest level in the topic is assigned to the title [0]. (1, not interested at all; 2, not interested; 3, not interested if anything; 4, interested if anything; 5, interested; 6, very interested) Table 5 : 5Examples of user profiles AcknowledgmentsThis work was supported by Japan Science and Technology Agency (JST) Program for Creating STart-ups from Advanced Research and Technology (START), Grant Number JPMJST1912 "Commercialization of Socially-Intelligent Conversational AI Media Service."Appendix Physics-inspired optimization for quadratic unconstrained problems using a Digital Annealer. 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Improving Speculative Language Detection using Linguistic Knowledge

In this paper we present an iterative methodology to improve classifier performance by incorporating linguistic knowledge, and propose a way to incorporate domain rules into the learning process. We applied the methodology to the tasks of hedge cue recognition and scope detection and obtained competitive results on a publicly available corpus.

Improving Speculative Language Detection using Linguistic Knowledge Association for Computational LinguisticsCopyright Association for Computational Linguistics13 July 2012. 2012 Guillermo Moncecchi Facultad de Ingeniería Laboratoire MoDyCo Universidad de la República Montevideo Uruguay Jean-Luc Minel Facultad de Ingeniería Université Paris Ouest Nanterre La DéfenseFrance Dina Wonsever Universidad de la República Montevideo Uruguay Improving Speculative Language Detection using Linguistic Knowledge Proceedings of the ACL-2012 Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics (ExProM-2012) the ACL-2012 Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics (ExProM-2012)Jeju, Republic of KoreaAssociation for Computational Linguistics13 July 2012. 2012 In this paper we present an iterative methodology to improve classifier performance by incorporating linguistic knowledge, and propose a way to incorporate domain rules into the learning process. We applied the methodology to the tasks of hedge cue recognition and scope detection and obtained competitive results on a publicly available corpus. Introduction A common task in Natural Language Processing (NLP) is to extract or infer factual information from textual data. In the field of natural sciences this task turns out to be of particular importance, because science aims to discover or describe facts from the world around us. Extracting these facts from the huge and constantly growing body of research articles in areas such as, for example, molecular biology, becomes increasingly necessary, and has been the subject of intense research in the last decade (Ananiadou et al., 2006). The fields of information extraction and text mining have paid particular attention to this issue, seeking to automatically populate structured databases with data extracted or inferred from text. In both cases, the problem of speculative language detection is a challenging one, because it may correspond to a subjective attitude of the writer towards the truth value of certain facts, and that information should not be lost when the fact is extracted or inferred. When researchers express facts and relations in their research articles, they often use speculative language to convey their attitude to the truth of what is said. Hedging, a term first introduced by Lakoff (1973) to describe 'words whose job is to make things fuzzier or less fuzzy' is 'the expression of tentativeness and possibility in language use' (Hyland, 1995), and is extensively used in scientific writing. Hyland (1996a) reports one hedge in every 50 words of a corpus of research articles; Light et al. (2004) mention that 11% of the sentences in MEDLINE contain speculative language. Vincze et al. (2008) report that 18% of the sentences in the scientific abstracts section of the Bioscope corpus correspond to speculations. Early work on speculative language detection tried to classify a sentence either as speculative or non-speculative (see, for example, Medlock and Briscoe (2007)). This approach does not take into account the fact that hedging usually affects propositions or claims (Hyland, 1995) and that sentences often include more than one of them. When the Bioscope corpus (Vincze et al., 2008) was developed the notions of hedge cue (corresponding to what was previously called just 'hedges' in the literature) and scope (the propositions affected by the hedge cues) were introduced. In this context, speculative language recognition can be seen as a two-phase process: first, the existence of a hedge cue in a sentence is detected, and second, the scope of the induced hedge is determined. This approach was first used by Morante et al. (2008) and subsequently in many of the studies presented in the CoNLL-2010 Conference Shared Task (Farkas et al., 2010a), and is the one used in this paper. For example, the sentence (1) This finding { suggests suggests that the BZLF1 promoter { may may be regulated by the degree of squamous differentiation} may } suggests . contains the word 'may' that acts as a hedge cue (i.e. attenuating the affirmation); this hedge only affects the propositions included in the subordinate clause that contains it. Each of these phases can be modelled (albeit with some differences, described in the following sections) as a sequential classification task, using a similar approach to that commonly used for named entity recognition or semantic labelling: every word in the sentence is assigned a class, identifying spans of text (as, for example, scopes) with, for example, a special class for the first and last element of the span. Correctly learning these classes is the computational task to be solved. In this paper we present a methodology and machine learning system implementing it that, based on previous work on speculation detection, studies how to improve recognition by analysing learning errors and incorporating advice from domain experts in order to solve the errors without hurting overall performance. The methodology proposes the use of domain knowledge rules that suggest a class for an instance, and shows how to incorporate them into the learning process. In our particular task domain knowledge is linguistic knowledge, as hedging and scopes issues are general linguistic devices. In this paper we are going both terms interchangeably. The paper is organized as follows. In Section 2 we review previous theoretical work on speculative language and the main computational approaches to the task of detecting speculative sentences. Section 3 briefly describes the corpus used for training and evaluation. In Section 4 we present the specific computational task to which our methodology was applied. In Section 5 we present the learning methodology we propose to use, and describe the system we implemented, including lexical, syntactic and semantic attributes we experimented with. We present and discuss the results obtained in Section 6. Finally, in Section 7 we analyse the approach presented here and discuss its advantages and problems, suggesting future lines of research. Related work The grammatical phenomenon of modality, defined as 'a category of linguistic meaning having to do with the expression of possibility and necessity' (von Fintel, 2006) has been extensively studied in the linguistic literature. Modality can be expressed using different linguistic devices: in English, for example, modal auxiliaries (such as 'could' or 'must'), adverbs ('perhaps'), adjectives ('possible'), or other lexical verbs ('suggest','indicate'), are used to express the different ways of modality. Other languages express modality in different forms, for example using the subjunctive mood. Palmer (2001) considers modality as the grammaticalization of speakers' attitudes and opinions, and epistemic modality, in particular, applies to 'any modal system that indicates the degree of commitment by the speaker to what he says'. Although hedging is a concept that is closely related to epistemic modality, they are different: modality is a grammatical category, whereas hedging is a pragmatic position (Morante and Sporleder, 2012). This phenomenon has been theoretically studied in different domains and particularly in scientific writing (Hyland, 1995;Hyland, 1996b;Hyland, 1996a). From a computational point of view, speculative language detection is an emerging area of research, and it is only in the last five years that a relatively large body of work has been produced. In the remainder of this section, we survey the main approaches to hedge recognition, particularly in English and in research discourse. Medlock and Briscoe (2007) applied a weakly supervised learning algorithm to classify sentences as speculative or non-speculative, using a corpus they built and made publicly available. Morante and Daelemans (2009) not only tried to detect hedge cues but also to identify their scope, using a metalearning approach based on three supervised learning methods. They achieved an F1 of 84.77 for hedge identification, and 78.54 for scope detection (using gold-standard hedge signals) in the Abstracts sections of the Bioscope corpus. Task 2 of the CoNLL-2010 Conference Shared Task (Farkas et al., 2010b) proposed solving the problem of in-sentence hedge cue phrase identi-fication and scope detection in two different domains (biological publications and Wikipedia articles), based on manually annotated corpora. The evaluation criterion was in terms of precision, recall and F-measure, accepting a scope as correctly classified if the hedge cue and scope boundaries were both correctly identified. The best result on hedge cue identification (Tang et al., 2010) obtained an F-score of 81.3 using a supervised sequential learning algorithm to learn BIO classes from lexical and shallow parsing information, also including certain linguistic rules. For scope detection, Morante et al. (2010) obtained an F-score of 57.3, using also a sequence classification approach for detecting boundaries (tagged in FOL format, where the first token of the span is marked with an F, while the last one is marked with an L). The attributes used included lexical information, dependency parsing information, and some features based on the information in the parse tree. The approximation of Velldal et al. (2010) for scope detection was somewhat different: they developed a set of handcrafted rules, based on dependency parsing and lexical features. With this approach, they achieved an F-score of 55.3, the third best for the task. Similarly, Kilicoglu and Bergler (2010) used a pure rule-based approach based on constituent parse trees in addition to syntactic dependency relations, and achieved the fourth best Fscore for scope detection, and the highest precision of the whole task (62.5). In a recent paper, Velldal et al. (2012) reported a better F-score of 59.4 on the same corpus for scope detection using a hybrid approach that combined a set of rules on syntactic features and n-gram features of surface forms and lexical information and a machine learning system that selected subtrees in constituent structures. Corpus The system presented in this paper uses the Bioscope corpus (Vincze et al., 2008) as a learning source and for evaluation purposes. The Bioscope corpus is a freely available corpus of medical free texts, biological full papers and biological abstracts, annotated at a token level with negative and speculative keywords, and at sentence level with their linguistic scope. (2008), gives some statistics related to hedge cues and sentences for the three sub corpora included in Bioscope. For the present study, we usee only the Abstract sub corpus for training and evaluation. We randomly separated 20% of the corpus, leaving it for evaluation purposes. We further sub-divided the remaining training corpus, separating another 20% that was used as a held out corpus. All the models presented here were trained on the resulting training corpus and their performance evaluated on the held out corpus. The final results were computed on the previously unseen evaluation corpus. Task description From a computational point of view, both hedge cue identification and scope detection can be seen as a sequence classification problem: given a sentence, classify each token as part of a hedge cue (or scope) or not. In almost every classification problem, two main approaches can be taken (although many variations and combinations exist in the literature): build the classifier as a set of handcrafted rules, which, from certain attributes of the instances, decide which category it belongs to, or learn the classifier from previously annotated examples, in a supervised learning approach. The rules approach is particularly suitable when domain experts are available to write the rules, and when features directly represent linguistic information (for example, POS-tags) or other types of domain information. It is usually a time-consuming task, but it probably grasps the subtleties of the linguistic phenomena studied better, making it possible to take them into account when building the classifier. The supervised learning approach needs tagged data; in recent years the availability of tagged text has grown, and this type of method has become the state-of-the-art solution for many NLP problems. In our particular problem, we have both tagged data and expert knowledge (represented by the body of work on modality and hedging), so it seems reasonable to see how we can combine the two methods to achieve better classification performance. Identifying hedge cues The best results so far for this task used a token classification approach or sequential labelling techniques, as Farkas et al. (2010b) note. In both cases, every token in the sentence is assigned a class label indicating whether or not that word is acting as a hedge cue. To allow for multiple-token hedge cues, we identify the first token of the span with the class B and every other token in the span with I, keeping the O class for every token not included in the span, as the following example shows: (2) The/O findings/O indicate/B that/I MNDA/O expression/O is/O . . . [ 401.8] After token labelling, hedge cue identification can be seen as the problem of assigning the correct class to each token of an unlabelled sentence. Hedge cue identification is a sequential classification task: we want to assign classes to an entire ordered sequence of tokens and try to maximize the probability of assigning the correct classes to every token in the sequence, considering the sequence as a whole, not just as a set of isolated tokens. Determining the scope of hedge cues The second sub-task involves marking the part of the sentence affected by the previously identified hedge cue. Scopes are also spans of text (typically longer than multi-word hedge cues), so we could use the same reduction to a token classification task. Being longer, FOL classes are usually used for classification, identifying the first token of the scope as F, the last token as L and any other token in the sentence as O. Scope detection poses an additional problem: hedge cues cannot be nested, but scopes (as we have already seen) usually are. In example 1, the scope of 'may' is nested within the scope of 'suggests'. To overcome this, Morante and Daelemans (2009) propose to generate a different learning example for each cue in the sen-tence. In this setting, each example becomes a pair labelled sentence, hedge cue position . So, for example 1, the scope learning instances would be: ( Learning on these instances, and using a similar approach to the one used in the previous task, we should be able to identify scopes for previously unseen examples. Of course, the two tasks are not independent: the success of the second one depends on the success of the first. Accordingly, evaluation of the second task can be done using gold standard hedge cues or with the hedge cues learned in the first task. Methodology and System Description To approach both sequential learning tasks, we follow a learning methodology (depicted in Figure 1), that starts with an initial guess of attributes for supervised learning and a learning method, and tries to improve its performance by incorporating domain knowledge. We consider that expressing this knowledge through rules (instead of learning features) is a better way for a domain expert to suggest new useful information or to generalize certain relations between attributes and classification results when the learning method cannot achieve this because of insufficient training data. These rules, of course, have to be converted to attributes to incorporate them into the learning process. These attributes are what we call knowledge rules and their generation will be described in the Analysis section. Preprocessing Before learning, we propose to add every possible item of external information to the corpus so as to integrate different sources of knowledge (either the result of external analysis or in the form of semantic resources). After this step, all the information is consolidated into a single structure, facilitating subsequent analysis. In our case, we incorporate POS-tagging information, resulting from the application of the GENIA tagger (Tsuruoka et al., 2005), and deep syntax information obtained with the application of the Stanford Parser (Klein and Manning, 2003), leading to a syntax-oriented representation of the training data. For a detailed description of the enriching process, the reader is referred to Moncecchi et al. (2010). Initial Classifier The first step for improving performance is, of course, to select an initial set of learning features, and learn from training data to obtain the first classifier, in a traditional supervised learning scenario. The sequential classification method will depend on the addressed task. After learning, the classifier is applied on the held out corpus to evaluate its performance (usually in terms of Precision, Recall and F1-measure), yielding performance results and a list of errors for analysis. This information is the source for subsequent linguistic analysis. As such, it seems important to provide ways to easily analyse instance attributes and learning errors. For our tasks, we have developed visualization tools to inspect the tree representation of the corpus data, the learning attributes, and the original and predicted classes. Analysis From the classifier results on the held-out corpus, an analysis phase starts, which tries to incorporate linguistic knowledge to improve performance. One typical form of introducing new information is through learning features: for example, we can add a new attribute indicating if the current instance (in our case, a sentence token) belongs to a list of common hedge cues. However, linguistic or domain knowledge can also naturally be stated as rules that suggest the class or list of classes that should be assigned to instances, based on certain conditions on features, linguistic knowledge or data observation. For example, based on corpus annotation guidelines, a rule could state that the scope of a verb hedge cue should be the verb phrase that includes the cue, as in the expression (5) This finding { suggests suggests that the BZLF1 promoter may be regulated by the degree of squamous differentiation} suggests . We assume that these rules take the form 'if a condition C holds then classify instance X with class Y'. In the previous example, assuming a FOL format for scope identification, the token 'suggest' should be assigned class F and the token 'differentiation' should be assigned class L, assigning class O to every other token in the sentence. The general problem with these rules is that as we do not know in fact if they always apply, we do not want to directly modify the classification results, but to incorporate them as attributes for the learning task. To do this, we propose to use a similar approach to the one used by Rosá (2011), i.e. to incorporate these rules as a new attribute, valued with the class predictions of the rule, trying to 'help' the classifier to detect those cases where the rule should fire, without ignoring the remaining attributes. In the previous example, this attribute would be (when the rule condition holds) valued F or L if the token corresponds to the first or last word of the enclosing verb phrase, respectively. We have called these attributes knowledge rules to reflect the fact that they suggest a classification result based on domain knowledge. This configuration allows us to incorporate heuristic rules without caring too much about their potential precision or recall ability: we expect the classification method to do this for us, detecting correlations between the rule result (and the rest of the attributes) and the predicted class. There are some cases where we do actually want to overwrite classifier results: this is the case when we know the classifier has made an error, because the results are not well-formed. For example, we have included a rule that modifies the assigned classes when the classifier has not exactly found one F token and one L token, as we know for sure that something has gone wrong. In this case, we decided to assign the scope based on a series of postprocessing rules: for example, assign the scope of the enclosing clause in the syntax tree as hedge scope, in the case of verb hedge cues. For sequential classification tasks, there is an additional issue: sometimes the knowledge rule indicates the beginning of the sequence, and its end can be determined using the remaining attributes. For example, suppose the classifier suggests the class . If we could associate the F class suggested by the classifier with the grand parent scope rule, we would not be concerned about the prediction for the last token, because we would knew it would always correspond to the last token of the grand parent clause. To achieve this, we modified the class we want to learn, introducing a new class, say X, instead of F, to indicate that, in those cases, the L token must not be learned, but calculated in the postprocessing step, in terms of other attributes' values (in this example, using the hedge cue grandparent constituent limits). This change also affects the classes of training data instances (in the example, every training instance where the scope coincides with the grand parent scope attribute will have its F-classified token class changed to X). Hedge PPOS GPPOS Lemma PScope GPScope Scope O VP S This O O O O VP S finding O O O O VP S suggest O O O O VP S that O O O O VP S the O F F O VP S BZLF1 O O O O VP S promoter O O O B VP S may F O O O VP S be O O O O VP S regulate O O O O VP S by O O O O VP S the O O O O VP S degree O O O O VP S of O O O O VP S squamous O O O O VP S differentiation L L O O VP S . O O O In the previous example, if the classifier assigns class X to the 'the' token, the postprocessing step will change the class assigned to the 'differentiation' token to L, no matter which class the classifier had predicted, changing also the X class to the original F, yielding a correctly identified scope. After adding the new attributes and changing the relevant class values in the training set, the process starts over again. If performance on the held out corpus improves, these attributes are added to the best configuration so far, and used as the starting point for a new analysis. When no further improvement can be achieved, the process ends, yielding the best We applied the proposed methodology to the tasks of hedge cue detection and scope resolution. We were mainly interested in evaluating whether systematically applying the methodology would indeed improve classifier performance. The following sections show how we tackled each task, and how we managed to incorporate expert knowledge and improve classification. Hedge Cue Identification To identify hedge cues we started with a sequential classifier based on Conditional Random Fields (Lafferty et al., 2001), the state-of-the-art classification method used for sequence supervised learning in many NLP tasks. The baseline configuration we started with included a size-2 window of surface forms to the left and right of the current token, pairs and triples of previous/current surface forms. This led to a highly precise classifier (an F-measure of 95.5 on the held out corpus). After a grid search on different configurations of surface forms, lemmas and POS tags, we found (somewhat surprisingly) that the best precision/recall tradeoff was obtained just using a window of size 2 of unigrams of surface forms, lemmas and tokens with a slightly worse precision than the baseline classifier, but compen- In the analysis step of the methodology we found that most errors came from False Negatives, i.e. words incorrectly not marked as hedges. We also found that those words actually occurred in the training corpus as hedge cues, so we decided to add new rule attributes indicating membership to certain semantic classes. After checking the literature, we added three attributes: • Hyland words membership: this feature was set to Y if the word was part of the list of words identified by Hyland (2005) • Hedge cue candidates: this feature was set to Y if the word appeared as a hedge cue in the training corpus • Words co-occurring with hedge cue candidates: this feature was set to Y if the word cooccured with a hedge cue candidate in the training corpus. This feature is based on the observation that 43% of the hedges in a corpus of scientific articles occur in the same sentence as at least another device (Hyland, 1995). After adding these attributes and tuning the window sizes, performance improved to an F-score of 87.5 in the held-out corpus Scope identification To learn scope boundaries, we started with a similar configuration of a CRF classifier, using a window of size 2 of surface forms, lemmas and POS-tags, and the hedge cue identification attribute (either obtained from the training corpus when using gold standard hedge cues or learned in the previous step), achieving a performance of 63.7 in terms of F-measure. When we incorporated information in the form of a knowledge rule that suggested the scope of the constituent of the parsing tree headed by the parent node of the first word of the hedge cue, and an attribute containing the parent POS-tag, performance rapidly improved about two points measured in terms of Fscore. After several iterations, and analyzing classification errors, we included several knowledge rules, attributes and postprocessing rules that dramatically improved performance on the held-out corpus: • We included attributes for the scope of the next three ancestors of the first word of the hedge cue in the parsing tree, and their respective POS-tags, in a similar way as with the parent. We also included a trigram with the ancestors POS from the word upward in the tree. • For parent and grandparent scopes, we incorporated X and Y classes instead of F, and modified postprocessing to use the last token of the corresponding scope when one of these classes was learned. • We modified the ancestors scopes to reflect some corpus annotation guidelines or other criteria induced after data examination. For example, we decided not to include adverbial phrases or prepositional phrases at the beginning of scopes, when they corresponded to a clause, as in (6) In addition,{unwanted and potentially hazardous specificities may be elicited. . . } • We added postprocessing rules to cope with cases where (probably due to insufficient training data), the classifier missclasified certain instances. For example, we forced classification to use the next enclosing clause (instead of verb phrase), when the hedge cue was a verb conjugated in passive voice, as in (7) {GATA3 , a member of the GATA family that is abundantly expressed in the T-lymphocyte lineage , is thought to participate in ...}. • We excluded references at the end of sentences from all the calculated scopes. • We forced classification to the next S,VP or NP ancestor constituent in the syntax tree (depending on the hedge cue POS), when full scopes could not be determined by the statistical classifier (missing either L or F, or learning more than one of them in the same sentence). Table 4 summarizes the results of scope identification in the held out corpus. The first results were obtained using gold-standard hedge cues, while the second ones used the hedge cues learned in the previous step (for hedge cue identification, we used the best configuration we found). In the gold-standard results, Precision, Recall and the F-measure are the same because every False Positive (incorrectly marked scope) implied a False Negative (the missed right scope). Evaluation To determine classifier performance, we evaluated the classifiers found after improvement on the evaluation corpus. We also evaluated the less efficient classifiers to see whether applying the iterative improvement had overfitted the classifier to the corpus. To evaluate scope detection, we used the best configuration found in the evaluation corpus for hedge cue identification. Tables 5 and 6 show the results for the hedge cue recognition and scope resolution, respectively. In both tasks, classifier performance improved in a similar way to the results obtained on the held out corpus. Finally, to compare our results with state-of-theart methods (even though that was not the main objective of the study), we used the corpus of de CoNLL 2010 Shared Task to train and evaluate our classifiers, using the best configurations found in the evaluation corpus, and obtained competitive results in both subtasks of Task 2. Our classifier for hedge cue detection achieved an F-measure of 79.9, better than the third position in the Shared Task for hedge identification. Scope detection results (using learned hedge cues) achieved an F-measure of 54.7, performing better than the fifth result in the corresponding task, and five points below the best results obtained so far in the corpus (Velldal et Conclusions and Future Research In this paper we have presented an iterative methodology to improve classifier performance by incorporating linguistic knowledge, and proposed a way to incorporate domain rules to the learning process. We applied the methodology to the task of hedge cue recognition and scope finding, improving performance by incorporating information of training corpus occurrences and co-occurrences for the first task, and syntax constituents information for the second. In both tasks, results were competitive with the best results obtained so far on a publicly available corpus. This methodology could be easily used for other sequential (or even traditional) classification tasks. Two directions are planned for future research: first, to improve the classifier results by incorporating more knowledge rules such as those described by Velldal et al. (2012) or semantic resources, specially for the scope detection task. Second, to improve the methodology, for example by adding some way to select the most common errors in the held out corpus and write rules based on their examination. Figure 1 : 1Methodology overview classifier as a result. 3) This/O finding/O suggests/F that/O the/OBZLF1/O promoter/O may/O be/O regulated/O by/O the/O degree/O of/O squamous/O differentiation/L./O, 3 (4) This/O finding/O suggests/O that/O the/F BZLF1/O promoter/O may/O be/O regulated/O by/O the/O degree/O of/O squamous/O differentiation/L./O, 8 Table 2 : 2Evaluation instance where the scope ending could not be identified scope in the learning instance shown in table 2 (us- ing as attributes the scopes of the parent and grand- parent constituents for the hedge cue in the syntax tree) Table 3 : 3Classification performance on the held out cor- pus for hedge cue detection. Conf1 corresponds to win- dows of Word, Lemma and POS attributes and Conf2 in- corporates hedge cue candidates and cooccuring words sated by an improvement of about six points in re- call, achieving an F-score of 86.9. Table 4 : 4Classification performance on the held out corpus. The baseline used a window of Word, Lemma, POS attributes and hedge cue tag; Conf1 included parent scopes, Conf2 added grandparents information; Conf3 added postprocessing rules. Finally, Conf4 used adjusted scopes and incorporated new postprocessing rules Table 5 : 5Classification performance on the evaluation corpus for hedge cue detectionConfiguration Gold-P P R F1 Baseline 74.0 71.9 68.1 70.0 Conf1 76.5 74.4 70.2 72.3 Conf2 80.0 77.2 72.9 75.0 Conf3 83.1 80.0 75.2 77.3 Conf4 84.7 80.1 75.8 77.9 Table 6 : 6Classification performance on the evaluation corpus for scope detection al.,Hedge cue iden- tification Scope detection Best results 81.7/81.0/81.3 59.6/55.2/57.3 Our results 83.2/76.8/79.9 56.7/52.8/54.7 Table 7 : 7Classification performance compared with best results in CoNLL Shared Task. Figures represent Precision/Recall/F1-measure Richárd Farkas, Veronika Vincze, György Móra, János Csirik, and György Szarvas. 2010a. 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Learning the Taxonomy of Function Words for Parsing

Completely data-driven grammar training is prone to over-fitting. Human-defined word class knowledge is useful to address this issue. However, the manual word class taxonomy may be unreliable and irrational for statistical natural language processing, aside from its insufficient linguistic phenomena coverage and domain adaptivity. In this paper, a formalized representation of function word subcategorization is developed for parsing in an automatic manner. The function word classification representing intrinsic features of syntactic usages is used to supervise the grammar induction, and the structure of the taxonomy is learned simultaneously. The grammar learning process is no longer a unilaterally supervised training by hierarchical knowledge, but an interactive process between the knowledge structure learning and the grammar training. The established taxonomy implies the stochastic significance of the diversified syntactic features. The experiments on both Penn Chinese Treebank and Tsinghua Treebank show that the proposed method improves parsing performance by 1.6% and 7.6% respectively over the baseline. This work is licenced under a Creative Commons Attribution 4.0 International License. Page numbers and proceedings footer are added by the organizers. License details: http://creativecommons.org/licenses/by/4.0/

Learning the Taxonomy of Function Words for Parsing August 23-29 2014 Dongchen Li Key Laboratory of Machine Perception and Intelligence Speech and Hearing Research Center Peking University BeijingChina Dingsheng LuoXiantao Zhang zhangxt@cis.pku.edu.cn Key Laboratory of Machine Perception and Intelligence Speech and Hearing Research Center Peking University BeijingChina Xihong Wu Key Laboratory of Machine Perception and Intelligence Speech and Hearing Research Center Peking University BeijingChina Learning the Taxonomy of Function Words for Parsing Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers COLING 2014, the 25th International Conference on Computational Linguistics: Technical PapersDublin, IrelandAugust 23-29 2014 Completely data-driven grammar training is prone to over-fitting. Human-defined word class knowledge is useful to address this issue. However, the manual word class taxonomy may be unreliable and irrational for statistical natural language processing, aside from its insufficient linguistic phenomena coverage and domain adaptivity. In this paper, a formalized representation of function word subcategorization is developed for parsing in an automatic manner. The function word classification representing intrinsic features of syntactic usages is used to supervise the grammar induction, and the structure of the taxonomy is learned simultaneously. The grammar learning process is no longer a unilaterally supervised training by hierarchical knowledge, but an interactive process between the knowledge structure learning and the grammar training. The established taxonomy implies the stochastic significance of the diversified syntactic features. The experiments on both Penn Chinese Treebank and Tsinghua Treebank show that the proposed method improves parsing performance by 1.6% and 7.6% respectively over the baseline. This work is licenced under a Creative Commons Attribution 4.0 International License. Page numbers and proceedings footer are added by the organizers. License details: http://creativecommons.org/licenses/by/4.0/ Introduction Probabilistic context-free grammar (PCFG) is widely used in the fields of speech recognition, machine translation, information retrieval, etc. It takes the empirical rules and probabilities from a Treebank. However, due to the context-free assumption, PCFG does not always perform well (Klein and Manning, 2003). For instance, it assumes adverbs, including temporal adverbs, degree adverbs and negation adverbs, to share the same distribution, whereas the distinction would provide useful indication for disambiguating the syntactic structure of the context. It arose that the manual word classification in linguistic research was used to enrich PCFG and improve the performance. However, from the point of view of statistical natural language processing, there are some drawbacks for manual classification. Firstly, Linguistic phenomena covered by the manual refinement may be limited by the linguistic observations of human. Secondly, the evidence of manual refinement is often based on a particular corpus or specific sources of knowledge acquisition. As a result, its adaptivity to different domains or genres may be insufficient. As for function words, due to the ambiguity and complexity in syntactic grammar, it is more difficult to develop formalized representation than for content words. There are diversified standards for grammar refinement. Consequently, the word classification or category refinement can be conducted in distinct manners, while each of them is reasonable in some sense. A delicate hierarchical classification inevitably involves in multiple dividing standards. However, the word sets under distinct dividing standards may be overlapping. The problems come up that how to choose the set of the multiple standards to cooperate to build the taxonomy, and how to decide the priority of each standard. Regarding that the manual method is hard to overcome critical issues, manual taxonomy for function words may not be reliable for statistical natural language processing. This article attempts to address these issues in a data-driven manner. we first manually construct a cursory and flat classification of function words. A hierarchical split-merge approach is employed to introduce our classification, and the PCFG training procedure is supervised to alleviate the over-fitting issue. The priorities of the subcategorization standards are determined by the measurement of effectiveness for parsing in a greedy manner in the hierarchical classification. And the hierarchical structure of the classification is learned by data-driven approach in the course of grammar induction, so as to fit the practical usages in the Treebank. Accordingly, the grammar learning process is no longer a unilaterally supervised training by hierarchical knowledge, but an interactive process between the knowledge representation induction and the grammar training. That is, the grammar induction is supervised by the knowledge and the structure of the taxonomy is learned simultaneously. These two processes are iterated for several rounds and the hierarchical structure of the function word taxonomy is constructed. In each round, the induced grammar could benefit from the optimized taxonomy during the learning process. The category split in the early rounds take more priorities than in the late ones. Thus, the learned taxonomy implies the stochastic significance of the series of the syntactic features. Experiments on Penn Chinese Treebank Fifth Edition (CTB5.0) (Xue et al., 2002) and Tsinghua Chinese Treebank (TCT) (Zhou, 2004) are performed. The results show that the induced grammars with refined conjunction categories gain parsing performance improvement by 1.6% on CTB and by 7.6% on TCT. During the training process, a taxonomy of function words is learned, which reflects their practical usages in the corpus. The rest of this paper is organized as follows. We first review related work on category refinement for parsing. Then we describe our manually defined categories of function words in Section 3. The hierarchical state-split approach for introducing the the categories are presented in Section 4, and our taxonomy learning method is described in Section 5. In Section 7, experimental comparison is conducted among various methods on granularity choosing. And conclusions of this research are drawn in last section. Related Work A variety of techniques have been proposed to enrich PCFG in either manual (Klein and Manning, 2003;Zhang and Clark, 2011) or automatic (Petrov, 2009;Cohen et al., 2012) manner. Automatic Refinement of Function Words for Parsing One way of grammar refinement is data-driven state-split methods (Matsuzaki et al., 2005;Prescher, 2005). The part-of-speech and syntactic tags in the grammar are automatically split to encode the kinds of linguistic distinctions exhibited in the Treebank. The hierarchical state-split approach (Petrov et al., 2006) started from a bare-bones Treebank derived grammar, and iteratively refined it in a split-mergesmooth cycle with the EM-based parameter re-estimation. It achieved state of the art accuracies for many languages including English, Chinese and German. One tag is usually heterogeneous, in the sense that its word set can be of multiple different types. Nevertheless, the automatic process tries to split the tags through a greedy data-driven manner, where multiple distinctive information is used simultaneously when dividing tags. Thus the refined tags are not intuitively interpretable. Meanwhile, considering that the EM algorithm usually gets stuck at a suboptimal configuration, this data-driven method suffers from the risk of over-fitting. As shown in their experiments, there is little to be gained from splitting the closed part-of-speech classes (e.g. DT, CC, IN) or the nonterminal ADJP. To alleviate the risk of over-fitting, we employ the human-defined knowledge to constrain the splitting process in this research. Based on the state-split model, our approach aims to reach a compromise between manual and automatic refinement approaches. Manual Refinement of Function Words for Parsing The other way to refine the annotation for training a parser is incorporating knowledge base. Semantic knowledge of content words has been proved to be effective in alleviate the data sparsity. Some researches utilized semantic knowledge in WordNet (Miller, 1995;Fellbaum, 1999) for English parsing (Fujita et al., 2010;Agirre et al., 2008), and Xiong et al. (2005;Lin et al. (2009) improved Chinese pars-ing by incorporating semantic knowledge in HowNet (Dong and Dong, 2003;Dong and Dong, 2006). While WordNet and Hownet contain word classification for content words, Li et al. (2014b;Li et al. (2014a) have focused on exploiting manual classification for conjunction in parsing. Klein and Manning (2003) examined the annotation in Penn English Treebank, manually split the majority of the part-of-speech (POS) tags. For the function words, they split the tag "IN" into subordinating conjunctions, complementizers and prepositions, and appendedBE to all forms of "be" andĤAVE to all forms of "have". Conjunction tags are also marked to indicate whether they were "But", "but" or "&". The experimental results showed that the split tags of function words surprisingly make much contribution to the overall improved parsing accuracy. Levy and Manning (2003) transferred this work to Penn Chinese Treebank. They found that, in some cases, certain adverbs such as "however (, )" and "especially (cÙ´)" preferred IP modification and could help disambiguate IP coordination from VP coordination. To capture this point, they marked those adverbs possessing an IP grandparent. However, these manual refinement methods seems to split the tags in a rough way, which might account for a modest accuracy achieved. Some existing work used heuristic rules to simply split the tags of function words (Klein and Manning, 2003;Levy and Manning, 2003). They demonstrated that many function words stood out to be helpful in predicting the syntactic structure and syntactic label. Manual Tabular Subcategories of Function Words When subcategorizing function words, in this section, we manually list various grammatical distinctions that are commonly made in traditional and generative grammar in a fairly flat taxonomy. The grammar training procedure learns by using our manual taxonomy as a starting point, and constructs a reasonable and subtle hierarchical strucutre based on the distribution of function words usages in the corpus. Based on some existing knowledge base (Xia, 2000;Xue et al., 2000;Zhu et al., 1995;Wang and Yu, 2003) and previous research work (Li et al., 2014b), we investigate and summarize the usage of function words, and come up with a hierarchical subcategories. The taxonomy of the function words is represented in a tree structure, where each subcategory of a function word corresponds to a node in the taxonomy, the nonterminals are subcategories and the terminals are words. For the convenience and consistence, our manual classification just gives a rough and broad taxonomy. It is labor-intensive and error-prone of classifying the function words manually to produce a consistent output. Fine-grained hierarchical structure is not obligatory, but would be harmful if inappropriately classified, as it may mislead the learning process. To avoid this kind of risk, the elaboration is saved, rather than introducing unnecessary bias. The learning process would perform the hierarchical classification according to the distribution in the corpus. For instance, the distinction within conjunctions is intricate. Conjunctions are the words that are called "connective words" in traditional Chinese grammar books. In Penn Chinese Treebank, they are tagged as coordinating conjunctions (CC), subordinating conjunctions (CS), or adverbs (AD) according to their syntactic distribution. CC conjoins two equivalent constituents (noun phrases, clauses, etc.), each of which has approximately the same function as the whole construction. CS precedes a subordinating clause, whereas conjunctive adverbs often appear in the main clause and pair with a subordinating conjunction (e.g., if (XJ)/CS ... then (Ò)/AD). However, in Chinese, it is often hard to tell the subordinating clause from the main clause in the compound statement. As a result, in the prospective of linguistic computing, the confusion is that, CS and conjunctive adverbs both precedes the subordinating clauses or main clauses, while CC connects two phrases or precedes the main clause. In our scheme, we simply conflates the CC, CS and conjunctive adverbs together. This result in a general "conjunction" category, within which we just enumerate all the possible uses of the conjunctions. As a result, the structure of our human-defined taxonomy is fairly flat, as briefly shown in Figure 1 and Figure 2. Our scheme releases our hands from the confusing situations, by leaving them to our data-driven method described in the following section. Many prepositions in Chinese are evolved from verbs, thus the linguistic characteristics of prepositions are somewhat similar to verbs. Therefore, this paper divides the preposition word set according to Adverb the types of their associated arguments: "benefactive", such as " •(for)" and "‰(to)", marks the beneficiary of an action; "locative", such as "3(in)", marks adverbials that indicate the place of the event; "direction", such as " •(towards)" and " d(from)", marks adverbials that answer the questions "from where?" and "to where?"; "temporal", such as "3(on)", marks temporal or aspectual adverbials that answer the question "when?", and so on. Subordinating Conjunction                                                                                                        Conjunctive Adverb                                          Transition        Refining Grammar with Hierarchical Category Refinement In this section, we choose the appropriate granularity in a data-driven manner based on the split-merge learning method in Section 2.1. Our approach first initializes the categories with the most general subcategories in the taxonomy and then splits the categories through the hypernym-hyponym relation in the taxonomy. Data-driven method is used to merge the overly refined subcategories. The top category in the taxonomy is used as the starting annotations of POS tags. As we cannot predict which layer should be the most adequate one, we try to avoid applying any priori restriction on the refinement granularity, and start with the most general tags. With the hierarchical knowledge, it turns out to be a critical issue that which granularity should be used to refine the tags for parsing. We intend to take neither too coarse subcategories nor too fine ones in the hierarchical knowledge for parsing. Instead, it would be our advantage to split the tags with the very granularity where needed, rather than splitting them all to one specific granularity in the taxonomy. For example, "Conjunctive Adverbs" are divided into three subcategories in our taxonomy as shown in Figure 2. The evidence for the refinement may occur in very rare case, and certainly some of the context of the different subcategories are quite the same. Splitting symbols with the same context is not only unnecessary, but potentially harmful, since it unreasonably fragments observations of other symbols.behavior. In this paper, the hierarchical subcategory knowledge is used to refine grammars by supervising the automatic hierarchical state-split approach. In the split stage in each cycle, the function word subcategory is split along the hierarchy of the knowledge, instead of being randomly split and classified automatically. In this way, we try to alleviate the over-fitting of the greedy data-driven approach, and a new set of knowledge-related tags are generated. In the following step, we retreat some of the split subcategories to their more general layer according to its likelihood loss of merging them. In this way, we try to avoid the excessive refinement in our hierarchical knowledge without sufficient data support. There are two issues that we have to consider in this process: a) how to deal with the polysemous words, and b) how to deal with the multi-branch situation other than binary branch in the taxonomy. Regarding to the polysemous words, they occur mostly in two situation for function words. Some are the polysemous words which can be taken as conjunctions or auxiliary words, while the others can be taken as preposition or adverbs. Fortunately there is no ambiguity for a word given its POS tag, so we could neglect this situation in the split process when training. We demonstrated the solution for the multiple branches in the Section 5. Learning the Taxonomy of Function Words There are multiple subcategorization criterions for building function word taxonomy, and it is difficulty for human to rank the ordering in the classification process. This section represents the method of learning the taxonomy of the function words in data-driven manner. Based on the manual tabular classification, the similar word classes are conflated to express the data distribution. The multiple branches in the taxonomy are intractable for the original split-merge method, because it splits every category into two and merges half of them for efficiency. If we follow this scheme in our training process, it would be difficult to deal with the multi-branch situation in the taxonomy, because how to choose the first two to split among the multiple branches is another challenge. It is an equally difficult problem for us to binarize the taxonomy by hand comparing to directly choosing the granularity. It would be our advantage to binarize the taxonomy by a data-driven method. For automatic binarization, a straightforward approach is to measure the utility of traversing all the plausible ways of cutting all the branches into two sets individually and use the best one. Then we can deal with the divided two sets in the same manner recursively. However, not only is this impractical, requiring an entire training phase for each possible binarization scheme which is exponentially expensive, but it assumes the contributions of multiple binarizations in different branches are independent. In fact, extra sub-symbols may need to be added to several nonterminals before they can cooperate to pass information along the parse tree. Therefore, we go in the opposite direction, and propose an extended version of split-merge learning to handle the multiple branches in the taxonomy. That is, we split each state into all the subcategories in the lower layer in the taxonomy even if it has multiple branches, train, and then measure for every two sibling subcategories in the same layer the loss in likelihood incurred when merging them. If this loss is small, the new division of these two subcategories does not carry enough useful information and can be merged back. Contrary to the gain in likelihood for splitting, the loss in likelihood for merging can be efficiently approximated (Petrov et al., 2006). More specifically, we assume transitivity in merging multiple subcategories in one layer. Figure 3 gives an illustration. After the split stage, the category A has been split into subcategories A-1, A-2, ... to A-7. Then we compute the loss in likelihood of the training data by merging back each pair of two subcategories through A-1 to A-7. If the loss is lower than a certain threshold 1 set for each round of merge, this pair of newly split subcategories will be merged. We only show the sibling ones for brevity in this example. Assume the losses of merging these pairs (A-1, A-2), (A-2, A-3), (A-3, A-4) and (A-4, A-5) are below the threshold ε. Thus, A-1, A-2, A-3, A-4 and A-5 are merged to X-1 due to the transitivity of the connected points, where X-1 is the automatically generated subcategory which contains the five conflated subcategories as its descendants. At the meantime, A-6 and A-7 still remain. This scheme is an approximation because it merges subcategories that should be merged with the same subcategory. But it will leave the split of this instances to the next round when more evidence on interaction with other more refined subcategories is given. (a) Refined subcategories before the merge stage (b) Refined subcategories after the merge stage Figure 3: Illustration of merging the subcategories for multiple branches in the taxonomy. Where ε is a certain threshold below which this pair of subcategories will be merged, and X is the automatically generated subcategory which contains the conflated subcategories as its descendants. After merging in each round, the hierarchical knowledge is reshaped to fit the practical usage in the Treebank. The split-merge cycles allow us to progressively increase the complexity of the hierarchical knowledge, and the more useful distinctions are represented as the higher level in the taxonomy, which gives priority to the most useful distinctions in return by supervising the grammar induction. Figure 4 demonstrates the transformation of the hierarchical structure from the tabular classification. Along this road, the training scheme is not a unilateral training, but an interactive process between the knowledge representation learning and the grammar training. Our learning process exerts a mutual effect to both the induced grammar and the optimized structure of the hierarchical knowledge. In this way, the set of dividing standards are chosen iteratively according to their syntactic features. The more effective divisions are conducted in the early stages. In the following stages, the divisions which interact with previous divisions to give the most effective disambiguating information are adopted. The final taxonomy are built based on manual classification in data-driven approach, and the hierarchical structure are optimized and rational in the perspective of actual data distribution. Figure 4 illustrates a concrete instance of the procedure of learning the taxonomy. On one hand, this procedure provides a more rational hierarchical subcategorization structure according to data distribution. On the other hand, the order of the division criterions represents the priorities the grammar induction takes for each criterion. The structure in the higher levels of the taxonomy are determined by the dominant syntactic characteristics. And the division in the later iterations are on the basis of minor distinctive characteristics. Experiments and Results Data Set We present experimental results on both CTB5.0 (All traces and functional tags were stripped.) and TCT. We ran experiments on CTB5.0 using the standard data allocation: files from CHTB 001.fid to CHTB 270.fid, and files from CHTB 400.fid to CHTB 1151.fid were used as training set. The development set includes files from CHTB 301.fid to CHTB 325.fid, and the test set includes files CHTB 271.fid (Zhou, 2012). We have parsed on the segmented text in the Treebank, namely, no use of gold POS-tags, use of gold segmentations, and full-length sentences. This is the same as for other 5 parsers in Table 1 for comparison. All the experiments were carried out after six cycles of split-merge. A A-7 A-6 A-5 A-4 A-3 A-2 A-1 (a) First round of category split A A-7 A-6 X-1 X-1 A-5 A-4 A-3 A-2 A-1 (b) First round of category merge A A-7 A-6 X-1 A-5 A-4 A-3 A-2 A-1 (c) Second round of category split A A-7 A-6 X-1 X-3 X-2 X-2 A-2 A-1 X-3 A-5 A-4 A-3 (d) Second round of category merge A A-7 A-6 X-1 X-3 A-5 A-4 A-3 X-2 A-2 A-1 (e) Third round of category split A A-7 A-6 X-1 X-3 X-4 A-3 X-2 A-2 A-1 X-4 A-5 A-4 (f) Final Results The final results are shown in Table 1. Our final parsing performance is higher than both the manual annotation method (Levy and Manning, 2003) and the data-driven method (Petrov, 2009 Given the manual labor required for generating the taxonomy (and in languages where there is a taxonomy, determining whether it is suitable), this first study focuses on a language where there is quite a bit of under-and over-specification in the Treebanks' tag sets. So this work is only implemented on Chinese. We regard it as future work to transfer this approach to other languages. Analysis The outline of constructing the taxonomy of function words are as follows. Firstly, the function words are manually subcategorized in a rough and cursory way. When dealing with subcategories hard to resolve their relation of subordination, we simply treat them as siblings in the tree in a rather flat stricture, and leave the elaboration of exquisite clustering to the algorithms. The data-driven approach in Section 4 automatically choose the appropriate granularity of refinement for our grammar. Moreover, the split-merge learning for multiple branches in the hierarchical subcategories in Section 5 exploits the relationship between the sibling nodes in the same layer, making use of the Treebank data to adjust and optimize the hierarchy. During the split-merge process, the hierarchical subcategories are learned to fit the data, which is a transformation of our manually defined hierarchy. The transformed hierarchy is just the route map of subcategories employed in our model. As abbreviated in Figure 5 and Figure 6, many distinctions between word sets of the subcategories have been exploited by our approach, and the learned taxonomy is interpretable. For instance, It shows that the learned structure of the taxonomy is reasonable. Comparison with Previous Work Although the taxonomy of function words are learned in the grammar training process, the grammar is trained on the Treebank in supervised manner. Thus, this work is not directly relevant with unsupervised grammar induction literature (Headden III et al., 2009;Berant et al., 2007;Mareček andŽabokrtskỳ, 2014). Where "X" represents the automatically generated subcategory. Li et al. (2014b) presented ideas of using either hierarchical semantic knowledge from HowNet for content words or grammar knowledge for subordinating conjunctions. They introduced hierarchical subcategory knowledge in a different stage. They split the original Treebank categories in split-merge process according to the data, and then find a method to map the subcategories to the node in the taxonomy, and constrain their further splitting. Comparing to their work, our approach is more delicate, which is splitting the categories according to the knowledge, and learning the knowledge structure according to data during the training course. Lin et al. (2009) incorporated semantic knowledge of content words into the data-driven method. It would be promising if this work stacks with the content word knowledge. However, the work with content word knowledge have to handle the polysemous words in the semantic taxonomy, so they split the categories according to the data, and then find a way to map the subcategories to the node in the taxonomy, and constrain their further splitting. It is our goal to make these two methods compatible with each other.                                                      Conjunctive Adverb                                              Transition         Incorporating word formation knowledge achieved higher parsing accuracy according to Zhang and Clark (2011). However, they ran their experiment on gold POS-tags and a different data set split, which is different form the setup of work in Table 1 including this work. They also presented their result on automatically assigned POS-tags and the same data set split as in the work in Table 1 to facilitate the performance comparison. It gave F 1 score of 81.45% for sentences with less than 40 words and 78.3% for all sentences, significantly lower than Petrov and Klein (2007). Zhang et al. (2013) exhaustively exploited character-level syntactic structures for words, and achieved 84.43% on F 1 measure. They placed more emphasis on the word-formation of content words, which our model highlights the value of the function words. The complementary intuitions make it possible to integrate these approaches together in the future work. Conclusion This paper presents an approach for inducing finer syntactic categories while learning the taxonomy for function words. It used linguistic insight to guide the state-split process, and the hierarchical structure representing syntactic features of function word usages was established during the grammar training process. Empirical evidence has been provided that automatically subcategorizing function words contributes to high parsing performance. The induced grammar supervised by the taxonomy outperformed pervious approaches, which benefited from both the knowledge and the data-driven method. The proposed approach for learning the structure of the taxonomy could be generalized to construct semantic knowledge base. Figure 1 and 1Figure 2 abbreviate the manual classification and their corresponding examples. Figure 1 : 1Abbreviated Hierarchical subcategories of subordinating conjunctions with examples. Figure 2 : 2Abbreviated Hierarchical subcategories of adverbs with examples. Figure 4 : 4Iteration of grammar induction and taxonomy structure learning to CHTB 300.fid. Experiments on TCT use the data set as in CIPS-SIGHAN-ParsEval-2012 Figure 5 : 5Abbreviated automatically learned hierarchical subcategories of subordinating conjunctions with examples. Figure 6 : 6Abbreviated automatically learned hierarchical subcategories of adverbs with examples. Lin et al. (2009) and Coordination: both Q Progression: not only Ø= Transition: although •, Preference: rather than †Ù Cause: because du Condition                      Assumption: if XJ Universalization: whatever ØØ Unnecessary Condition: since Q, Insufficient Condition: although =¦ Sufficient Condition: as long as • ‡ Necessary Condition: only if •k Equality: unless Øš ... ... Preference: would rather ØX In case: lest ±• Otherwise: or else ÄK However: but % Therefore: so ¤± Then, As a result: as soon Ò So that: so that ±B Furthermore: but also … In addition: moreover , Later: subsequently ' As well: likewise • ... Frequency Adverbs: for many times õg Degree Adverbs: very 4• ... ...Result    Progression        Adjunct Adverb    ). compares our results with the system of Beijing Information Science and Technology University (BISTU) which got the second place in the competition.Parser Precision Recall F 1 Levy(2003) 78.40 79.20 78.80 Petrov(2009) 84.82 81.93 83.33 Lin(2009) 86.00 83.10 84.50 Qian(2012) 84.57 83.68 84.13 Zhang(2013) 84.42 84.43 84.43 This paper 86.55 83.41 84.95 Table 1: Our final parsing performance compared with the best previous work on CTB5.0. On test set TCT, the method achieves the best precision, recall and F-measure in the CIPS-SIGHAN- ParsEval-2012 competition, and table 2 Parser Precision Recall F 1 BISTU 70.10 68.08 69.08 This paper 76.81 76.66 76.74 Table 2 : 2Our final parsing performance compared with the best previous works on TCT. Coordination: both Q X Progression: not only Ø= Transition: although •,X Preference: rather than †Ù Cause: because du Condition                        Universalization: ØØ Equality: Øš X                X      X Assumption: XJ Sufficient Condition: • ‡ Necessary Condition: •k X Unnecessary Condition: Q, Insufficient Condition: =¦ ...  Preference: would rather ØXXIn case: lest ±• Otherwise: or else ÄK However: but % X Therefore: so ¤± So that: so that ±B Then, As a result: as soon Ò Later: subsequently 'As well: likewise • ...Result      Progression          X      X Furthermore: but also … In addition: moreover , In practice, instead of setting a predefined threshold for merging, we merge a specific number of the newly split subcategories. Acknowledgments Improving parsing and pp attachment performance with sense information. Eneko Agirre, Timothy Baldwin, David Martinez, Proceedings of ACL-08: HLT. 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Input Specification in the WAG Sentence Generation System

This paper describes the input specification language of the WAG Sentence Generation system. The input is described in terms ofHalliday's (1978)three meaning components, ideational meaning (the propositional content to be expressed), interactional meaning (what the speaker intends the listener to do in making the utterance), and textual meaning (how the content is structured as a message, in terms of theme, reference, etc.).

Input Specification in the WAG Sentence Generation System Michael O&apos;donnell emaihmicko@aisb.ed.ac.uk Department of AI University of Edinburgh 80 South BridgeEH1 1HNEdinburghUK Input Specification in the WAG Sentence Generation System This paper describes the input specification language of the WAG Sentence Generation system. The input is described in terms ofHalliday's (1978)three meaning components, ideational meaning (the propositional content to be expressed), interactional meaning (what the speaker intends the listener to do in making the utterance), and textual meaning (how the content is structured as a message, in terms of theme, reference, etc.). Introduction This paper describes the input specification language of the WAG Sentence Generation system. The input is described in terms of Halliday's (1978) three meaning components, ideational meaning (the propositional content to be expressed), interactional meaning (what the speaker intends the listener to do in making the utterance), and textual meaning (how the ideational content is structured as a message, in terms of theme, reference, etc.). One motivation for this paper is the lack of descriptions of input-specifications for sentence generators. I have been asked at various times to fill this gap. Perhaps a better motivation is the need to argue for a more abstract level of input. Many of the available sentence generators require specification of syntactic information within the input specification. This means that any text-planner which uses this system as its realisation module needs to concern itself with these fiddling details. One of the aims in the WAG system has been to lift the abstractness of sentence specification to a semantic level. This paper discusses this representation. The WAG Sentence Generation System is one component of the Workbench for Analysis and Generation (WAG), a system which offers various tools for developing Systemic resources (grammars, semantics, lexicons, etc.), maintaining these resources (lexical acquisition tools, network graphers, hypertext browsers, etc.), and processing (sentence analysis -O' Donnell 1993Donnell , 1994; sentence generation O'Donnell 1995b; knowledge representation -O'Donnell 1994; corpus tagging and exploration-O'Donnell 1995a). The Sentence Generation component of this system generates single sentences from a semantic input. This semantic input could be supplied by a human user. Alternatively, the semantic input can be generated as the output of a multi-sentential text generation system, allowing such a system to use the WAG system as its realisation component. The sentence generator can thus be treated a blackbox unit. Taking this approach, the designer of the multi-sentential generation system can focus on multi-sentential concerns without worrying about sentential issues. WAG improves on earlier sentence generators in various ways. Firstly, it provides a more abstract level of input than many other systems (Mumble: McDonald 1980;Meteer et al. 1987;FUF: Elhadad 1991), as will be demonstrated throughout this paper. The abstractness improves even over the nearest comparable system, Penman (Mann 1983;Mann 8z Matthiessen 1985), in its treatment of textual information (see below). Other sentence generators, while working from abstract semantic specifications, do not represent a generalised realiser, but are somewhat domain specific in implementation, e.g., Proteus (Davey 1974(Davey /1978; Slang (Patten 1988). Other systems do not allow generation independent from user interaction, for instance, Genesys (Faw-cett & Tucker 1990) requires the user to make decisions throughout the generation process. Against WAG, it does not yet have the grammatical and semantic coverage of Penman, FUF or Mumble, although its coverage is reasonable, and growing quickly. Semantic Metafunctions The input to the WAG Sentence generation system is a specification of an utterance on the semantic stratum. We thus need to explore further the nature of Systemic semantic representation. Halliday (1978) divides semantic resources into three areas, called metafunctions: 1. Interactional Metafunction: viewing language as interaction, i.e., an activity involving speakers and listeners, speechacts, etc. Interactional meaning includes the attitudes, social roles, illocutionary goals, etc of interactants. 2. Ideational Metafunction: concerned with the propositional content of the text, structured in terms of processes (mental, verbal, material, etc.), the participants in the process (Actor, Actee, etc.), and the circumstances surrounding the process (Location, Manner, Cause, etc.). 3. Textual Metafunction: how the text is constructed as a message conveying information. This concerns, for instance, the thematic structuring of the ideation presented in the text, its presentation as recoverable or not, the semantic relevance of information, etc. Although these metafunctions apply to both the semantics of sentence-size and multisentential texts, this paper will focus on sentential semantics, since we are dealing with the input to a sentence generator. Below we explore the nature of this semantic specification in more detail. Interactional Specification Interactional representation views the text as part of the interaction between participants. Sentences themselves serve an important part in interaction, they form the basic units -the movesof which interactions are composed. Moves are also called speech-acts. Note that WAG serves in monologic as well as dialogic interactions. The input to the WAG generator is basically a speech-act specification, although this specification includes ideational and textual specification. Figure 1 shows a sample speech-act specification, from which the generator would produce: I'd like information on some body repairers. The distinct contributions of the three meta-functions are separated by the grey boxes. Say is the name of the lisp function which evaluates the speech-act specification, calling the generator, dialog-5 is the name of this particular speech-act (each speech-act is given a unique identifier, its unit-id). In specifying the speech-act, there are several important things which need to be specified: • Speech-Function: what does the speaker requires the hearer to do in regard to the encoded proposition? 1 This is called in Systemics the speech-function. Is the hearer supposed to accept the content as a fact? Or are they supposed to complete the proposition in some way? Or perform some action in response to the utterance? Participants: who is uttering the speechact (the Speaker), and who is it addressed to (the Hearer). Content: what proposition is being negotiated between the speaker and hearer? The participant roles do not need to be included in every sentence-specification, but may be in some, for the following reasons: • Pronominalisation: If the filler of the Speaker or Hearer role happens to play some role in the ideational specification, then an appropriate pronoun will be used in the generated string (e.g., T, 'you'). • Voice Selection: If the spoken output mode is used, WAG will select a voice of the same gender as the speaker entity. 1For ease of writing, I use the terms 'speaker' and 'hearer' to apply to the participants in both spoken and written language. Paris 1993): lexico-grammatical choices can be constrained by reference to attributes specified in the Speaker and Hearer roles. 2 This has not, however, been done at present: while the implementation is set up to handle this tailoring, the resources have not yet been appropriately constrained. WAG's semantic input improves over that of Penman in regards to the relationship between the speech-act and the proposition. In Penman, the ideational specification is central: a semantic specification is basically an ideational specification, with the speech-act added as an additional (and optional) field. This approach is taken because Penman was designed with monologic text in mind, so the need for varied speech-acts is not well integrated. 3 2Since the fillers of the Speaker and Hearer roles are ideational units, they can be extensively specified for user-modelling purposes, including the place of origin, social class, social roles, etc of the participant. Relations between the participants can also be specified, for instance, parent/child, or doctor/patient relations. Lexico-grammatical decisions can be made by reference to this information: tailoring the language to the speaker's and hearer's descriptions. a~VAG also allows the representation of non-verbal moves (e.g., the representation of system or user physical actions), which allows WAG to model interaction in a wider sense. WAG however takes the speech-act as central, the semantic specification is a specification of a speech-act. The ideational specification is provided as a role of the speech-act (the :proposition role). WAG thus integrates with more ease into a system intended for dialogic interaction, such as a tutoring system. In particular, it simplifies the representation of speech-acts with no ideational content, such as greetings, thank-yous, etc. Figure 2 shows the systems of the speech-act network used in WAG (based on O 'Donnell 1990, drawing upon Berry 1981Martin 1992). The main systems in this network are as follows: T y p e s o f S p e e c h -A c t s I n i t i a t i o n : The grammatical form used to realise a particular utterance depends on whether the speaker/writer is initiating a new exchange, or responding to an existing exchange (e.g., an answer to a question). Responding moves reflect a far higher degree of ellipsis than initiating moves. In particular, a move responding to a whquestion usually only needs to provide the whelement in their reply. An elicit move indicates that the speaker requires some contentfull response, while a propose move may require changes of state of belief in the hearer, support moves indicate the speaker's acceptance of the prior speaker's proposition. Other speech-functions cater to various alternative responses in dialogue, for instance: deny-knowledge -the speaker indicates that they are unable to answer a question due to lack of knowledge; contradict: the speaker indicates that they disagree with the prior speaker's proposition; requestrepeat: the speaker indicates that they did not fully hear the prior speaker's move. In writing a speech-act specification, the :is field is used to specify the the speech-act type (the same key is used to specify ideational types in propositional units). The speech-act of figure 1 is specified to be (:and initiate propose). Feature-specifications can be arbitrarily complex, consisting of either a single feature, or a logical combination of features (using any combination of :and, :or or :not). One does not need to specify features which are systemically implied, e.g., specifying propose is equivalent to specifying (:and move speech-act negotiatory propose). Hovy (1993) points out that as the input specification language gets more powerful, the amount of information required in the specification gets larger, and more complex. WAG thus allows elements of the semantic specification to take a default value if not specified. For instance, the example in figure 1 does not specify a choice between negotiate-information or negotiate-action (the first is the default). Other aspects are also defaulted, for instance, the relation between the speaking time and the time the event takes place (realised as tense). Ideational Specification Once we have specified what the speech-act is doing, and who the participants are, we need to specify the ideational content of the speechact. Ideational Representation When talking about ideational specification, we need to distinguish ideational potentialthe specification of what possible ideational structures we can have; and ideational instanrials -actual ideational structures. The first is sometimes termed terminological knowledge knowledge about terms and their relations, the second, assertional knowledge -knowledge about actual entities and their relations. Ideational Potential: Ideational potential is represented in terms of an ontology of semantic types, a version of Penman's Upper Model (UM) (Bateman 1990;Bateman et al. 1990). 4 The root of this ontology is shown in figure 3. Many of the types in this ontology will have associated role constraints, for instance, a mental-process requires a Sensor role, which must be filled by a conscious entity. The UM thus constrains the possible ideational structures which can be produced. The UM provides a generalised classification system of conceptual entities. For representing concepts which are domain-specific (e.g., bodyrepairer), users provide domain-models, where domain-specific concepts are subsumed to concepts in the UM. Ideational Structure: An ideational specification is a structure of entities (processes, t-hings and qualities), and the relations between these entities. Such a structure is specified by providing two sets of information for each entity (as in the propositional slot of figure 1): • Type Information: a specification of the semantic types of the entity, derived from the UM, or associated domainmodel. 4WAG's Upper Model has been re-represented in terms of system networks, rather than the more loosely defined type-lattice language used in Penman. WAG thus uses the same formalism for representing ideational, inteLctional and lexico-grammatical information. ideational-unit -I "c°nsci°us q-human... Typically, a text-planner has a knowledgebase (KB) to express, and produces a set of sentence-specifications to express it. The form of the sentence-specifications differs depending on the degree of integration between the textplanner and the sentence-realiser. In most systems, the sentence-realiser has no access to the KB of the text-planner. This is desirable so that the sentence-realiser is independent of the text-planner -it can act as an independent module, making no assumptions as to the internal representations of the text-planner. The sentence-realiser can thus be used in connection with many different textplanners. The sole communication between the two systems is through a sentence-specification -the text-planner produces a sentencespecification, which the sentence-realiser takes as input. The text-planner thus needs to re-express the contents of its KB into the ideational notation used by the sentencerealiser. This approach has been followed with systems such as Penman, FUF, and Mumble. Each of these has been the platform supporting various text-planners (often experimental). WAG also has been designed to support this planner-realiser separation, if need be. WAG can thus act as a stand-alone sentence realiser. The sentence specifihation of figure 1 reflects this mode of generation. However, WAG supports a second mode of generation, allowing a higher degree of integration between the text-planner and the sentence realiser. In this approach, both processes have access to the KB. Ideational material thus does not need to be included within the input specification. Rather, the input specification provides only a pointer into the attached KB. Since the information to be expressed is already present in the KB, why does it need to be re-expressed in the semantic specification? Taking this approach, the role of the semantic specification is to describe how the information in the KB is to be expressed, including both interactional and textual shaping. This integration allows economies of generation not possible where content used for textplanning and content used for sentence generation are represented distinctly. One benefit involves economy of code -many of the processes which need to be coded to deal with ideation for a text as a whole can also be used to deal with ideation for single sentences. Another involves the possibility of integrating the two processes -since the sentence realiser has access to the same knowledge as the multi-sentential planner, it can make decisions without requiring explicit informing from the planner. Another economy arises because translation between representations is avoided. In the stand-alone approach, the sentence-planner needs knowledge of how ideational specifications are formulated in the sentence specification language. It needs to map from the language of its KB to the language of the sentence specification. This is not necessary in an integrated approach. To demonstrate this integrated approach to sentence generation, we show below the generation of some sentences in two stagesfirstly, assertion of knowledge into the KB, and secondly, the evaluation of a series of speechacts, which selectively express components of this knowledge. ; Participants (tell John :is male :name "John") (tell Mary :is female :name "Mary") (tell Party :is spatial) Selective Expression of KB Now we are ready to express this knowledge. The following sentence-specification indicates that the speaker is proposing in/ormation, and that the leaving process is to be the semantic head of the expression. It also indicates which of the roles of each entity are relevant for expression (and are thus expressed if possible), -and which entities are identifiable in context (and can thus be referred to by name). The generation process, using this specification, produces the sentence shown after the form. (say example-1 : is propose : proposition leaving :relevant-roles ( (leaving Actor) (causation Head Dependent) (arrival Actor) ) : identifiable-entities (John Mary)) => Mary left because John arrived. As stated above, this approach does not require the sentence-specification to include any ideational-specification, except for a pointer into the KB. The realiser operates directly on the KB, using the information within the sentence-specification to tailor the expression. Alternative sentence-specificati0ns result in different expressions of the same information, for instance, including more or less detail, changing the speech-act, or changing the textual status of various entities. The expression can also be altered by selecting a ~different entity as the head of the utterance. For instance, the following sentence-specification is identical to the previous, except the cause relation is now taken as the head, producing a substantially different sentence: (say example-2 :is propOse :proposition causation :relevant-roles ((causation Head Dependent) (leaving AcSor) (arrival Actor)) :identifiable-entities (John lMary)) => John's arrival caused Mary ~to leave. We will now turn to the textual component of the input specification, which iS responsible for tailoring the expression of the ideational content. Textual Specification Textual semantics concerns the role of the text and its components as a message, While creating a text (whether a single utterance or a whole book), we have a certain amount of content we wish to encode. But there are various ways to encode this information, to present our message. The textual semantics represents the various strategies for structuring the message. Relevant-Roles One of the main steps in the text generation process involves content selection -the selection of information from the speaker's knowledge base for presentation. Such a process must decide what information is relevant at each point of the unfolding discourse. In some systems, content selection is driven through the construction of the rhetorical structure of the text (e.g., Moore & Paris 199). As we build a rhetorical structure tree, the ideation which is necessary for each rhetorical relation is selected. For instance, if we add an evidence relation to an existing RST tree, the ideation which functions as evidence is selected for expression. The rhetorical structure thus organises the ideational content to be expressed, selecting out those parts of the ideation-base which are relevant to the achievement of the discourse goals at each point of the text. I use the term rhetorical relevance to refer to this sort of relevance. 5 Rhetorical relevance is dynamic -it changes as the text progresses. It represents a shifting focus on the ideation base (Halliday ~ Matthiessen, 1995, pp373-380). What is relevant changes as the text unfolds, as the rhetorical structure is realised. Relevance forms what Grosz (1977/86) calls a focus space. 6 Halliday & Matthiessen (1995) extend Grosz's notion of focus space to include other types of textual spaces: thematic spaces, identifiability spaces, new spaces, etc. (p376). Each of these spaces can be though of as a pattern stated over the ideation base. According to Grosz, focus is "that part of the knowledge base relevant at a given point of a dialog." (p353). However, Grosz's notion of relevance is based on the needs of a text understanding system -which objects in the knowledge-base can be used to interpret the utterance. My sense of relevance is derived from relevance in generation -what information has been selected as relevant to the speaker's unfolding discourse goals. She is dealing with a set of objects which may potentially appear in the text at this point, while I am dealing with the set of objects which most probably do appear in the text. To represent the relevance space in a sentence specification, I initially provided a :relevant-entities field, which listed those ideational entities which were relevant for expression. However, problems soon arose with 5See Pattabhiraman & Cercone (1990) for a good computational treatment of relevance, and its relation to salience. 6Various earlier linguists and computational linguists have also used the notion of 'spaces' to represent textual status, see for instance, Reichman (1978); Grimes (1982). this approach. Take for instance a situation where Mark owns both a dog and a house, and the dog destroyed the house. Now, we might wish to express a sentence to the effect that A dog destroyed Mark's house, which ignores Mark's ownership of the dog. In a system where relevance is represented as a list of entities, we could not produce this sentence. What we need is a representation of the relevant relations in the KB. To this end, WAG's input specification allows a field :relevant-roles, which records the roles of each entity which are currently relevant for expression, e.g., as was used in the examples of section 3.2.2. 7 While constructing a sentence, the sentence generator refers to this list at various points, to see if a particular semantic role is relevant, and on the basis of this, chooses one syntactic structure over another. At present, the ordering of roles in the list is not significant, but it could be made so, to constrain grammatical salience, etc. Theme The :theme field of the speech-act specifies the unit-id of the ideational entity which is thematic in the sentence. If a participant in a process, it will typically be made Subject of the sentence. If the Theme plays a circumstantial role in the proposition, it is usually realised as a sentence-initial adjunct. WAG's treatment of Theme needs to be extended to handle the full range of thematic phenomena. Theme specification in WAG is identical to that used in Penman. Information Status The participants in an interaction each possess a certain amount of information, some of which is shared, and some unshared. I use the term information status to refer to the status of information as either shared or unshared. The information status of ideational entities affects the way in which those items can be referred to. Below we discuss two dimensions of information status: TIf the explicit ideational specification is included in the say form (as in figure 1), then the relevance space need not be stated, it is assumed that all the entities included within the specification axe relevant, and no others. . Shared Entities: entities which the speaker believes are known to the hearer can be referred to using identifiable reference, e.g., definite deixis, e.g., the President; and naming, e.g., Ronald Reagan. Entities which are not believed to be shared require some form of indefinite deixis, e.g., a boy called John; Eggs; Some eggs, etc. A speaker uses indefinite deixis to indicate that he believes the entity unknown to the hearer. It is thus a strategy used to introduce unshared entities into the discourse. Once the entity is introduced, some form of definite reference is appropriate. 2. Recoverable Entities: Entities which are part of the immediate discourse context can be referred to using pronominalisation (e.g., she, them, it, this, etc.); substitution (e.g., I saw one;); or ellipsis (the non-mention of an entity, e.g., Going to the shop?). The immediate discourse context includes entities introduced earlier in the discourse; and also entities within the immediate physical context of the discourse, e.g., the discourse participants (speaker, hearer, or speaker+hearer) and those entities which the participants can point at, for instance, a nearby table, or some person. Two fields in the semantic specification allow the user to specify the information status of ideational entities -and thus how they can be referred to in discourse s (these lists will typically be maintained by the text-planner as part of its model of discourse context): • The Shared-Entities Field: a list of the ideational entities which the speaker wishes to indicate as known by the hearer, e.g., by using definite reference. • The Recoverable-entities Field: a list of t the ideational entities which are recoverable from context, whether from the prior text, or from the immediate interactional context. SInformation status only partially constrains the choice of referential form -the choice between the remaining possibilities can be made by the sentence planner, by specifying directly grammatical preferences. / Conclusions The input specification for the WAG sentence generator is a' speech-act, which includes an indication of which relations in the KB are relevant for expression at this point. Other information in the input specification helps tailor the expression of the content, such as an indicator of which KB element to use as the head of the generated form, which is theme, which elements are recoverable and identifiable. In taking this approach, WAG attempts to extend the degree to which surface forms can be constrained by semantic specification. In many sentence generation systems, direct specifications of grammatical choices or forms is often needed, or, in the case Of Penman, the user needs to include arcane inquiry preselections -interventions in the interstratal mapping component, perhaps more arcane than grammar-level intervention. By providing a more abstract form of representation, text-planners using WAG need less knowledge of grammatical forms, and can spend more of their efforts dealing with issues of text-planning. I say 'less' here because, although WAG has extended the level at which surface forms can be specified semantically, there are still gaps. To allow for this, WAG allows input specifications to directly constrain the surface generation, either by directly specifying the grammatical feature(s) a given unit must have, or alternatively, specifying grammatical defaults: grammatical features which will be preferred if there is a choice. The advantages of WAG's input specification language are summarised below: 1. Interactional Specification: By placing the proposition as a role of the speech-act, rather than visa-versa, WAG allows cleaner integration into systems intended for dialogic interaction. WAG's input specification also allows a wider range of specification of the speech-act type than used in Penman and other sentencegeneration systems. 2. Ideational Specification: WAG allows two modes of expressing the KB -in one mode, each sentence specification is a self-contained specification, containing all the ideational information needed (the 'black-box' mode). In the other, a sentence specification contains only a pointer into the KB, allowing finer integration between text-planner and sentence realiser. The availability of both alternatives means that WAG can fit a wider range of generation environments. . Textual Specification: WAG introduces a high level means of representing the textual status of information to be expressed. Following Grosz (1977/86), andHalliday &Matthiessen (1995), I use the notion of textual spaces, partitionings of the ideation base, each of which shifts dynamically as the discourse unfolds. I have outlined: • a relevance space: the information which is rhetorically relevant at the present point of the discourse; • a shared-entity space: the information which is part of the shared knowledge of the speaker and hearer. • a recoverability space: the information which has entered the discourse context, including the entities which have been mentioned up to this point in the discourse. Information in the recoverability space can be presumed, or pronominalised. While the WAG generator has only been under development for a few years, and by a single author, in many aspects it meets, and in some ways surpasses, the functionality and power of the Penman system, as discussed above. It is also easier to use, having been designed to be part of a Linguist's Workbencha tool aimed at linguists without programming skills. The main advantage of the Penman system over the WAG system is the extensive linguistic resources available. Penman comes with a large grammar and semantics of English (and other languages). WAG comes with a mediumsized grammar of English. 9 Penman also supports a wider range of multi-lingual processing. 9While the WAG system can work with the grammar and lexicons of the Nigel resources, the resources which map grammar and semantics in Nigel are in a form incompatible with WAG). theory, the Speaker and Hearer fields are available for usermodelling purposes (cf. Figure 2 : 2N e g o t i a t o r y vs. S a l u t o r y : negotiatory speech-acts contribute towards the construction of an ideational proposition. while salutory moves do not, The Speech-Act Network serving a phatic function, for instance, greetings, farewells, and thank-yous.• Speech Function: The speech-function is the speaker's indication of what they want the hearer to do with the utterance. quality... q-material-quality. . . quality I -p°lar'quality''" t-process-quality... Figure 3 : 3The Upper Model • Role Information: a specification of the roles of the entity, and of the entities which fill these roles. 3.2 Expressing the KB: Stand-alone vs. Integrated approaches Figure 4 :Figure 4 44Building shows the forms which assert some knowledge about John and Mary into the KB. The information basically tells that Mary left a party because John arrived at the party, tell is a lisp macro form used to assert knowledge into the KB. • Object of Negotiation: Speech-acts can negotiate information (questions, statements, etc.), or action (commands, permission, etc.). A move with features (:and elicit negotiate-action) would be realised as a request for action (e.g., Will you go now?), while a move with features (:and propose negotiate-action) would be realised as a command (e.g., Go now. O. Upper Modeling: organizing knowledge for natural language processing. John Bateman, Proceedings of the Fifth International Workshop on Natural Language Generation. the Fifth International Workshop on Natural Language GenerationPittsburghBateman, John .1990 "Upper Modeling: organizing knowledge for natural language processing", Pro- ceedings of the Fifth International Workshop on Natural Language Generation, June 1990, Pitts- burgh. 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Anthony Davey, University PressEdinburghD. dissertation, University of EdinburghPublished version of PhDavey, Anthony 1974/1978 Discourse Production: a computer model of some aspects o] a speaker, Ed- inburgh University Press, Edinburgh, 1978. Pub- lished version of Ph.D. dissertation, University of Edinburgh, 1974. FUF: The Universal Unifier User Manual Version 5.0. Michael Elhadad, CUCS-038-91New YorkColumbia UniversityTechnical ReportElhadad, Michael 1991 "FUF: The Universal Uni- fier User Manual Version 5.0", Technical Report CUCS-038-91, Columbia University, New York, 1991. Demonstration of GENESYS: a very large semantically based Systemic Functional Grammar. Robin P Fawcett, H -Gordon, Tucker, Proceedings of the 13th Int. Con]. on Computational Linguistics (COLING '90). the 13th Int. Con]. on Computational Linguistics (COLING '90)Fawcett, Robin P. -Gordon H. Tucker (1990) "Demonstration of GENESYS: a very large se- mantically based Systemic Functional Grammar". In Proceedings of the 13th Int. Con]. on Computa- tional Linguistics (COLING '90). Reference Spaces in Text. J E Grimes, Proceedings of the 51st Nobel Symposium. the 51st Nobel SymposiumStockholmGrimes, J. E. 1982 "Reference Spaces in Text", in Proceedings of the 51st Nobel Symposium, Stock- holm. The Representation and Use of Focus in Dialog Understanding. B Grosz, Readings in Natural Language Processing. B.J. Grosz, K. Sparck-Jones, & B.L. WebberLos Altos, CAMorgan Kaufmann PublishersTechnical Report 151California.Grosz, B. 1977/86 "The Representation and Use of Focus in Dialog Understanding", Technical Re- port 151, Artificial Intelligence Centre, SRI Inter- national, California. Reprinted in B.J. Grosz, K. Sparck-Jones, & B.L. Webber (eds.), Readings in Natural Language Processing, Morgan Kaufmann Publishers, Los Altos, CA, 1986. Language as social semiotic. The social interpretation of language and meaning. M A K Halliday, Edward ArnoldLondonHalliday, M.A.K. 1978 Language as social semiotic. The social interpretation of language and meaning. London: Edward Arnold. Construing experience through meaning: a languagebased approach to cognition. M A K C I M Halliday, Matthiessen, Pinter: LondonHalliday, M.A.K. & C.I.M. Matthiessen 1995 Con- struing experience through meaning: a language- based approach to cognition. Pinter: London. On the Generator Input of the Future. Eduard Hovy, New Concepts in Natural Language Generation: Planning, Realisation and Systems. Helmut Horacek & Michael ZockLondonPinterHovy, Eduard 1993 "On the Generator Input of the Future", in Helmut Horacek & Michael Zock (eds.), New Concepts in Natural Language Genera- tion: Planning, Realisation and Systems, London: Pinter, p283-287. An Overview of the Penman Text Generation System. William C Mann, RR-84-127Technical ReportMann, William C. 1983 "An Overview of the Pen- man Text Generation System ", USC/ISI Technical Report RR-84-127. Demonstration of the Nigel Text Generation Computer Program. W C C I M Mann, Systemic Perspectives on Discourse. Benson and GreavesNorwoodAblex1Mann, W. C. & C. I. M. M:~tthiessen 1985 "Demon- stration of the Nigel Text Generation Computer Program", In Benson and Greaves (eds.), Systemic Perspectives on Discourse, Volume 1. Norwood: Ablex. James R Martin, Text: system and structure. Amsterdam: Benjamins. Martin, James R. 1992English Text: system and structure. Amsterdam: Benjamins. Language Production as a Process of Decision-making under Constraints, MIT Ph.D. Dissertation. D Mcdonald, MIT ReportMcDonald, D. 1980 Language Production as a Pro- cess of Decision-making under Constraints, MIT Ph.D. Dissertation, 1980. MIT Report. Mumble-86: Design and Implementation. 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The role of named entities in Web People Search

The ambiguity of person names in the Web has become a new area of interest for NLP researchers. This challenging problem has been formulated as the task of clustering Web search results (returned in response to a person name query) according to the individual they mention. In this paper we compare the coverage, reliability and independence of a number of features that are potential information sources for this clustering task, paying special attention to the role of named entities in the texts to be clustered. Although named entities are used in most approaches, our results show that, independently of the Machine Learning or Clustering algorithm used, named entity recognition and classification per se only make a small contribution to solve the problem.

The role of named entities in Web People Search August 2009. 2009 Javier Artiles Enrique Amigó enrique@lsi.uned.es Julio Gonzalo UNED NLP & IR group Madrid UNED NLP & IR group Madrid Spain, Spain UNED NLP & IR group Madrid Spain The role of named entities in Web People Search ACL and AFNLP the 2009 Conference on Empirical Methods in Natural Language ProcessingSingaporeAugust 2009. 2009 The ambiguity of person names in the Web has become a new area of interest for NLP researchers. This challenging problem has been formulated as the task of clustering Web search results (returned in response to a person name query) according to the individual they mention. In this paper we compare the coverage, reliability and independence of a number of features that are potential information sources for this clustering task, paying special attention to the role of named entities in the texts to be clustered. Although named entities are used in most approaches, our results show that, independently of the Machine Learning or Clustering algorithm used, named entity recognition and classification per se only make a small contribution to solve the problem. Introduction Searching the Web for names of people is a highly ambiguous task, because a single name tends to be shared by many people. This ambiguity has recently become an active research topic and, simultaneously, in a relevant application domain for web search services: Zoominfo.com,Spock.com,123people.com are examples of sites which perform web people search, although with limited disambiguation capabilities. A study of the query log of the AllTheWeb and Altavista search sites gives an idea of the relevance of the people search task: 11-17% of the queries were composed of a person name with additional terms and 4% were identified as person names (Spink et al., 2004). According to the data available from 1990 U.S. Census Bureau, only 90,000 different names are shared by 100 million people (Artiles et al., 2005). As the amount of information in the WWW grows, more of these people are mentioned in different web pages. Therefore, a query for a common name in the Web will usually produce a list of results where different people are mentioned. This situation leaves to the user the task of finding the pages relevant to the particular person he is interested in. The user might refine the original query with additional terms, but this risks excluding relevant documents in the process. In some cases, the existence of a predominant person (such as a celebrity or a historical figure) makes it likely to dominate the ranking of search results, complicating the task of finding information about other people sharing her name. The Web People Search task, as defined in the first WePS evaluation campaign (Artiles et al., 2007), consists of grouping search results for a given name according to the different people that share it. Our goal in this paper is to study which document features can contribute to this task, and in particular to find out which is the role that can be played by named entities (NEs): (i) How reliable is NEs overlap between documents as a source of evidence to cluster pages? (ii) How much recall does it provide? (iii) How unique is this signal? (i.e. is it redundant with other sources of information such as n-gram overlap?); and (iv) How sensitive is this signal to the peculiarities of a given NE recognition system, such as the granularity of its NE classification and the quality of its results? Our aim is to reach conclusions which are are not tied to a particular choice of Clustering or Machine Learning algorithms. We have taken two decisions in this direction: first, we have focused on the problem of deciding whether two web pages refer to the same individual or not (page coreference task). This is the kind of relatedness measure that most clustering algorithms use, but in this way we can factor out the algorithm and its parameter settings. Second, we have developed a measure, Maximal Pairwise Accuracy (PWA) which, given an information source for the problem, estimates an upper bound for the performance of any Machine Learning algorithm using this information. We have used PWA as the basic metric to study the role of different document features in solving the coreference problem, and then we have checked the predictive power of PWA with a Decision Tree algorithm. The remainder of the paper is organised as follows. First, we examine the previous work in Section 2. Then we describe the our experimental settings (datasets and features we have used) in Section 3 and our empirical study in Section 4. The paper ends with some conclusions in Section 5. Previous work In this section we will discuss (i) the state of the art in Web People Search in general, focusing on which features are used to solve the problem; and (ii) lessons learnt from the WePS evaluation campaign where most approaches to the problem have been tested and compared. The disambiguation of person names in Web results is usually compared to two other Natural Language Processing tasks: Word Sense Disambiguation (WSD) (Agirre and Edmonds, 2006) and Cross-document Coreference (CDC) (Bagga and Baldwin, 1998). Most of early research work on person name ambiguity focuses on the CDC problem or uses methods found in the WSD literature. It is only recently that the web name ambiguity has been approached as a separate problem and defined as an NLP task -Web People Search -on its own (Artiles et al., 2005;Artiles et al., 2007). Therefore, it is useful to point out some crucial differences between WSD, CRC and WePS: • WSD typically concentrates in the disambiguation of common words (nouns, verbs, adjectives) for which a relatively small number of senses exist, compared to the hundreds or thousands of people that can share the same name. • WSD can rely on dictionaries to define the number of possible senses for a word. In the case of name ambiguity no such dictionary is available, even though in theory there is an exact number of people that can be accounted as sharing the same name. • The objective of CDC is to reconstruct the coreference chain for every mention of a per-son. In Web person name disambiguation it suffices to group the documents that contain at least one mention to the same person. Before the first WePS evaluation campaign in 2007 (Artiles et al., 2007), research on the topic was not based on a consistent task definition, and it lacked a standard manually annotated testbed. In the WePS task, systems were given the top web search results produced by a person name query. The expected output was a clustering of these results, where each cluster should contain all and only those documents referring to the same individual. Features for Web People Search Many different features have been used to represent documents where an ambiguous name is mentioned. The most basic is a Bag of Words (BoW) representation of the document text. Withindocument coreference resolution has been applied to produce summaries of text surrounding occurrences of the name (Bagga and Baldwin, 1998;Gooi and Allan, 2004). Nevertheless, the full document text is present in most systems, sometimes as the only feature (Sugiyama and Okumura, 2007) and sometimes in combination with otherssee for instance (Chen and Martin, 2007;Popescu and Magnini, 2007)-. Other representations use the link structure (Malin, 2005) or generate graph representations of the extracted features (Kalashnikov et al., 2007). Some researchers (Cucerzan, 2007;Nguyen and Cao, 2008) have explored the use of Wikipedia information to improve the disambiguation process. Wikipedia provides candidate entities that are linked to specific mentions in a text. The obvious limitation of this approach is that only celebrities and historical figures can be identified in this way. These approaches are yet to be applied to the specific task of grouping search results. Biographical features are strongly related to NEs and have also been proposed for this task due to its high precision. Mann (2003) extracted these features using lexical patterns to group pages about the same person. Al-Kamha (2004) used a simpler approach, based on hand coded features (e.g. email, zip codes, addresses, etc). In Wan (2005), biographical information (person name, title, organisation, email address and phone number) improves the clustering results when combined with lexical features (words from the doc-ument) and NE (person, location, organisation). The most used feature for the Web People Search task, however, are NEs. Ravin (1999) introduced a rule-based approach that tackles both variation and ambiguity analysing the structure of names. In most recent research, NEs (person, location and organisations) are extracted from the text and used as a source of evidence to calculate the similarity between documents -see for instance (Blume, 2005;Chen and Martin, 2007;Popescu and Magnini, 2007;Kalashnikov et al., 2007)-. For instance, Blume (2005) uses NEs coocurring with the ambiguous mentions of a name as a key feature for the disambiguation process. Saggion (2008) compared the performace of NEs versus BoW features. In his experiments a only a representation based on Organisation NEs outperformed the word based approach. Furthermore, this result is highly dependent on the choice of metric weighting (NEs achieve high precision at the cost of a low recall and viceversa for BoW). In summary, the most common document representations for the problem include BoW and NEs, and in some cases biographical features extracted from the text. Named entities in the WePS campaign Among the 16 teams that submitted results for the first WePS campaign, 10 of them 1 used NEs in their document representation. This makes NEs the second most common type of feature; only the BoW feature was more popular. Other features used by the systems include noun phrases (Chen and Martin, 2007), word n-grams (Popescu and Magnini, 2007), emails and URLs (del Valle-Agudo et al., 2007), etc. In 2009, the second WePS campaign showed similar trends regarding the use of NE features (Artiles et al., 2009). Due to the complexity of systems, the results of the WePS evaluation do not provide a direct answer regarding the advantages of using NEs over other computationally lighter features such as BoW or word n-grams. But the WePS campaigns did provide a useful, standardised resource to perform the type of studies that were not possible before. In the next Section we describe this dataset and how it has been adapted for our purposes. Each WePS dataset consists of 30 test cases: a random sample of 10 names from the US Census, 10 names from Wikipedia, and 10 names from Programme Committees in the Computer Science domain (ACL and ECDL). Each test case consists of, at most, 100 web pages from the top search results of a web search engine, using a (quoted) person name as query. For each test case, annotators were asked to organise the web pages in groups where all documents refer to the same person. In cases where a web page refers to more than one person using the same ambiguous name (e.g. a web page with search results from Amazon), the document is assigned to as many groups as necessary. Documents were discarded when they did not contain any useful information about the person being referred. Both the WePS-1 and WePS-2 testbeds have been used to evaluate clustering systems by WePS task participants, and are now the standard testbed to test Web People Search systems. Features The evaluated features can be grouped in four main groups: token-based, n-grams, phrases and NEs. Wherever possible, we have generated local versions of these features that only consider the sentences of the text that mention the ambiguous person name 4 . Token-based features considered include document full text tokens, lemmas (using the OAK analyser, see below), title, snippet (returned in the list of search results) and URL (tokenised using non alphanumeric characters as boundaries) tokens. English stopwords were removed, including Web specific stopwords, as file and domain extensions, etc. We generated word n-grams of length 2 to 5, using the sentences found in the document text. Punctuation tokens (commas, dots, etc) were generalised as the same token. N-grams were discarded when they were composed only of stopwords or when they did not contain at least one token formed by alphanumeric characters (e.g. ngrams like "at the" or "# @"). Noun phrases (using OAK analyser) were detected in the document and filtered in a similar way. Named entities were extracted using two different tools: the Stanford NE Recogniser and the OAK System 5 . Stanford NE Recogniser 6 is a high-performance Named Entity Recognition (NER) system based on Machine Learning. It provides a general implementation of linear chain Conditional Random Field sequence models and includes a model trained on data from CoNLL, MUC6, MUC7, and ACE newswire. Three types of entities were extracted: person, location and organisation. OAK 7 is a rule based English analyser that includes many functionalities (POS tagger, stemmer, chunker, Named Entity (NE) tagger, dependency analyser, parser, etc). It provides a fine grained NE recognition covering 100 different NE types (Sekine, 2008). Given the sparseness of most of these fine-grained NE types, we have merged them in coarser groups: event, facility, location, person, organisation, product, periodx, timex and numex. We have also used the results of a baseline NE recognition for comparison purposes. This method detects sequences of two or more uppercased tokens in the text, and discards those that are found lowercased in the same document or that are composed solely of stopwords. Other features are: emails, outgoing links found in the web pages and two boolean flags that indicate whether a pair of documents is linked or belongs to the same domain. Because of their low impact in the results these features haven't received an individual analysis, but they are included in the "all features" combination in Figure 7. Experiments and results Reformulating WePS as a classification task As our goal is to study the impact of different features (information sources) in the task, a direct evaluation in terms of clustering has serious disadvantages. Given the output of a clustering system it is not straightforward to assess why a document has been assigned to a particular cluster. There are at least three different factors: the document similarity function, the clustering algorithm and its parameter settings. Features are part of the document similarity function, but its performance in the clustering task depends on the other factors as well. This makes it difficult to perform error analysis in terms of the features used to represent the documents. Therefore we have decided to transform the clustering problem into a classification problem: deciding whether two documents refer to the same person. Each pair of documents in a name dataset is considered a classification instance. Instances are labelled as coreferent (if they share the same cluster in the gold standard) or non coreferent (if they do not share the same cluster). Then we can evaluate the performance of each feature separately by measuring its ability to rank coreferent pairs higher and non coreferent pairs lower. In the case of feature combinations we can study them by training a classifier or using the maximal pairwise accuracy methods (explained in Section 4.3). Each instance (pair of documents) is represented by the similarity scores obtained using different features and similarity metrics. We have calculated for each feature three similarity metrics: Dice's coefficient, cosine (using standard tf.idf weighting) and a measure that simply counts the size of the intersection set for a given feature between both documents. After testing these metrics we found that Dice provides the best results across different feature types. Differences between Dice and cosine were consistent, although they were not especially large. A possible explanation is that Dice does not take into account the redundancy of an n-gram or NE in the document, and the cosine distance does. This can be a crucial factor, for instance, in the document retrieval by topic; but it doesn't seem to be the case when dealing with name ambiguity. The resulting classification testbed consists of 293,914 instances with the distribution shown in Analysis of individual features There are two main aspects related with the usefulness of a feature for WePS task. The first one is its performance. That is, to what extent the similarity between two documents according to a feature implies that both mention the same person. The second aspect is to what extent a feature is orthogonal or redundant with respect to the standard token based similarity. Feature performance According to the transformation of WePS clustering problem into a classification task (described in Section 4.1), we follow the next steps to study the performance of individual features. First, we compute the Dice coefficient similarity over each feature for all document pairs. Then we rank the document pair instances according to these similarities. A good feature should rank positive instances on top. If the number of coreferent pairs in the top n pairs is t n and the total number of coreferent pairs is t, then P = tn n and R = tn t . We plot the obtained precision/recall curves in Figures 1, 2, 3 and 4. From the figures we can draw the following conclusions: First, considering subsets of tokens or lemmatised tokens does not outperform the basic token distance (figure 1 compares token-based features). We see that only local and snippet tokens perform slightly better at low recall values, but do not go beyond recall 0.3. Second, shallow parsing or n-grams longer than 2 do not seem to be effective, but using bi-grams improves the results in comparison with tokens. Figure 2 compares n-grams of different sizes with noun phrases and tokens. Overall, noun phrases have a poor performance, and bi-grams give the best results up to recall 0.7. Four-grams give slightly better precision but only reach 0.3 recall, and three-grams do not give better precision than bi-grams. Figure 3 and Figure 4 display the results obtained by the Stanford and OAK NER tools respectively. In the best case, Stanford person and organisation named entities obtain results that match the tokens feature, but only at lower levels of recall. Finally, using different NER systems clearly leads to different results. Surprisingly, the baseline NE system yields better results in a one to one comparison, although it must be noted that this baseline agglomerates different types of en- tities that are separated in the case of Stanford and OAK, and this has a direct impact on its recall. The OAK results are below the tokens and NE baseline, possibly due to the sparseness of its very fine grained features. In NE types, cases such as person and organisation results are still lower than obtained with Stanford. Redundancy In addition to performance, named entities (as well as other features) are potentially useful for the task only if they provide information that complements (i.e. that does not substantially overlap) the basic token based metric. To estimate this redundancy, let us consider all document tuples of size four < a, b, c, d >. In 99% of the cases, token similarity is different for < a, b > than for < c, d >. We take combinations such that < a, b > are more similar to each other than < c, d > according to tokens. That is: sim token (a, b) > sim token (c, d) Then for any other feature similarity sim x (a, b), we will talk about redundant samples when sim x (a, b) > sim x (c, d), non redundant samples when sim x (a, b) < sim x (c, d), and non informative samples when sim x (a, b) = sim x (c, d). If all samples are redundant or non informative, then sim x does not provide additional information for the classification task. Figure 5 shows the proportion of redundant, non redundant and non informative samples for several similarity criteria, as compared to token-based similarity. In most cases NE based similarities give little additional information: the baseline NE recogniser, which has the largest independent contribution, gives additional information in less than 20% of cases. In summary, analysing individual features, the NEs do not outperform BoW in terms of the classification task. In addition, NEs tend to be redundant regarding BoW. However, if we are able to combine optimally the contributions of the different features, the BoW approach could be improved. We address this issue in the next section. Up to now we have analysed the usefulness of individual features for the WePS Task. However, this begs to ask to what extent the NE features can contribute to the task when they are combined together and with token and n-gram based features. First, we use each feature combinations as the input for a Machine Learning algorithm. In particular, we use a Decision Tree algorithm and WePS-1 data for training and WePS-2 data for testing. The Decision Tree algorithm was chosen because we have a small set of features to train (similarity metrics) and some of these features output Boolean values. Results obtained with this setup, however, can be dependent on the choice of the ML approach. To overcome this problem, in addition to the results of a Decision Tree Machine Learning algorithm, we introduce a Maximal Pairwise Accuracy (MPA) measure that provides an upper bound for any machine learning algorithm using a feature combination. We can estimate the performance of an individual similarity feature x in terms of accuracy. It is considered a correct answer when the similarity x(a, a ) between two pages referring to the same person is higher than the similarity x(b, c) between two pages referring to different people. Let us call this estimation Pairwise Accuracy. In terms of probability it can be defined as: P W A = Prob(x(a, a ) > x(c, d)) PWA is defined over a single feature (similarity metric). When considering more than one similarity measure, the results depend on how measures are weighted. In that case we assume that the best possible weighting is applied. When combining a set of features X = {x 1 . . . x n }, a perfect Machine Learning algorithm would learn to always "listen" to the features giving correct information and ignore the features giving erroneous information. In other words, if at least one feature gives correct information, then the perfect algorithm would produce a correct output. This is what we call the Maximal Pairwise Accuracy estimation of an upper bound for any ML system using the set of features X: MaxPWA(X) = Prob(∃x ∈ X.x(a, a ) > x(c, d)) The upper bound (MaxPWA) of feature combinations happens to be highly correlated with the PWA obtained by the Decision Tree algorithm (using its confidence values as a similarity metric). Figure 6 shows this correlation for several features combinations. This is an indication that the Decision Tree is effectively using the information in the feature set. Figure 7 shows the PWA upper bound estimation and the actual PWA performance of a Decision Tree ML algorithm for three combinations: (i) all features; (ii) non linguistic features, i.e., features which can be extracted without natural language processing machinery: tokens, url, title, snippet, local tokens, n-grams and local n-grams; and (iii) just tokens. The results show that according to both the Decision Tree results and the upperbound (MaxPWA), adding new features to tokens improves the classification. However, taking nonlinguistic features obtains similar results than taking all features. Our conclusion is that NE features are useful for the task, but do not seem to offer a competitive advantage when compared with nonlinguistic features, and are more computationally expensive. Note that we are using NE features in a direct way: our results do not exclude the possibility of effectively exploiting NEs in more sophisticated ways, such as, for instance, exploiting the underlying social network relationships between NEs in the texts. Results on the clustering task In order to validate our results, we have tested whether the classifiers learned with our feature sets lead to competitive systems for the full clustering task. In order to do so, we use the output of the classifiers as similarity metrics for a particular clustering algorithm, using WePS-1 to train the classifiers and WePS-2 for testing. We have used a Hierarchical Agglomerative Clustering algorithm (HAC) with single linkage, using the classifier's confidence value in the negative answer for each instance as a distance metric 8 between document pairs. HAC is the algorithm used by some of the best performing systems in the WePS-2 evaluation. The distance threshold was trained using the WePS-1 data. We report results with the official WePS-2 evaluation metrics: extended B-Cubed Precision and Recall (Amigó et al., 2008). Two Decision Tree models were evaluated: (i) ML-ALL is a model trained using all the available features (which obtains 0.76 accuracy in the classification task) (ii) ML-NON LING was trained with all the features except for OAK and Stanford NEs, noun phrases, lemmas and gazetteer features (which obtains 0.75 accuracy in the classification task). These are the same classifiers considered in Figure 7. Table 2 shows the results obtained in the clustering task by the two DT models, together with the four top scoring WePS-2 systems and the average values for all WePS-2 systems. We found that a ML based clustering using only non linguistic information slightly outperforms the best participant in WePS-2. Surprisingly, adding linguistic information (NEs, noun phrases, etc.) has a small negative impact on the results (0.81 versus 0.83), although the classifier with linguistic information was a bit better than the non-linguistic one. This seems to be another indication that the use of noun phrases and other linguistic features to improve the task is non-obvious to say the least. Table 2: Evaluation on the WePS-2 clustering task Conclusions We have presented an empirical study that tries to determine the potential role of several sources of information to solve the Web People Search clustering problem, with a particular focus on studying the role of named entities in the task. To abstract the study from the particular choice of a clustering algorithm and a parameter setting, we have reformulated the problem as a coreference classification task: deciding whether two pages refer to the same person or not. We have also proposed the Maximal Pairwise Accuracy estimation that establish an upper bound for the results obtained by any Machine Learning algorithm using a particular set of features. Our results indicate that (i) NEs do not provide a substantial competitive advantage in the clustering process when compared to a rich combination of simpler features that do not require linguistic processing (local, global and snippet tokens, n-grams, etc.); (ii) results are sensitive to the NER system used: when using all NE features for training, the richer number of features provided by OAK seems to have an advantage over the simpler types in Stanford NER and the baseline NER system. This is not exactly a prescription against the use of NEs for Web People Search, because linguistic knowledge can be useful for other aspects of the problem, such as visualisation of results and description of the persons/clusters obtained: for example, from a user point of view a network of the connections of a person with other persons and organisations (which can only be done with NER) can be part of a person's profile and may help as a summary of the cluster contents. But from the perspective of the clustering problem per se, a direct use of NEs and other linguistic features does not seem to pay off. 1 By team ID: CU-COMSEM, IRST-BP, PSNUS, SHEF, FICO, UNN, AUG, JHU1, DFKI2 Figure 1 :Figure 2 : 12Precision/Recall curve of token-based features Precision/Recall curve of word n-grams Third, individual types of NEs do not improve over tokens. Figure 3 :Figure 4 : 34Precision/Recall curve of NEs obtained with the Stanford NER tool Precision/Recall curve of NEs obtained with the OAK NER tool Figure 5 : 5Independence of similarity criteria with respect to the token based feature 4.3 Analysis of feature combinations Figure 6 :Figure 7 : 67Estimated PWA upper bound versus the real PWA of decision trees trained with feature combinations Maximal Pairwise Accuracy vs. results of a Decision Tree Table 1 , 1where each instance is represented by 69 features.true false total WePS1 61,290 122,437 183,727 WePS2 54,641 55,546 110,187 WePS1+WePS2 115,931 177,983 293,914 Table 1 : 1Distribution of classification instances The WePS-1 corpus includes data from the Web03 testbed(Mann, 2006) which follows similar annotation guidelines, although the number of document per ambiguous name is more variable.3 Both corpora are available from the WePS website http://nlp.uned.es/weps 4 A very sparse feature might never occur in a sentence with the person name. In that cases there is no local version of the feature. From the output of both systems we have discarded person NEs made of only one token (these are often first names that significantly deteriorate the quality of the comparison between documents). 6 http://nlp.stanford.edu/software/CRF-NER.shtml 7 http://nlp.cs.nyu.edu/oak . OAK was also used to detect noun phrases and extract lemmas from the text. The DT classifier output consists of two confidence values, one for the positive and one for the negative answer, that add up to 1.0 . AcknowledgmentsThis work has been partially supported by the Regional Government of Madrid, project MAVIR S0505-TIC0267. Word Sense Disambiguation: Algorithms and Applications. Eneko Agirre and Philip EdmondsSpringerEneko Agirre and Philip Edmonds, editors. 2006. Word Sense Disambiguation: Algorithms and Appli- cations. Springer. Grouping search-engine returned citations for person-name queries. 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Domain Adaptation for Neural Machine Translation

The development of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and training time. However, such translation models struggle when translating text of a new or unfamiliar domain(Koehn and Knowles, 2017). A domain may be a well-defined topic, text of a specific provenance, text of unknown provenance with an identifiable vocabulary distribution, or language with some other stylometric feature.NMT models can achieve good translation performance on domain-specific data via simple tuning on a representative training corpus. However, such data-centric approaches have negative sideeffects, including over-fitting and brittleness on narrow-distribution samples and catastrophic forgetting of previously seen domains.This thesis focuses instead on more robust approaches to domain adaptation for NMT. We consider the case where a system is adapted to a specified domain of interest, but may also need to accommodate new language, or domain-mismatched sentences. As well, the thesis highlights that lines of MT research other than performance on traditional 'domains' can be framed as domain adaptation problems. Techniques that are effective for e.g. adapting machine translation to a biomedical domain can also be used when making use of language representations beyond the surface-level, or when encouraging better machine translation of gendered terms.Over the course of the thesis we pose and answer five research questions: * Now at RWS Language Weaver * © 2022 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND.How effective are data-centric approaches to NMT domain adaptation? We find that simply selecting-domain relevant training data and finetuning an existing model achieves strong results, especially when a domain-specific data curriculum is used during training. However, we also demonstrate the side-effects of exposure bias and catastrophic forgetting.Given an adaptation set, what training schemes improve NMT quality? We investigate two variations on the NMT adaptation algorithm, regularized tuning including Elastic Weighting Consolidation, and a new variant of Minimum Risk Training. We show they can mitigate the pitfalls of datacentric adaptation. Aside from avoiding the failure modes of data-centric methods, we show these methods may also give better model convergence.Can domain adaptation help when the test domain is unknown? Most approaches to domain adaptation in the literature assume any unseen test data of interest has a known, fixed domain, with a matching set of tuning data. This thesis works towards relaxing these assumptions. We show that adapting sequentially across domains with regularization can achieve good cross-domain performance without knowing the specific test domain. We also explore domain adaptive model ensembling and automatic model selection. We find this can outperform oracle approaches, which select the best model for inference by using known provenance labels.Can changing data representation have similar effects to changing data domain? Unlike data domain, data representation -for example, choice of subword granularity or use of syntactic annotation -does not change meaning or correspond to provenance. However, like domain, it can affect the information available to the model, and therefore impacts NMT quality for a given input. We combine multiple representations in a single model or in ensembles in a way reminiscent of multi-domain translation. In particular, we develop a scheme for ensembles of models producing multiple target language representations, and show that multi-representation ensembles improve syntax-based NMT.Can gender bias in NMT systems be mitigated by treating it as a domain? We show that translation of gendered language is strongly influenced by vocabulary distributions in the training data, a hallmark of a domain. We also show that data selection methods have a strong effect on apparent NMT gender bias. We apply techniques from elsewhere in the thesis to tune NMT on a 'gender' domain, specifically regularized adaptation and multi-domain inference. We show this can improve gendered language translation while maintaining generic translation quality.Human language itself is constantly adapting, and people's interactions with and expectations of MT are likewise evolving. With this thesis we hope to draw attention to the possible benefits and applications of different approaches to adapting machine translation. We hope that future work on adaptive NMT will focus not only on the language of immediate interest but the machine translation abilities or tendencies that we wish to maintain or abandon.

Domain Adaptation for Neural Machine Translation Danielle Saunders ds636@cantab.ac.uk Department of Engineering University of Cambridge Cambridge UK Domain Adaptation for Neural Machine Translation The development of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and training time. However, such translation models struggle when translating text of a new or unfamiliar domain(Koehn and Knowles, 2017). A domain may be a well-defined topic, text of a specific provenance, text of unknown provenance with an identifiable vocabulary distribution, or language with some other stylometric feature.NMT models can achieve good translation performance on domain-specific data via simple tuning on a representative training corpus. However, such data-centric approaches have negative sideeffects, including over-fitting and brittleness on narrow-distribution samples and catastrophic forgetting of previously seen domains.This thesis focuses instead on more robust approaches to domain adaptation for NMT. We consider the case where a system is adapted to a specified domain of interest, but may also need to accommodate new language, or domain-mismatched sentences. As well, the thesis highlights that lines of MT research other than performance on traditional 'domains' can be framed as domain adaptation problems. Techniques that are effective for e.g. adapting machine translation to a biomedical domain can also be used when making use of language representations beyond the surface-level, or when encouraging better machine translation of gendered terms.Over the course of the thesis we pose and answer five research questions: * Now at RWS Language Weaver * © 2022 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND.How effective are data-centric approaches to NMT domain adaptation? We find that simply selecting-domain relevant training data and finetuning an existing model achieves strong results, especially when a domain-specific data curriculum is used during training. However, we also demonstrate the side-effects of exposure bias and catastrophic forgetting.Given an adaptation set, what training schemes improve NMT quality? We investigate two variations on the NMT adaptation algorithm, regularized tuning including Elastic Weighting Consolidation, and a new variant of Minimum Risk Training. We show they can mitigate the pitfalls of datacentric adaptation. Aside from avoiding the failure modes of data-centric methods, we show these methods may also give better model convergence.Can domain adaptation help when the test domain is unknown? Most approaches to domain adaptation in the literature assume any unseen test data of interest has a known, fixed domain, with a matching set of tuning data. This thesis works towards relaxing these assumptions. We show that adapting sequentially across domains with regularization can achieve good cross-domain performance without knowing the specific test domain. We also explore domain adaptive model ensembling and automatic model selection. We find this can outperform oracle approaches, which select the best model for inference by using known provenance labels.Can changing data representation have similar effects to changing data domain? Unlike data domain, data representation -for example, choice of subword granularity or use of syntactic annotation -does not change meaning or correspond to provenance. However, like domain, it can affect the information available to the model, and therefore impacts NMT quality for a given input. We combine multiple representations in a single model or in ensembles in a way reminiscent of multi-domain translation. In particular, we develop a scheme for ensembles of models producing multiple target language representations, and show that multi-representation ensembles improve syntax-based NMT.Can gender bias in NMT systems be mitigated by treating it as a domain? We show that translation of gendered language is strongly influenced by vocabulary distributions in the training data, a hallmark of a domain. We also show that data selection methods have a strong effect on apparent NMT gender bias. We apply techniques from elsewhere in the thesis to tune NMT on a 'gender' domain, specifically regularized adaptation and multi-domain inference. We show this can improve gendered language translation while maintaining generic translation quality.Human language itself is constantly adapting, and people's interactions with and expectations of MT are likewise evolving. With this thesis we hope to draw attention to the possible benefits and applications of different approaches to adapting machine translation. We hope that future work on adaptive NMT will focus not only on the language of immediate interest but the machine translation abilities or tendencies that we wish to maintain or abandon. The development of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and training time. However, such translation models struggle when translating text of a new or unfamiliar domain (Koehn and Knowles, 2017). A domain may be a well-defined topic, text of a specific provenance, text of unknown provenance with an identifiable vocabulary distribution, or language with some other stylometric feature. NMT models can achieve good translation performance on domain-specific data via simple tuning on a representative training corpus. However, such data-centric approaches have negative sideeffects, including over-fitting and brittleness on narrow-distribution samples and catastrophic forgetting of previously seen domains. This thesis focuses instead on more robust approaches to domain adaptation for NMT. We consider the case where a system is adapted to a specified domain of interest, but may also need to accommodate new language, or domain-mismatched sentences. As well, the thesis highlights that lines of MT research other than performance on traditional 'domains' can be framed as domain adaptation problems. Techniques that are effective for e.g. adapting machine translation to a biomedical domain can also be used when making use of language representations beyond the surface-level, or when encouraging better machine translation of gendered terms. Over the course of the thesis we pose and answer five research questions: * Now at RWS Language Weaver * © 2022 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND. How effective are data-centric approaches to NMT domain adaptation? We find that simply selecting-domain relevant training data and finetuning an existing model achieves strong results, especially when a domain-specific data curriculum is used during training. However, we also demonstrate the side-effects of exposure bias and catastrophic forgetting. Given an adaptation set, what training schemes improve NMT quality? We investigate two variations on the NMT adaptation algorithm, regularized tuning including Elastic Weighting Consolidation, and a new variant of Minimum Risk Training. We show they can mitigate the pitfalls of datacentric adaptation. Aside from avoiding the failure modes of data-centric methods, we show these methods may also give better model convergence. Can domain adaptation help when the test domain is unknown? Most approaches to domain adaptation in the literature assume any unseen test data of interest has a known, fixed domain, with a matching set of tuning data. This thesis works towards relaxing these assumptions. We show that adapting sequentially across domains with regularization can achieve good cross-domain performance without knowing the specific test domain. We also explore domain adaptive model ensembling and automatic model selection. We find this can outperform oracle approaches, which select the best model for inference by using known provenance labels. Can changing data representation have similar effects to changing data domain? Unlike data domain, data representation -for example, choice of subword granularity or use of syntactic annotation -does not change meaning or correspond to provenance. However, like domain, it can affect the information available to the model, and therefore impacts NMT quality for a given input. We combine multiple representations in a single model or in ensembles in a way reminiscent of multi-domain translation. In particular, we develop a scheme for ensembles of models producing multiple target language representations, and show that multi-representation ensembles improve syntax-based NMT. Can gender bias in NMT systems be mitigated by treating it as a domain? We show that translation of gendered language is strongly influenced by vocabulary distributions in the training data, a hallmark of a domain. We also show that data selection methods have a strong effect on apparent NMT gender bias. We apply techniques from elsewhere in the thesis to tune NMT on a 'gender' domain, specifically regularized adaptation and multi-domain inference. We show this can improve gendered language translation while maintaining generic translation quality. Human language itself is constantly adapting, and people's interactions with and expectations of MT are likewise evolving. With this thesis we hope to draw attention to the possible benefits and applications of different approaches to adapting machine translation. We hope that future work on adaptive NMT will focus not only on the language of immediate interest but the machine translation abilities or tendencies that we wish to maintain or abandon. http://www.hpc.cam.ac.uk AcknowledgmentsThe author would like to thank her PhD supervisor, Bill Byrne. The work was supported by EPSRC grants EP/M508007/1 and EP/N509620/1, with some experiments performed using resources from the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service 1 funded by EPSRC Tier-2 capital grant EP/P020259/1. Philipp Koehn, Rebecca Knowles, Six Challenges for Neural Machine Translation Proceedings of the First Workshop on Neural Machine Translation. VancouverKoehn, Philipp and Rebecca Knowles. 2017. Six Chal- lenges for Neural Machine Translation Proceedings of the First Workshop on Neural Machine Transla- tion, Vancouver, 28-39.

Using Anaphora Resolution to Improve Opinion Target Identification in Movie Reviews

Current work on automatic opinion mining has ignored opinion targets expressed by anaphorical pronouns, thereby missing a significant number of opinion targets. In this paper we empirically evaluate whether using an off-the-shelf anaphora resolution algorithm can improve the performance of a baseline opinion mining system. We present an analysis based on two different anaphora resolution systems. Our experiments on a movie review corpus demonstrate, that an unsupervised anaphora resolution algorithm significantly improves the opinion target extraction. We furthermore suggest domain and task specific extensions to an off-the-shelf algorithm which in turn yield significant improvements.

Using Anaphora Resolution to Improve Opinion Target Identification in Movie Reviews Association for Computational LinguisticsCopyright Association for Computational LinguisticsJuly 2010. 2010 Niklas Jakob Iryna Gurevych Technische Universität Darmstadt Hochschulstraße 10 64289Darmstadt Technische Universität Darmstadt Hochschulstraße 10 64289Darmstadt Using Anaphora Resolution to Improve Opinion Target Identification in Movie Reviews Proceedings of the ACL 2010 Conference Short Papers the ACL 2010 Conference Short PapersUppsala, Sweden; cAssociation for Computational LinguisticsJuly 2010. 2010 Current work on automatic opinion mining has ignored opinion targets expressed by anaphorical pronouns, thereby missing a significant number of opinion targets. In this paper we empirically evaluate whether using an off-the-shelf anaphora resolution algorithm can improve the performance of a baseline opinion mining system. We present an analysis based on two different anaphora resolution systems. Our experiments on a movie review corpus demonstrate, that an unsupervised anaphora resolution algorithm significantly improves the opinion target extraction. We furthermore suggest domain and task specific extensions to an off-the-shelf algorithm which in turn yield significant improvements. Introduction Over the last years the task of opinion mining (OM) has been the topic of many publications. It has been approached with different goals in mind: Some research strived to perform subjectivity analysis at the document or sentence level, without focusing on what the individual opinions uttered in the document are about. Other approaches focused on extracting individual opinion words or phrases and what they are about. This aboutness has been referred to as the opinion target or opinion topic in the literature from the field. In this work our goal is to extract opinion target -opinion word pairs from sentences from movie reviews. A challenge which is frequently encountered in text mining tasks at this level of granularity is, that entities are being referred to by anaphora. In the task of OM, it can therefore also be necessary to analyze more than the content of one individual sentence when extracting opinion targets. Consider this example sentence: "Simply put, it's unfathomable that this movie cracks the Top 250. It is absolutely awful.". If one wants to extract what the opinion in the second sentence is about, an algorithm which resolves the anaphoric reference to the opinion target is required. The extraction of such anaphoric opinion targets has been noted as an open issue multiple times in the OM context (Zhuang et al., 2006;Hu and Liu, 2004;. It is not a marginal phenomenon, since Kessler and Nicolov (2009) report that in their data, 14% of the opinion targets are pronouns. However, the task of resolving anaphora to mine opinion targets has not been addressed and evaluated yet to the best of our knowledge. In this work, we investigate whether anaphora resolution (AR) can be successfully integrated into an OM algorithm and whether we can achieve an improvement regarding the OM in doing so. This paper is structured as follows: Section 2 discusses the related work on opinion target identification and OM on movie reviews. Section 3 outlines the OM algorithm we employed by us, while in Section 4 we discuss two different algorithms for AR which we experiment with. Finally, in Section 5 we present our experimental work including error analysis and discussion, and we conclude in Section 6. Related Work We split the description of the related work in two parts: In Section 2.1 we discuss the related work on OM with a focus on approaches for opinion target identification. In Section 2.2 we elaborate on findings from related OM research which also worked with movie reviews as this is our target domain in the present paper. Opinion Target Identification The extraction of opinions and especially opinion targets has been performed with quite diverse approaches. Initial approaches combined statistical information and basic linguistic features such as part-of-speech tags. The goal was to identify the opinion targets, here in form of products and their attributes, without a pre-built knowledge base which models the domain. For the target candidate identification, simple part-of-speech patterns were employed. The relevance ranking and extraction was then performed with different statistical measures: Pointwise Mutual Information (Popescu and Etzioni, 2005), the Likelihood Ratio Test and Association Mining (Hu and Liu, 2004). A more linguistically motivated approach was taken by Kim and Hovy (2006) through identifying opinion holders and targets with semantic role labeling. This approach was promising, since their goal was to extract opinions from professionally edited content i.e. newswire. Zhuang et al. (2006) present an algorithm for the extraction of opinion target -opinion word pairs. The opinion word and target candidates are identified in the annotated corpus and their extraction is then performed by applying possible paths connecting them in a dependency graph. These paths are combined with part-of-speech information and also learned from the annotated corpus. To the best of our knowledge, there is currently only one system which integrates coreference information in OM. The algorithm by Stoyanov and Cardie (2008) identifies coreferring targets in newspaper articles. A candidate selection or extraction step for the opinion targets is not required, since they rely on manually annotated targets and focus solely on the coreference resolution. However they do not resolve pronominal anaphora in order to achieve that. Opinion Mining on Movie Reviews There is a huge body of work on OM in movie reviews which was sparked by the dataset from Pang and Lee (2005). This dataset consists of sentences which are annotated as expressing positive or negative opinions. An interesting insight was gained from the document level sentiment analysis on movie reviews in comparison to documents from other domains: Turney (2002) observes that the movie reviews are hardest to classify since the review authors tend to give information about the storyline of the movie which often contain characterizations, such as "bad guy" or "violent scene". These statements however do not reflect any opin-ions of the reviewers regarding the movie. Zhuang et al. (2006) also observe that movie reviews are different from e.g. customer reviews on Amazon.com. This is reflected in their experiments, in which their system outperforms the system by Hu and Liu (2004) which attributes an opinion target to the opinion word which is closest regarding word distance in a sentence. The sentences in the movie reviews tend to be more complex, which can also be explained by their origin. The reviews were taken from the Internet Movie Database 1 , on which the users are given a set of guidelines on how to write a review. Due to these insights, we are confident that the overall textual quality of the movie reviews is high enough for linguistically more advanced technologies such as parsing or AR to be successfully applied. Opinion Target Identification Dataset Currently the only freely available dataset annotated with opinions including annotated anaphoric opinion targets is a corpus of movie reviews by Zhuang et al. (2006). Kessler and Nicolov (2009) describe a collection of product reviews in which anaphoric opinion targets are also annotated, but it is not available to the public (yet). Zhuang et al. (2006) used a subset of the dataset they published (1829 documents), namely 1100 documents, however they do not state which documents comprise this subset used in their evaluation. In our experiments, we therefore use the complete dataset available, detailed in Table 1. As shown, roughly 9.5% of the opinion targets are referred to by pronouns. Table 2 outlines detailed statistics on which pronouns occur as opinion targets. Baseline Opinion Mining We reimplemented the algorithm presented by Zhuang et al. (2006) as the baseline for our experiments. Their approach is a supervised one. The annotated dataset is split in five folds, of which four are used as the training data. In the first step, opinion target and opinion word candidates are extracted from the training data. Frequency counts of the annotated opinion targets and opinion words are extracted from four training folds. The most frequently occurring opinion targets and opinion words are selected as candidates. Then the annotated sentences are parsed and a graph containing the words of the sentence is created, which are connected by the dependency relations between them. For each opinion target -opinion word pair, the shortest path connecting them is extracted from the dependency graph. A path consists of the part-of-speech tags of the nodes and the dependency types of the edges. In order to be able to identify rarely occurring opinion targets which are not in the candidate list, they expand it by crawling the cast and crew names of the movies from the IMDB. How this crawling and extraction is done is not explained. Algorithms for Anaphora Resolution As pointed out by Charniak and Elsner (2009) there are hardly any freely available systems for AR. Although Charniak and Elsner (2009) present a machine-learning based algorithm for AR, they evaluate its performance in comparison to three non machine-learning based algorithms, since those are the only ones available. They observe that the best performing baseline algorithm (OpenNLP) is hardly documented. The algorithm with the next-to-highest results in (Charniak and Elsner, 2009) is MARS (Mitkov, 1998) from the GuiTAR (Poesio and Kabadjov, 2004) toolkit. This algorithm is based on statistical analysis of the antecedent candidates. Another promising algorithm for AR employs a rule based approach for antecedent identification. The Cog-NIAC algorithm (Baldwin, 1997) was designed for high-precision AR. This approach seems like an adequate strategy for our OM task, since in the dataset used in our experiments only a small fraction of the total number of pronouns are ac-tual opinion targets (see Table 1). We extended the CogNIAC implementation to also resolve "it" and "this" as anaphora candidates, since off-the-shelf it only resolves personal pronouns. We will refer to this extension with [id]. Both algorithms follow the common approach that noun phrases are antecedent candidates for the anaphora. In our experiments we employed both the MARS and the CogNIAC algorithm, for which we created three extensions which are detailed in the following. Extensions of CogNIAC We identified a few typical sources of errors in a preliminary error analysis. We therefore suggest three extensions to the algorithm which are on the one hand possible in the OM setting and on the other hand represent special features of the target discourse type: [1.] We observed that the Stanford Named Entity Recognizer (Finkel et al., 2005) is superior to the Person detection of the (MUC6 trained) CogNIAC implementation. We therefore filter out Person antecedent candidates which the Stanford NER detects for the impersonal and demonstrative pronouns and Location & Organization candidates for the personal pronouns. This way the input to the AR is optimized. [2.] The second extension exploits the fact that reviews from the IMDB exhibit certain contextual properties. They are gathered and to be presented in the context of one particular entity (=movie). The context or topic under which it occurs is therefore typically clear to the reader and is therefore not explicitly introduced in the discourse. This is equivalent to the situational context we often refer to in dialogue. In the reviews, the authors often refer to the movie or film as a whole by a pronoun. We exploit this by an additional rule which resolves an impersonal or demonstrative pronoun to "movie" or "film" if there is no other (matching) antecedent candidate in the previous two sentences. [3.] The rules by which CogNIAC resolves anaphora were designed so that anaphora which have ambiguous antecedents are left unresolved. This strategy should lead to a high precision AR, but at the same time it can have a negative impact on the recall. In the OM context, it happens quite frequently that the authors comment on the entity they want to criticize in a series of arguments. In such argument chains, we try to solve cases of antecedent ambiguity by analyzing the opinions: If there are ambiguous antecedent candidates for a pronoun, we check whether there is an opinion uttered in the previous sentence. If this is the case and if the opinion target matches the pronoun regarding gender and number, we resolve the pronoun to the antecedent which was the previous opinion target. In the results of our experiments in Section 5, we will refer to the configurations using these extensions with the numbers attributed to them above. Experimental Work To integrate AR in the OM algorithm, we add the antecedents of the pronouns annotated as opinion targets to the target candidate list. Then we extract the dependency paths connecting pronouns and opinion words and add them to the list of valid paths. When we run the algorithm, we extract anaphora which were resolved, if they occur with a valid dependency path to an opinion word. In such a case, the anaphor is substituted for its antecedent and thus extracted as part of an opinion target -opinion word pair. To reproduce the system by Zhuang et al. (2006), we substitute the cast and crew list employed by them (see Section 3.2), with a NER component (Finkel et al., 2005). One aspect regarding the extraction of opinion target -opinion word pairs remains open in Zhuang et al. (2006): The dependency paths only identify connections between pairs of single words. However, almost 50% of the opinion target candidates are multiword expressions. Zhuang et al. (2006) do not explain how they extract multiword opinion targets with the dependency paths. In our experiments, we require a dependency path to be found to each word of a multiword target candidate for it to be extracted. Furthermore, Zhuang et al. (2006) do not state whether in their evaluation annotated multiword targets are treated as a single unit which needs to be extracted, or whether a partial matching is employed in such cases. We require all individual words of a multiword expression to be extracted by the algorithm. As mentioned above, the dependency path based approach will only identify connections between pairs of single words. We therefore employ a merging step, in which we combine adjacent opinion targets to a multiword expression. We have compiled two result sets: Table 3 shows the results of the overall OM in a five-fold cross-validation. Table 4 gives a detailed overview of the AR for opinion target identification summed up over all folds. In Table 4, a true positive refers to an extracted pronoun which was annotated as an opinion target and is resolved to the correct antecedent. A false positive subsumes two error classes: A pronoun which was not annotated as an opinion target but extracted as such, or a pronoun which is resolved to an incorrect antecedent. As shown in Table 3, the recall of our reimplementation is slightly higher than the recall reported in Zhuang et al. (2006). However, our precision and thus f-measure are lower. This can be attributed to the different document sets used in our experiments (see Section 3.1), or our substitution of the list of peoples' names with the NER component, or differences regarding the evaluation strategy as mentioned above. We observe that the MARS algorithm yields an improvement regarding recall compared to the baseline system. However, it also extracts a high number of false positives for both the personal and impersonal / demonstrative pronouns. This is due to the fact that the MARS algorithm is designed for robustness and always resolves a pronoun to an antecedent. CogNIAC in its off-the-shelf configuration already yields significant improvements over the baseline regarding f-measure 2 . Our CogNIAC extension [id] improves recall slightly in comparison to the off-the-shelf system. As shown in Table 4, the algorithm extracts impersonal and demonstrative pronouns with lower precision than personal pronouns. Our error analysis shows that this is mostly due to the Person / Location / Organization classification of the CogNIAC implementation. The names of actors and movies are thus often misclassified. Extension [1] mitigates this problem, since it increases precision (Table 3 row tary regarding the extraction of impersonal and demonstrative pronouns. This configuration yields statistically significant improvements regarding fmeasure over the off-the-shelf CogNIAC configuration, while also having the overall highest recall. Error Analysis When extracting opinions from movie reviews, we observe the same challenge as Turney (2002): The users often characterize events in the storyline or roles the characters play. These characterizations contain the same words which are also used to express opinions. Hence these combinations are frequently but falsely extracted as opinion target -opinion word pairs, negatively affecting the precision. The algorithm cannot distinguish them from opinions expressing the stance of the author. Overall, the recall of the baseline is rather low. This is due to the fact that the algorithm only learns a subset of the opinion words and opinion targets annotated in the training data. Currently, it cannot discover any new opinion words and targets. This could be addressed by integrating a component which identifies new opinion targets by calculating the relevance of a word in the corpus based on statistical measures. The AR introduces new sources of errors regarding the extraction of opinion targets: Errors in gender and number identification can lead to an incorrect selection of antecedent candidates. Even if the gender and number identification is correct, the algorithm might select an incorrect antecedent if there is more than one possible candidate. A non-robust algorithm as CogNIAC might leave a pronoun which is an actual opinion target unresolved, due to the ambiguity of its antecedent candidates. The upper bound for the OM with perfect AR on top of the baseline would be recall: 0.649, precision: 0.562, f-measure: 0.602. Our best configuration reaches ∼ 50% of the improvements which are theoretically possible with perfect AR. Conclusions We have shown that by extending an OM algorithm with AR for opinion target extraction significant improvements can be achieved. The rule based AR algorithm CogNIAC performs well regarding the extraction of opinion targets which are personal pronouns. The algorithm does not yield high precision when resolving impersonal and demonstrative pronouns. We present a set of extensions which address this challenge and in combination yield significant improvements over the off-the-shelf configuration. A robust AR algorithm does not yield any improvements regarding f-measure in the OM task. This type of algorithm creates many false positives, which are not filtered out by the dependency paths employed in the algorithm by Zhuang et al. (2006). AR could also be employed in other OM algorithms which aim at identifying opinion targets by means of a statistical analysis. Vicedo and Ferrández (2000) successfully modified the relevance ranking of terms in their documents by replacing anaphora with their antecedents. The approach can be taken for OM algorithms which select the opinion target candidates with a relevance ranking (Hu and Liu, 2004;. Table 1 : 1Dataset Statistics# Documents 1829 # Sentences 24918 # Tokens 273715 # Target + Opinion Pairs 5298 # Targets which are Pronouns 504 # Pronouns > 11000 Table 2 : 2Pronouns as Opinion Targetsit 274 he 58 she 22 they 22 this 77 his 26 her 10 him 15 6), while not affecting recall. The overall improvement of our extensions [id] + [1] is however not statistically significant in comparison to off-the-shelf CogNIAC. Our extensions [2] and [3] in combination with [id] each increase recall at the expense of precision. The improvement in f-measure of CogNIAC [id] + [3] over the off-the-shelf system is statistically significant. The best overall results regarding f-measure are reached if we combine all our extensions of the CogNIAC algorithm. The results of this configuration show that the positive effects of extensions [2] and [3] are complemen- Table 3 : 3Op. Target -Op. Word Pair Extraction Configuration Reca. Prec. F-Meas.Table 4: Results of AR for Opinion Targets personal, impersonal & demonstrative pronouns 2 true positives, false positivesResults in Zhuang et al. 0.548 0.654 0.596 Our Reimplementation 0.554 0.523 0.538 MARS off-the-shelf 0.595 0.467 0.523 CogNIAC off-the-shelf 0.586 0.534 0.559 * * CogNIAC+[id] 0.594 0.516 0.552 CogNIAC+[id]+[1] 0.594 0.533 0.561 CogNIAC+[id]+[2] 0.603 0.501 0.547 CogNIAC+[id]+[3] 0.613 0.521 0.563 * CogNIAC+[id]+[1]+[2]+[3] 0.614 0.531 0.569 * Algorithm Pers. 1 Imp. & Dem. 1 TP 2 FP 2 TP FP MARS off-the-shelf 102 164 115 623 CogNIAC off-the-shelf 117 95 0 0 CogNIAC+[id] 117 95 105 180 CogNIAC+[id]+[1] 117 41 105 51 CogNIAC+[id]+[2] 117 95 153 410 CogNIAC+[id]+[3] 131 103 182 206 CogNIAC+[id]+[1]+[2]+[3] 124 64 194 132 1 http://www.imdb.com (IMDB) Significance of improvements was tested using a paired two-tailed t-test and p ≤ 0.05 ( * ) and p ≤ 0.01 ( * * ) AcknowledgmentsThe project was funded by means of the German Federal Cogniac: High precision coreference with limited knowledge and linguistic resources. 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Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts

Despite the pervasiveness of clinical depression in modern society, professional help remains highly stigmatized, inaccessible, and expensive. Accurately diagnosing depression is difficult-requiring time-intensive interviews, assessments, and analysis. Hence, automated methods that can assess linguistic patterns in these interviews could help psychiatric professionals make faster, more informed decisions about diagnosis. We propose JLPC, a method that analyzes interview transcripts to identify depression while jointly categorizing interview prompts into latent categories. This latent categorization allows the model to identify high-level conversational contexts that influence patterns of language in depressed individuals. We show that the proposed model not only outperforms competitive baselines, but that its latent prompt categories provide psycholinguistic insights about depression.

Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts Association for Computational LinguisticsCopyright Association for Computational LinguisticsJuly 5 -10, 2020. 2020 Alex Rinaldi arinaldi@ucsc.edu Jean E Fox Tree Snigdha Chaturvedi snigdha@cs.unc.edu Department of Computer Science Department of Psychology UC Department of Computer Science UC Santa Cruz Santa Cruz Carolina at Chapel Hill University of North Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics the 58th Annual Meeting of the Association for Computational LinguisticsAssociation for Computational LinguisticsJuly 5 -10, 2020. 2020 Despite the pervasiveness of clinical depression in modern society, professional help remains highly stigmatized, inaccessible, and expensive. Accurately diagnosing depression is difficult-requiring time-intensive interviews, assessments, and analysis. Hence, automated methods that can assess linguistic patterns in these interviews could help psychiatric professionals make faster, more informed decisions about diagnosis. We propose JLPC, a method that analyzes interview transcripts to identify depression while jointly categorizing interview prompts into latent categories. This latent categorization allows the model to identify high-level conversational contexts that influence patterns of language in depressed individuals. We show that the proposed model not only outperforms competitive baselines, but that its latent prompt categories provide psycholinguistic insights about depression. Introduction Depression is a dangerous disease that effects many. A 2017 study by Weinberger et al. (2018) finds that one in five US adults experienced depression symptoms in their lifetime. Weinberger et al. also identify depression as a significant risk factor for suicidal behavior. Unfortunately, professional help for depression is not only stigmatized, but also expensive, timeconsuming and inaccessible to a large population. Lakhan et al. (2010) explain that there are no laboratory tests for diagnosing psychiatric disorders; instead these disorders must be identified through screening interviews of potential patients that require time-intensive analysis by medical experts. This has motivated developing automated depression detection systems that can provide confidential, inexpensive and timely preliminary triaging that can help individuals in seeking help from medical experts. Such systems can help psychiatric professionals by analyzing interviewees for predictive behavioral indicators that could serve as additional evidence . Language is a well-studied behavioral indicator for depression. Psycholinguistic studies by Segrin (1990), Rude et al. (2004), andAndreasen (1976) identify patterns of language in depressed individuals, such as focus on self and detachment from community. To capitalize on this source of information, recent work has proposed deep learning models that leverage linguistic features to identify depressed individuals (Mallol-Ragolta et al., 2019). Such deep learning models achieve high performance by uncovering complex, unobservable patterns in data at the cost of transparency. However, in the sensitive problem domain of diagnosing psychiatric disorders, a model should offer insight about its functionality in order for it to be useful as a clinical support tool. One way for a model to do this is utilizing the structure of the input (interview transcript) to identify patterns of conversational contexts that can help professionals in understanding how the model behaves in different contexts. A typical interview is structured as pairs of prompts and responses such that participant responses follow interviewer prompts (such as "How have you been feeling lately?"). Intuitively, each interviewer prompt serves as a context that informs how its response should be analyzed. For example, a short response like "yeah" could communicate agreement in response to a question such as "Are you happy you did that?", but the same response could signal taciturnity or withdrawal (indicators of depression) in response to an encouraging prompt like "Nice!". To enable such contextdependent analysis, the model should be able to group prompts based on the types of conversa-tional context they provide. To accomplish this, we propose a neural Joint Latent Prompt Categorization (JLPC) model that infers latent prompt categories. Depending on a prompt's category, the model has the flexibility to focus on different signals for depression in the corresponding response. This prompt categorization is learned jointly with the end task of depression prediction. Beyond improving prediction accuracy, the latent prompt categorization makes the proposed model more transparent and offers insight for expert analysis. To demonstrate this, we analyze learned prompt categories based on existing psycholinguistic research. We also test existing hypotheses about depressed language with respect to these prompt categories. This not only offers a window into the model's working, but also can be used to design better clinical support tools that analyze linguistic cues in light of the interviewer prompt context. Our key contributions are: • We propose an end-to-end, data-driven model for predicting depression from interview transcripts that leverages the contextual information provided by interviewer prompts • Our model jointly learns latent categorizations of prompts to aid prediction • We conduct robust experiments to show that our model outperforms competitive baselines • We analyze the model's behavior against existing psycholinguistic theory surrounding depressed language to demonstrate the interpretability of our model Joint Latent Prompt Categorization We propose a Joint Latent Prompt Categorization (JLPC) model that jointly learns to predict depression from interview transcripts while grouping interview prompts into latent categories. 1 . The general problem of classifying interview text is defined as follows: let X denote the set of N interview transcripts. Each interview X i is a sequence of j conversational turns consisting of interviewer's prompts and participant's responses: X i = {(P ij , R ij ) for j = {1...M i }, where M i is the number of turns in X i , P ij is the j th prompt in the i th interview, and R ij is the participant's re-1 Code and instructions for reproducing our results are available at https://github.com/alexwgr/ LatentPromptRelease sponse to that prompt. Together, (P ij , R ij ) form the j th turn in i th interview. Each interview X i is labeled with a ground-truth class Y i ∈ {1, ..C}, where C is the number of possible labels. In our case, there are two possible labels: depressed or not depressed. Our model, shown in Figure 1, takes as input an interview X i and outputs the predicted labelŶ i . Our approach assumes that prompts and responses are represented as embeddings P ij ∈ R E and R ij ∈ R E respectively. We hypothesize that prompts can be grouped into latent categories (K in number) such that corresponding responses will exhibit unique, useful patterns. To perform a soft assignment of prompts to categories, for each prompt, our model computes a category member- ship vector h ij = [h 1 ij , · · · , h K ij ]. It represents the probability distribution for the j th prompt of the i th interview over each of K latent categories. h ij is computed as a function φ of P ij and trainable parameters θ CI (illustrated as the Category Inference layer in Figure 1): h ij = φ(P ij , θ CI )(1) Based on these category memberships for each prompt, the model then analyzes the corresponding responses so that unique patterns can be learned for each category. Specifically, we form K category-aware response aggregations. Each of these aggregations,R k i ∈ R E , is a category-aware representation of all responses of the i th interview with respect to the k th category. R k i = 1 Z k i M i j=1 h k ij × R ij (2) Z k i = M i j=1 h k ij(3) where, h k ij is the k th scalar component of the latent category distribution vector h ij and Z k i is a normalizer added to prevent varying signal strength, which interferes with training. We then compute the output class probability vector y i as a function ψ of the response aggregations [R 1 i , · · · ,R K i ] and trainable parameters θ D (illustrated as the Decision Layer in Figure 1). y i = ψ(R 1 i , · · · ,R K i , θ D )(4) The predicted labelŶ i is selected as the class with the highest probability based on y i . Figure 1: The architecture of our JLPC model with K = 3. For each prompt P ij in interview i, the Category Inference layer computes a latent category membership vector, h ij . These are used as weights to form K separate Category-Aware Response Aggregations, which in turn are used by the Decision Layer to predict the output. The Category Inference Layer We compute the latent category membership for all prompts in interview i using a feed-forward layer with K outputs and softmax activation: φ(P ij , θ CI ) = σ(row j (P i W CI + B CI )) (5) As shown in Equation 1, φ(P ij , θ CI ) produces the desired category membership vector h ij over latent categories for the j th prompt of the i th interview. P i ∈ R M ×E is defined as [P i1 , · · · , P iM ] T , where M is the maximum conversation length in X i and P im = 0 E for all M i < m ≤ M . P i W CI + B CI computes a matrix where row j is a vector of energies for the latent category distribution for prompt j, and σ denotes the softmax function. W CI ∈ R E×K and B CI ∈ R K are the trainable parameters for this layer: θ CI = {W CI , B CI }. The Decision Layer The Decision Layer models the probabilities for each output class (depressed and not-depressed) using a feed-forward layer over the concatenation R i of response aggregations [R 1 i , · · · ,R K i ]. This allows each response aggregationR k i to contribute to the final classification through a separate set of trainable parameters. ψ(R 1 i , · · · ,R K i , θ D ) = σ(R T i W D + B D )(6) As shown in Equation 4, ψ(R 1 i , · · · ,R K i , θ D ) produces the output class probability vector y i . W D ∈ R (E * K)×C and B D ∈ R C are the trainable parameters for the decision layer: θ D = {W D , B D }. We then compute the cross entropy loss L(Y,Ŷ ) between ground truth labels and y i . Entropy regularization The model's learning goal as described above only allows the output prediction error to guide the separation of prompts into useful categories. However, in order to encourage the model to learn distinct categories, we employ entropy regularization (Grandvalet and Bengio, 2005) by penalizing overlap in the latent category distributions for prompts. That is, we compute the following entropy term using components of the category membership vector h ij from Equation 1: E(X i ) = 1 u i N i=1 M i j=1 E j (X i )(7) where, E j (X i ) = − K k=1 h k ij ln h k ij (8) u i = N i=1 M i(9) Finally, the model's overall learning goal minimizes entropy regularized cross entropy loss: arg min θ L(Y,Ŷ ) + λE(X i ) where, λ is a hyper-parameter that controls the strength of the entropy regularization term. Leveraging Prompt Representations in the Decision Layer While prompt representations are used to compute latent category assignments, the model described so far (JLPC) cannot directly leverage prompt features in the final classification. To provide this capability, we define two additional model variants with pre-aggregation and post-aggregation prompt injection: JLPCPre and JLPCPost, respectively. JLPCPre is similar to the JLPC model, except that it aggregates both prompt and response representations based on prompt categories. In other words, the aggregated response representation,R k i in Equation 2, is computed as: R k i = 1 Z k i M i j=1 h k ij [ P ij , R ij ] JLPCPost is also similar to JLPC except that it includes the average of prompt representations as additional input to the decision layer. That is, Equation 6 is modified to the following: ψ(R 1 i , · · · ,R K i , θ D ) = σ([P i ,R i ] T W D + B D )(10 ) P i is the uniformly-weighted average of prompt representations in X i . Dataset We evaluate our model on the Distress Analysis Interview Corpus (DAIC) . DAIC consists of text transcripts of interviews designed to emulate a clinical assessment for depression. The interviews are conducted between human participants and a human-controlled digital avatar. Each interview is labeled with a binary depression rating based on a score threshold for the 9th revision of the Patient Health Questionnaire (PHQ-9). In total, there are 170 interviews, with 49 participants identified as depressed. To achieve stable and robust results given the small size of the DAIC dataset, we report performance over 10 separate splits of the dataset into training, validation, and test sets. For each split, 70% is used as training data, and 20% of the training data is set aside as validation data. Preprocessing and Representation DAIC interview transcripts are split into utterances based on pauses in speech and speaker change, so we concatenate adjacent utterances by the same speaker to achieve a prompt-response structure. We experiment with two types of continuous representations for prompts and responses: averaged word embeddings from the pretrained GloVe model (Pennington et al., 2014), and sentence embeddings from the pretrained BERT model (Devlin et al., 2019). Further details are given in Appendix A.1. Reported results use GloVe embeddings because they led to better validation scores. Exclusion of Predictive Prompts Our preliminary experiments showed that it is possible to achieve better-than-random performance on the depression identification task using only the set of prompts (excluding the responses). This is possibly because the interviewer identified some individuals as potentially depressed during the interview, resulting in predictive follow-up prompts (for example, "How long ago were you diagnosed?"). To address this, we iteratively remove predictive prompts until the development performance using prompts alone is not significantly better than random (see Appendix A.3). This is to ensure our experiments evaluate the content of prompts and responses rather than fitting to any bias in question selection by the DAIC corpus interviewers, and so are generalizable to other interview scenarios, including future fully-automated ones. Experiments We now describe our experiments and analysis. Baselines Our experiments use the following baselines: • The RO baseline only has access to responses. It applies a dense layer to the average of response representations for an interview. • The PO baseline only has access to prompts, following the same architecture as RO. • The PR baseline has access to both prompts and responses. It applies a dense layer to the average of prompt and response concatenations. Standard deviation is reported in parentheses. Two of the proposed models, JLPC and JLPCPost, improve over baselines including the BERT fine-tuned model (Devlin et al., 2019), with the JLPCPost achieving a statistically significant improvement (p < 0.05). • BERT refers to the BERT model (Devlin et al., 2019) fine-tuned on our dataset (see Appendix A.2). Training details All models are trained using the Adam optimizer. We use mean validation performance to select hyper-parameter values: number of epochs = 1300, learning rate = 5 × 10 −4 , number of prompt categories K = 11 and entropy regularization strength λ = 0.1. Quantitative Results We computed the F1 scores of the positive (depressed) and negative (not-depressed) classes averaged over the 10 test sets. Given the class imbalance in the DAIC dataset, we compare models using F1 score for the depressed class. As an additional baseline, we also implemented methods from Mallol-Ragolta et al. (2019) but do not report their performance since their model performs very poorly (close to random) when we consider averaged performance over 10 test sets. This is likely because of the large number of parameters required by the hierarchical attention model. Table 1 summarizes our results. The belowrandom performance of the PO baseline is expected, since the prompts indicative of depression were removed as described in Section 3.2. This indicates the remaining prompts, by themselves, are not sufficient to accurately classify interviews. The RO model performs better, indicating the response information is more useful. The PR baseline improves over the RO baseline indicating that Figure 2: Ablation study on validation set demonstrating the importance of prompt categorization and entropy regularization for our model. the combination of prompt and response information is informative. The BERT model, which also has access to prompts and responses, shows a reasonable improvement over all baselines. JLPC and JLPCPost outperform the baselines, with JLPCPost achieving a statistically significant improvement over both the PR and BERT baselines (p < 0.05). 2 This indicates the utility of our prompt-category aware analysis of the interviews. Ablation study We analyzed how the prompt categorization and entropy regularization contribute to our model's validation performance. The contributions of each component are visualized in Figure 2. Our analysis shows that while both components are important, latent prompt categorization yields the highest contribution to the model's performance. Analyzing Prompt Categories Beyond improving classification performance, the latent categorization of prompts yields insight about conversational contexts relevant for analyzing language patterns in depressed individuals. To explore the learned categories, we isolate interviews from the complete corpus that are correctly labeled by our best-performing model. We say that the model "assigns" an interview prompt to a given category if the prompt's membership for that category (Equation 1) is stronger than for other categories. We now describe the various prompts assigned to different categories. 3 Firstly, all prompts that are questions like "Tell me more about that", "When was the last time you had an argument?", etc. are grouped together into a single category, which we refer to as the Starters category. Previous work has identified usefulness of such questions as conversation starters since they assist in creating a sense of closeness (Mcallister et al., 2004;Heritage and Robinson, 2006). Secondly, there are several categories reserved exclusively for certain backchannels. Backchannels are short utterances that punctuate longer turns by another conversational participant (Yngve, 1970;Goodwin, 1986;Bavelas et al., 2000). Specifically, the model assigns the backchannels "mhm," "mm," "nice," and "awesome" each to separate categories. Research shows that it is indeed useful to consider the effects different types of backchannels separately. For example, Bavelas et al. (2000) propose a distinction between specific backchannels (such as "nice" and "awesome") and generic backchannels (such as "mm" and "mhm"), and Tolins and Fox Tree (2014) demonstrated that each backchannel type serves a different purpose in conversation. Thirdly, apart from starters and backchannels, the model isolates one specific prompt -"Have you been diagnosed with depression?" 4 into a separate category. Clearly, this is an important prompt and it is encouraging to see that the model isolates it as useful. Interestingly, the model assigns the backchannel "aw" to the same category as "Have you been diagnosed with depression?" suggesting that responses to both prompts yield similar signals for depression. Lastly, the remaining five categories are empty -no prompt in the corpus has maximum salience with any of them. A likely explanation for this observation stems from the choice of normalizing factor Z k i in Equation 3: it causesR k i to regress to the unweighted average of response embeddings when all prompts in an interview have low salience with category k. Repeated empty categories then function as an "ensemble model" for the average response embeddings, potentially improving predictive performance. Category-based Analysis of Responses The prompt categories inferred by our JLPCPost model enable us to take a data-driven approach to investigating the following category-specific psycholinguistic hypotheses about depression: Table 2: Indicators for social skills: mean response length (RL) and discourse marker/filler rates (DMF) for responses to prompts in starters and backchannel (collectively representing "mhm", "mm", "nice", and "awesome") categories, for depressed (D) and notdepressed (ND) participants. Statistically significant differences are underlined (p < 0.05). Both measures are significantly lower for the depressed class for responses to starters, but not to backchannels. H1 Depression correlates with social skill deficits (Segrin, 1990) H2 Depressed language is vague and qualified (Andreasen, 1976) H3 Depressed language is self-focused and detached from community (Rude et al., 2004) For hypothesis H1, we evaluate measures of social skill in responses to different categories of prompts. While research in psychology uses several visual, linguistic and paralinguistic indicators of social skills, in this paper we focus on two indicators that are measurable in our data: average response length in tokens and the rate of spoken-language fillers and discourse markers usage. 5 The first measure -response length -can be seen as a basic measure of taciturnity. The second measure -usage of fillers and discourse markers -can be used as proxy for conversational skills, since speakers use these terms to manage conversations (Fox Tree, 2010). Christenfeld (1995) and Lake et al. (2011) also find that discourse marker usage correlates with social skill. Following is the list of fillers and discourse markers: "um", "uh", "you know", "well", "oh", "so", "I mean", and "like". Table 2 shows the values of these measures for social skill for responses to backchannels and starters categories. We found that both measures were significantly lower for responses to starters-category prompts for depressed participants as opposed to not-depressed participants (p < 0.05). However, the measures showed no significant difference between depressed and notdepressed individuals for responses to categories representing backchannels ("mhm," "mm," "awesome," and "nice"). Note that a conversation usually begins with prompts from the starters category and thereafter backchannels are used to encourage the speaker to continue speaking (Goodwin, 1986). Given this, our results suggest that depressed individuals in the given population indeed initially demonstrate poorer social skills than notdepressed individuals, but the effect levels off as the interviewer encourages them to keep speaking using backchannels. Given this, our results suggest that depressed individuals in the given population indeed initially demonstrate poorer social skills than not depressed individuals, but the effect stops being visible as the conversation continues, either because the depressed individuals become more comfortable talking or because the interviewers' encouragement through backchannels elicits more contributions. Hypotheses H2 and H3 -regarding qualified language and self-focus, respectively -involve semantic qualities of depressed language. To explore these hypotheses, we use a reverse engineering approach to determine salient words for depression in responses to each prompt category. We describe this reverse engineering approach as follows: since the aggregated representation of an individual's responses in a category (R k i computed in Equation 2) resides in the same vector space as individual word embeddings, we can identify words in our corpus that produce the strongest (positive) signal for depression in various categories. 6 We refer to these as signal words. Signal words are ranked not by their frequency in the dataset, but by their predictive potentialthe strength of association between the word's semantic representation and a given category. We evaluate hypotheses H2 and H3 by observing semantic similarities between these signal words and the language themes identified by the hypotheses. Selections from the top 10 signal words for depression associated with categories corresponding to starters, specific backchannels, and generic backchannels are shown in Figure 3. Figure 3 shows hypothesis H2 is supported by signal words in responses to generic backchannels; words such as "theoretical" and "plausible" constitute qualified language, and in the context of generic backchannels, the proposed model identifies them as predictive of depression. Similarly, hypothesis H3 is also supported in responses to generic backchannels. The model identifies words related to community ("kids," "neighborhood," "we") as strong negative signals for depression, supporting that depressed language reflects detachment from community. However, the model only focuses on these semantic themes in responses to generic backchannel categories. As we found in our evaluation of hypothesis H1, the model localizes cues for depression to specific contexts. Signal words for depression in responses to the starters category are more reflective of our findings for hypothesis H1: the model focuses on short, low-semantic-content words that could indicate social skill deficit. For example, Figure 3 shows we identified "wow" as a signal word for the starters category. In one example from the corpus, a depressed participant uses "wow" to express uncomfortability with an emotional question: the interviewer asks, "Tell me about the last time you were really happy," and the interviewee responds, "wow (laughter) um." For responses to specific backchannels, strong signal words reflect themes of goals and desires ("wished," "mission," "accomplished"). Psychologists have observed a correlation between depression and goal commitment and pursuit (Vergara and Roberts, 2011;Klossek, 2015), and our finding indicates that depressed individuals discuss goal-related themes as response to specific backchannels. Overall, our model's design not only helps in reducing its opacity but also informs psycholinguistic analysis, making it more useful as part of an informed decision-making process. Our analysis indicates that even though research has shown strong correlation between depression and various interpersonal factors such as social skills, self-focus and usage of qualified language, clinical support tools should focus on these factors in light of conversational cues. Sources of Error In this section, we analyze major sources of error. We apply a similar reverse engineering method as in Section 4.6. For prompts in each category, we consider corresponding responses that result in strong incorrect signals (false positive or false negative) based on the category's weights in the decision layer. We focus on the categories with the most significance presence in the dataset: the categories corresponding to starters, the "mhm" backchannel, and the prompt "Have you been diagnosed with depression?". For the starters category, false positive-signal responses tend to contain a high presence of fillers and discourse markers ("uh," "huh," "post mm traumatic stress uh no uh uh," "hmm"). It is possible that because the model learned to focus on short, low-semantic-content responses, it incorrectly correlates presence of fillers and discourse markers with depression. For the "mhm" category, we identified several false negatives, in which the responses included concrete words like "uh nice environment", "I love the landscape", and "I love the waters". Since the "mhm" category focuses on vague, qualified language to predict depression (see Figure 3), the presence of concrete words in these responses could have misled the model. For the "Have you been diagnosed with depression?" category, the misclassified interviews contained short responses to this prompt like "so," "never," "yes," "yeah," and "no," as well as statements containing the word "depression." For this category, the model seems to incorrectly correlate short re-sponses and direct mentions of depression with the depressed class. Related Work Much work exists at the intersection of natural language processing (NLP), psycholinguistics, and clinical psychology. For example, exploring correlations between counselor-patient interaction dynamics and counseling outcomes (Althoff et al., 2016); studying linguistic development of mental healthcare counsellors (Zhang et al., 2019); identifying differences in how people disclose mental illnesses across gender and culture (De Choudhury et al., 2017); predicting a variety of mental health conditions from social media posts (Sekulic and Strube, 2019;De Choudhury et al., 2013a;Guntuku et al., 2019;Coppersmith et al., 2014); and analyzing well-being (Smith et al., 2016) and distress (Buechel et al., 2018). Specifically, many researchers have used NLP methods for identifying depression (Morales et al., 2017). They focus on for predicting depression from Twitter posts (Resnik et al., 2015;De Choudhury et al., 2013b;Jamil et al., 2017), Facebook updates (Schwartz et al., 2014), student essays (Resnik et al., 2013), etc. Previous works have also focused on predicting depression severity from screening interview data Sun et al., 2017;Pampouchidou et al., 2016). Unlike ours, these approaches rely on audio, visual, and text input. More recent approaches are based on deep learning. Yang et al. (2017) propose a CNNbased model leveraging jointly trained paragraph vectorizations, Al Hanai et al. (2018) propose an LSTM-based model fusing audio features with Doc2Vec representations of response text, Makiuchi et al. (2019) combine LSTM andCNN components, andMallol-Ragolta et al. (2019) propose a model that uses a hierarchical attention mechanism. However, these approaches are more opaque and difficult to interpret. Other approaches are similar to ours in the sense that they utilize the structure provided by interview prompts. Al Hanai et al. (2018) and Gong and Poellabauer (2017) propose models that extract separate sets of features for responses to each unique prompt in their corpus. However, these approaches require manually identifying unique prompts. Our model can instead automatically learn new, task-specific categorization of prompts. Lubis et al. (2018) perform a K-means clustering of prompt to assign prompts to latent dialogue act categories. These are used as features in a neural dialogue system. Our approach expands upon this idea of incorporating a separate unsupervised clustering step by allowing the learning goal to influence the clustering. Our approach is also related to that of Chaturvedi et al. (2014) in that it automatically categorizes various parts of the conversation. However, they use domain-specific handcrafted features and discrete latent variables for this categorization. Our approach instead can leverage the neural architecture to automatically identify features useful for this categorization. To the best of our knowledge, our approach is the first deep learning approach that jointly categorizes prompts to learn context-dependent patterns in responses. Conclusion This paper addressed the problem of identifying depression from interview transcripts. The proposed model analyzes the participant's responses in light of various categories of prompts provided by the interviewer. The model jointly learns these prompt categories while identifying depression. We show that the model outperforms competitive baselines and we use the prompt categorization to investigate various psycholinguistic hypotheses. Depression prediction is a difficult task which requires especially trained experts to conduct interviews and do their detailed analysis (Lakhan et al., 2010). While the absolute performance of our model is low for immediate practical deployment, it improves upon existing methods and at the same time, unlike modern methods, provides insight about the model's workflow. For example, our findings show how language of depressed individuals changes when interviewers use backchannels to encourage continued speech. We hope that this combination will encourage the research community to make more progress in this direction. Future work can further investigate temporal patterns in how language used by depressed people evolves over the course of an interaction. A Appendices A.1 Continuous representation of utterances For continuous representation using the GloVe model, we use the pretrained 100-dimensional embeddings (Pennington et al., 2014). The representation of an utterance is computed as the average of embeddings for words in the utterance, with 0 100 used to represent words not in the pretrained vocabulary. Based on the pretrained vocabulary, contractions (e.g. "can't") are decomposed. For continuous representation with the BERT model, utterances are split into sequences of sub-word tokens following the authors' specifications (Devlin et al., 2019), and the pretrained BERT (Base, Uncased) model computes a 768dimensional position-dependent representation. A.2 Training the BERT Model For the BERT model, all interviews were truncated to fit the maximum sequence length of the pretrained BERT model (Base, Uncased): 512 subword tokens. Truncation occurs by alternating between removing prompt and response tokens until the interview length in tokens is adequate. Devlin et al. (2019) suggest trying a limited number of combinations of learning rate and training epochs to optimize the BERT classification model. Specifically, the paper recommends combinations of 2, 3, or 4 epochs and learning rates of 2E-5, 3E-5, and 5E-5. We noted that validation and test scores were surprisingly low (significantly below random) using these combinations, and posited that the small number of suggested epochs could have resulted from the authors only evaluating BERT on certain types of datasets. Accordingly, we evaluated up to 50 epochs with the suggested learning rates and selected a learning rate of 2E-5 with 15 epochs based on validation results. A.3 Exclusion of prompts The goal of removing prompts is to prevent a classifier from identifying participants as depressed based on certain prompts simply being present in the interview, such as "How long ago were you diagnosed [with depression]?" While some prompts are clear indicators, early tests showed that even with these prompts removed, other prompts were predictors for the participant being depressed for no obvious reason, indicating a bias in the design in the interview. Rather than using arbitrary means to determine whether prompts could be predictive, we used a machine-learning based algorithm to identify and remove predictive prompts from interviews. After the division of interviews into turns as described in Section 3.1, we extracted the set of distinct prompts P distinct from all interviews (with no additional preprocessing). We then iteratively performed 10 logistic regression experiments using the same set of splits described in Section 4.2. In a given experiment, each interview was represented as an indicator vector with |P distinct | di-mensions, such that position p is set to 1 if prompt p ∈ {1, · · · , |P distinct |} is present in the interview, and 0 otherwise. Logistic Regression was optimized on the vector representations for the training interviews. The predicted F1 score for the depressed class on the validation set was recorded for each experiment. The average weight vector for the 10 Logistic regression models was computed. The prompt corresponding to the highest weight was removed from P distinct and added to a separate set D of predictive prompts. The process was repeated until the mean validation F1 score was less than the random baseline for the dataset (see Section 4.3). The final set of 31 prompts D had to be removed from the dataset before the baselines and proposed approaches could be evaluated. The design of the DAIC interview posed a challenge, however: the same prompt can appear in many interviews, but preceded by unique interjections by the interviewer, such as "mhm," "nice," and "I see". We refer to this interjections as "prefixes." We manually compiled a list of 37 prefixes that commonly reoccur in interviews. For all interviews, if a prompt from P distinct occurred in the interview after prefixes were ignored, then both the prompt and its corresponding response were removed from the interview before training. This resulted in an removing an average of 13.64 turns from each interview in the dataset. Figure 3 : 3Signal words associated with language in depressed individuals. Columns represent various types of prompts (Starters, Generic Backchannels and Specific Backchannels). The bottom half shows ranked lists of signal words from the responses. Blue words are strongly indicative and red words are least indicative of depression. Statistical significance is calculated from the test prediction using two-sided T-test for independent samples of scores 3 To verify consistency of prompt categorization, we rerun the model with multiple initialization and they all yielded the same general trends as described in the paper. Note that this prompt was not removed in Section 3.2 since by itself, the prompt's presence is not predictive of depression (without considering the response). We compute this measure as the ratio of discourse marker and filler occurrences to number of tokens, averaged over responses. A word's signal strength is computed for a given category k by taking the dot product of the word's embedding with the weights in the decision layer corresponding to category k. Large positive numbers correspond to positive predictions and vice versa. Since the Decision Layer is a dot product with all response aggregations, it is intuitive to compute prediction strength for a group of categories by adding together prediction strengths from individual groups. Detecting Depression with Audio/Text Sequence Modeling of Interviews. Al Tuka, Mohammad Hanai, James Ghassemi, Glass, 10.21437/Interspeech.2018-252219th Annual Conference of the International Speech Communication Association. 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Prediction of a Movie's Success From Plot Summaries Using Deep Learning Models August 1 Jin Kim Department of Applied Data Science College of Computing Sungkyunkwan University Suwon-si South Korea Jung-Hoon Lee College of Computing Sungkyunkwan University Suwon-si South Korea Yun-Gyung Cheong Sungkyunkwan University Suwon-si South Korea Prediction of a Movie's Success From Plot Summaries Using Deep Learning Models Proceedings of the Second Storytelling Workshop the Second Storytelling WorkshopFlorence, ItalyAugust 1127 As the size of investment for movie production grows bigger, the need for predicting a movie's success in early stages has increased. To address this need, various approaches have been proposed, mostly relying on movie reviews, trailer movie clips, and SNS postings. However, all of these are available only after a movie is produced and released. To enable a more earlier prediction of a movie's performance, we propose a deep-learning based approach to predict the success of a movie using only its plot summary text. This paper reports the results evaluating the efficacy of the proposed method and concludes with discussions and future work. Introduction Movie industry is a huge sector within the entertainment industry. The global movie box office revenue is predicted to reach nearly 50 billion U.S dollars in 2020 (Sachdev et al., 2018). With huge capital investments, the movie business is a high-risk venture (De Vany and Walls, 1999). Therefore, an early prediction of a movie's success can make a great contribution to the film industry, when post-production factors are unknown before the film's release. This task is extremely challenging, as the success of the movie should be determined based on the scenario or plot of the movie without using the post-production drivers such as actor, actress, director, MPAA rating and etc. To address this issue, our work attempts to predict a movie's success from its textual summary. We used the CMU Movie Summary Corpus 1 , which contains crowd-sourced summaries from the real users. The success of a movie is assessed with the review scores of Rotten Tomatoes 2 , an American review-aggregation website for film and television. The scoring system utilizes two scores: the tomato-meter and the audience score. The tomato-meter score is estimated by hundreds of film and television critics, appraising the artistic quality of a movie. The audience score is computed by the collective scores from regular movie viewers. In this paper we present a deep-learning based approach to classify a movie popularity and quality labels using the movie textual summary data. The primary hypothesis that we attempted to answer is to predict a movie's success in terms of popularity and artistic quality by analyzing only the textual plot summary. The contributions of our research are as follows: • To prepare a data set to define a movie's success • To incorporate sentiment score in predicting a movie's success • To evaluate the efficacy of ELMO embedding in predicting a movie's success • To evaluate merged deep learning models (CNN and residual LSTM) in predicting a movie's success 2 Our Approach Figure 1 illustrates the system architecture that classifies an input text as successful or nonsuccessful based on the critics score and the audience score. The pre-processing step tokenizes the summary text into sentences. Then, the list of sentences are given to the ELMO embedding and the sentiment score extraction modules. The ELMO embedding module converts the sentences into word vectors. The sentiment score extractor generates a sentiment score that combines the positive and negative sentiment score of each sentence. Lastly, the two outputs are merged to classify a movie summary into the success or non-success classes. Data To evaluate our approach, we used the CMU Movie Summary Corpus (Bamman et al., 2013), which contains crowd-sourced summaries from the real users. The corpus contains 42,306 movie plot summaries and their metadata such as genre, release date, cast, character traits, etc. However, we use only the plot summary text feature and the genre. The following example summary which consists of 36 sentences and 660 words, shows a part of the plot summary of 'The Avengers' (released in 2012) directed by Joss Whedo. The Asgardian Loki encounters the Other,the leader of an extraterrestrial race known as the Chitauri. In exchange for retrieving the Tesseract, a powerful energy source of unknown potential, ... In the first of two post-credits scenes, the Other confers with his master about the attack on Earth and humanity's resistance; in the second, the Avengers eat in silence at a shawarma restaurant. We created the classification labels based on the Rotten tomato scores that we crawled from Rotten Tomatoes' website with the Selenium 3 and Beautiful Soup python packages (Richardson, 2013). These scores serve as a credible indicator of a movie's success (Doshi et al., 2010). We classify movies following the Rotten Tomato rule; if the review score is greater than 75, the corresponding movie is classified fresh (1); if its score is less than 60, the movie is classified not fresh (0). As some movies do not have both the audience and the critics score, we collected 20,842 and 12,019 movie plot summary data for the audience score and for the critic score respectively. The audience score is assessed by ordinary people, we regard the class 1 as representing 'popular' movies and the class 0 as representing 'not popular' movies. Likewise, since the critics score is assessed by professionals in the industry, we consider class 1 as representing 'wellmade' movies and class 0 as representing 'not well-made' movies. Since these scores indicate the popularity and quality of a movie, we define a successful movie as having the combination of these score greater than 75. Finally, we prepared the third data set considering both of the audience and the critics scores. We define movies with each audience and critics score greater than 75 as 'successful' and less than 60 as 'not successful'. There are two reasons that the number of instances in the prepared data is less than the number of summaries in the CMU Movie summary corpus. First, movies that have received review scores above 60 and below 75 are filtered out. Second, some movies in the CMU Movie summary corpus have no scores at the Rotten Tomato site. Table 1 shows the statistics of the data set. The ratio between class 1 and 0 is approximately 6:4 for the audience score and 5:5 for the critics score and the combination of both scores. The data sets were also divided into different genres, to test whether the genre of a movie has an impact on the prediction of a performance. The table shows the ratios between class 1 and 0 are balanced except for the thriller and comedy genres in the audience score. Since each movie is tagged with multiple genres, the sum of all the number of summaries of each genre is greater than the total number of summaries. A simple statistical analysis shows that the maximum number of sentences in the longest summary in the train set is 198, the minimum is 1, and the average is 18.3. The number of words in the largest summary is 4,264, while that of the shortest summary is 10. The average is 361.2 words. ELMO embedding When the list of sentences representing a movie summary is given as input, the module creates its corresponding word embedding vectors. Traditional word embedding schemes such as Word2vec (Mikolov et al., 2013) and Glove (Pennington et al., 2014) produce a fixed vector for each word. While those embedding methods have been shown effective in many NLP applications, they do not deal with words which mean differently as their contexts vary such as homophones. Thus, We applied a contextualized embedding method that can generate different word vectors depending on the context. ELMO (Peters et al., 2018) is a popular contextualized embedding method, which uses two bidirectional LSTM networks for constructing the vector. In this work, we utilized the TensorFlow Hub implementation 4 to represent the word vector. We then fine-tuned the weight for ELMO embedding to gain better performance for the classification task (Perone et al., 2018). Since the length of the summary varies, we need to set a maximum number of sentences in a summary. We set the maximum number at 198, as it is the number of sentences in the longest summary found in the train set. Sentiment score extraction To extract the sentiment score of each sentence, we applied the NLTK's Vader sentiment analyzer (Hutto and Gilbert, 2014) to each sentence. Figure 2 illustrates a part of the sentiment vector representation of the movie 'The Avengers'. A summary is represented as a 198 dimensional vector, where each denotes the sentiment score of a single sentence. A summary shorter than 198 sentences is zero-padded. The highlight of the story (i.e., the conflict and resolution stages) is usually located towards the end of the story. So, we reversed the order as the vector is given as input to the LSTM deep learning model in the next stage which better remember the recent input. The VADER(Valence Aware Dictionary for sentiment Reasoning) module computes four scores for each sentence: negative, positive, neutral, and compound scores. In this research, we use the compound score ranging from -1 (most negative) to 1 (most positive). Figure 3: Sentiment flow graphs of successful movies. X axis denotes the sentence index, and the Y axis denotes the sentiment score of a sentence normalized between -1 and 1. Figure 4: Sentiment flow graphs of unsuccessful movies. X axis denotes the sentence index, and the Y axis denotes the sentiment score of a sentence normalized between -1 and 1. Figure 3 and Figure 4 depict the sentiment plots of successful movies and unsuccessful movies respectively. The 4 graphs shown in Figure 3 exhibit various patterns of successful movies' sentiment flows. The movie Alice in Wonderland begins and ends positively. On the other hand, the movies Das Boot and A Man for All Seasons begin and end with negatively. The movie Gettysburg shows the reversal of fortune pattern which begins negatively and ends positively. It is commonly noted that these successful movies have frequence sentiment fluctuations. On the other hand, the graphs in Figure 4 illustrate unsuccessful movies' sentiment flows, which exhibit less frequent sentiment fluctuations. Both the movie The Limits of Control and The Lost Bladesman have negative beginning and ending. The movie Tai-Pan begins negatively and ends positively. The movie Bluetproof Monk begins and ends positively, however, its majority sentiment scores are negative while the story is being developed. Therefore, it suggests that the frequency of sentiment changes may signal the success of films. Yet, the polarity of sentiment have a little impact on predicting a movie's success. Classification Models We built an ELMO, a merged 1D CNN (Figure 5), and a merged residual LSTM (Figure 6) networks. We establish our baseline by calculating a majority class baseline for comparison. First, we use deep contextualized word representations created by the ELMO embedding. This network consists of a character embedding layer, a convolutional layer, two highway networks, and two LSTM layers. Each token is converted to a character embedding representation, which is fed to a convolutional layer. Then, it goes through two highway networks to help the deep learning network training. Then, the output is fed to the LSTM layer as input data. The weights of each LSTM hidden layer are combined to generate the ELMO embedding. Finally, a 1024 dimensional ELMO embedding vector is constructed for each sentence, which is put into the 256 dimensional dense network. RELU (Nair and Hinton, 2010) is used as its activation function. Figure 5 shows the 1D CNN merged network, where the sentiment score vector is given as input to the CNN network. The model consists of two 1D convolutional layers, with 64-size filters and 3-size kernels. The second CNN layer includes a dropout layer. The next max-pooling layer reduces the learned features to 1/4 of their size. The final flatten layer constructs a single 100-dimensional vector. Then, the output from the ELMO embedding and the output from the CNN model is concatenated and given to the last 1-dense classification layer. Figure 6 employs two bidirectional LSTM layers which have 128 memory units. The outputs of these layers are added and flattened to create a 50,688 dimensional vector. 50,688 was obtained as the length of the sentences (198) times the size of the vector (256). Then, the next 128 dense layer reduces the vector for the final binary classification. We employed the binary cross-entropy as the loss function and the Adam optimizer. Evaluation Results We evaluated the classification performance of our approach for the audience score and for the critics score. We also inspected the performance based on the movie genre. We report the performance in terms of recall, precision, and F1 scores. Table 2 shows the performance result for the audience score. We use the F1 score as the primary metric for comparison as it is the harmonic means of recall and precision. Overall, the classification performance of 'not popular ' movies better than that of 'popular ' ones. The CNN model performed best in 'all genre ' with F1 of 0.79, which is 0.17 higher than the majority class baseline (F1 of 0.62). The ELMO model outperformed best in the genres of drama, thriller, comedy, and romance. On the contrary, the ELMO model had the highest performance for 'popular' at 0.58 and 0.62 in overall and romance genre respectively, while LSTM and CNN had the highest performance in the rest of the genre Table 3 summarizes the evaluation results for the critics score. The Results For all the genres, the deep learning models outperform the majority class baseline (F1 score=0.51) for predicting 'well-made ' movies producing its highest F1 of 0.70. The CNN model achieved the highest F1 score of 0.72 in predicting 'well-made' drama movies when its majority class baseline performance is 0.58. In the thriller, the CNN model also outperformed the baseline (F1 score=0.52) producing an F1 score of 0.75. The LSTM model achieved the best performance in predicting 'not well-made' movies, and yet the score is low-0.65. Inspection of the genre-specific F1 score shows that the best performance was obtained from CNN model when predicting 'not well-made' movies for the comedy genre (F1 score of 0.77). Finally, Table 4 shows the results when our approach is applied to the combined score. The ELMO embedding model outperforms the majority class baseline and the other models, achieving F1 scores of 0.68 and 0.73 when predicting 'successful' and 'not successful' movies respectively. Discussions Overall, the results suggest that the merged deep learning models proposed in this paper outperform the majority class baseline. For the audience score, the performance results of predicting 'not popular' movies outperform that of predicting 'popular' movies. This may suggest that using the textual summary only is limited in predicting 'popular' movies. When inspecting the results genre-wise, the precision of predicting 'not popular' movies for the thriller and the comedy genres yields the best performance when the LSTM model is used along with the sentiment score. On the other hand, the ELMO model out-performs the merged deep learning models that employ the sentiment score in predicting 'popular' movies with significant difference. The CNN model produces a F1 score higher than ELMo does in the thrillers and comedy genres and in the drama genre for 'popular' movies. In case of the critics score, the overall performance was inferior to that of the audience score. Inspection of the F1 score of each genre shows that predicting 'not well-made' movies in the thriller and the comedy genre achieved the best performance (0.75 and 0.77 respectively) when the CNN model was used along with the sentiment score. Generally, the CNN or LSTM models have shown F1 scores higher than the ELMO models at predicting well-made movies using the critics score except the drama genre. Then, employing the ELMO model outperforms other models that used the sentiment score as well. This may suggest that words are the primary determiner of predicting a movie' success. The research work by Eliashberg et al. Eliashberg et al. (2007) is most similar to our work. Their evaluation achieved the F1 score of 0.5 (recomputed from the evaluation metrics reported) in predicting a movie's success using the CART (Bootstrap Aggregated Classification and Regression Tree) model and the movie spoiler text which is 4-20 pages long. Although our result appear to be superior to their work in terms of yielding higher F1 score, it is not directly comparable since the data sets and the evaluation metrics are different. Related work The prediction of movie box office results has been actively researched (Rhee and Zulkernine, 2016; Eliashberg et al., 2007Eliashberg et al., , 2010Eliashberg et al., , 2014Sharda and Delen, 2006;Zhang et al., 2009;Du et al., 2014). Most researches predict a movie's success using various factors such as SNS data, cost, critics ratings, genre, distributor, release season, and the main actors award history, etc (Mestyán et al., 2013;Rhee and Zulkernine, 2016;Jaiswal and Sharma, 2017). This means that the prediction is made in the later stages of movie production, when the movie has already been produced and released. The evaluation carried out in (Jaiswal and Sharma, 2017) achieved the highest performance with F1 score of 0.79, which is recomputed from the evaluation metrics reported. However, this performance is not directly comparable to our result, since their work employed a small data set which consists of 557 movies and was based on a different genre (i.e., Bollywood movie). Their work employs rich feature such as YouTube statistics, lead actor, actress and director ratings, critics reviews, which are mostly available only after the movie is produced. Therefore, movie distributors and investors cannot rely on this approach when they need to make an investment decision. To overcome this problem, our approach relies on only the plot summary, which can assist the investors in making their invest decisions in the very early stages when they only have the written movie script. Conclusions In this paper, we propose a deep learning based approach utilizing the ELMO embedding and sentiment scores of sentences for predicting the success of a movie, based only on a textual summary of the movie plot. To test the efficacy of our approach, we prepared our evaluation data sets: movie plot summaries gathered from the CMU Movie Summary Corpus and their review scores from a movie review website. Since these plot summaries were obtained from Wikipedia, where the data are crowd sourced voluntarily. Hence, some movie summaries may have been written by people who like or value the movie. This may complicate our task to predict the movie's success only from the summary. We built three deep learning models: an ELMO embedding and two merged deep learning models (a merged 1D CNN network and a merged residual bidirectional LSTM network). The evaluation results show that our deep learning models outperform the majority class baseline. For the combination of the audience and the critics scores, the majority class baseline is F1 of 0.53 for 'not successful' , and 0 for 'successful '. Our best model obtained the highest F1 score of 0.68 for predicting 'successful' movies and that of 0.70 for predicting 'not successful' movies were obtained. Considering that only textual summaries of the movie plot are used for the predictions, the study results are promising. Forecasting the popularity and success of movies only with their textual descriptions of the plot, will aid the decision-making in funding movie productions. It seems that predicting 'not popular' or 'not successful' movies performs better than that of predicting 'popular' or 'successful' movies. Predicting unsuccessful movies can be useful for the Internet Protocol television (IPTV) content providers such as Netflix. Whereas tens of thousands of TV contents are made available, only a small portion of them are actually consumed (Reformat and Yager, 2014). Therefore, our approach can be used to filter out such contents that are not appealing to the content viewers. For future work, we will further investigate the efficacy of our approach in the thriller and the comedy genes, which presented the best performances. In addition, we will extend our model to deal with the magnitude of a movie's success. For this, linear regression models can be applied to predict different levels of success. Figure 1 : 1The overall classification procedure Figure 2 : 2The sentiment vector representation of the movie 'The Avengers'. Figure 5 :Figure 6 : 56A A merged bidirectional residual LSTM Table 2 : 2The evaluation results for the audience score. The best performances in F1 score are in bold. Table 3 : 3The evaluation results for the critics score. The best performances in F1 score are in bold.Score Genre Model Recall Precision F1 1 0 1 0 1 0 Audience&Critics All genre ELMO 0.67 0.74 0.69 0.72 0.68 0.73 CNN 0.68 0.70 0.64 0.67 0.66 0.69 LSTM 0.68 0.67 0.64 0.71 0.66 0.69 Table 4 : 4The evaluation results for the audience & critics score. The best performances in F1 score are in bold. http://www.cs.cmu.edu/˜ark/personas/ 2 https://www.rottentomatoes.com/ https://www.seleniumhq.org/ https://tfhub.dev/google/elmo/2 Learning latent personas of film characters. David Bamman, Brendan Oconnor, Noah A Smith, Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. the 51st Annual Meeting of the Association for Computational Linguistics1David Bamman, Brendan OConnor, and Noah A Smith. 2013. Learning latent personas of film char- acters. 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GENERATION OF NOUN COMPOUNDS IN HEBREW: CAN SYNTACTIC KNOWLEDGE BE FULLY ENCAPSULATED?

Hebrew •includes a very productive noun-compounding construction called smixut. Because smixut is marked morphologically and is restricted by many syntactic constraints, it has been the focus of many descriptive studies in Hebrew grammar.We present the treatment of smixut in HUGG, a FUF-based syntactic realization system capable of producing complex noun phrases in Hebrew. We contrast the treatment of smixut with noun-compounding in English and illustrate the potential for paraphrasing it introduces.We Specifically address the issue of determining when a smixut construction can be generated as opposed to other semantically equivalent constructs. We investigate several competing hypotheses -smixut is lexically, semantically and/or pragmatically determined. For each hy: pothesis, we explain why the decision to produce a smixut construction cannot be reduced to a computation Over features produced by an outside module that Would not need to know about the smixut phenomenon.We conclude that smixut provides yet another theoretical example where the interface that a syntactic realization component presents to the other components of a generation architecture cannot be made as isolated as we would hope. While the syntactic constraints on smixut are encapsulated within HUGG, the input Specification language to HUGG must contain a feature that specifies that smixut is requested if possible.• However, because smixut accounts for close to half the cases of NP modifiers observed on a corpus of complex NPs, and it •appears to be the unmarked realization form for some frequent semantic relations, we empirically evaluate a default setting strategy for the feature use-smixut based on a simple semantic Classification of the relations head-modifier in the NP. This study provides a Solid ground for the definition of a small set of predicates in the input specification language to HUGG, that has applications beyond the selection of smixut --for the determination of the order of modifiers in the NP and the use of stacking vs. conjunction --and for the definition of a bilingual input specification language.

GENERATION OF NOUN COMPOUNDS IN HEBREW: CAN SYNTACTIC KNOWLEDGE BE FULLY ENCAPSULATED? Yael Dahan Netzer Michael Elhadad Ben Gurion University Department of Mathematics and Computer Science 84105Beer ShevaIsrael GENERATION OF NOUN COMPOUNDS IN HEBREW: CAN SYNTACTIC KNOWLEDGE BE FULLY ENCAPSULATED? Hebrew •includes a very productive noun-compounding construction called smixut. Because smixut is marked morphologically and is restricted by many syntactic constraints, it has been the focus of many descriptive studies in Hebrew grammar.We present the treatment of smixut in HUGG, a FUF-based syntactic realization system capable of producing complex noun phrases in Hebrew. We contrast the treatment of smixut with noun-compounding in English and illustrate the potential for paraphrasing it introduces.We Specifically address the issue of determining when a smixut construction can be generated as opposed to other semantically equivalent constructs. We investigate several competing hypotheses -smixut is lexically, semantically and/or pragmatically determined. For each hy: pothesis, we explain why the decision to produce a smixut construction cannot be reduced to a computation Over features produced by an outside module that Would not need to know about the smixut phenomenon.We conclude that smixut provides yet another theoretical example where the interface that a syntactic realization component presents to the other components of a generation architecture cannot be made as isolated as we would hope. While the syntactic constraints on smixut are encapsulated within HUGG, the input Specification language to HUGG must contain a feature that specifies that smixut is requested if possible.• However, because smixut accounts for close to half the cases of NP modifiers observed on a corpus of complex NPs, and it •appears to be the unmarked realization form for some frequent semantic relations, we empirically evaluate a default setting strategy for the feature use-smixut based on a simple semantic Classification of the relations head-modifier in the NP. This study provides a Solid ground for the definition of a small set of predicates in the input specification language to HUGG, that has applications beyond the selection of smixut --for the determination of the order of modifiers in the NP and the use of stacking vs. conjunction --and for the definition of a bilingual input specification language. Introduction Over the past three years,• we have started developing HUGG, a syntactic realization component for Hebrew. One of our objectives is to investigate constraints on the design of the input specification language to a syntactic realization component through a contrastive analysis of the requirements of English and Hebrew. By design, we are attempting to keep the input to HUGG as similar as possible to the one we defined in the SURGE syntactic realization for English [7]. A detailed analysis of syntactic constructs specific to Hebrew becomes, therefore, critical to evaluate to which extent the input specification language can abstract away from knowledge of the syntax. We investigate in this paper one such construct: the Hebrew noun-compounding form known as srnixut. Because smixut is morphologically marked and remarkably productive in Hebrew, there exists a vast tradition of work in descriptive grammar of Hebrew providing functional analysis of the phenomenon [11] [10] [13]. This previous work has served as a fertile ground for our own generation-specific purposes. The specific issue we discuss in this paper is: what information in the input specification to the syntactic realization component can license the selection of a smixut construct. The classical objectives of modularity and knowledge encapsulation indicate that this decision should be a private decision of the syntactic realization component. Because there are so many syntactic constraints on the use of smixut, the objective of encapsulation is made even more desirable. After a thorough analysis of the different functions of the smixut construct and the constraints over its use, our conclusion, however, is that this reductionist strategy fails: we cannot explain the selection of a smixut construct without considering simultaneously lexical, semantic and pragmatic factors. Theoretically, in order to allow the syntactic realization component to select a smixut construct adequately, we are, therefore, left with two options: (1) either provide full, detailed access from the syntactic realization component to the complex semantic and pragmatic features that can impact on the decision; or else, (2) allow the other components to request the use of a smixut construct when they deem it adequate. In either case, modularity and encapsulation suffer. This analysis informs us in our design of a bilingual realization component: if a feature like use-smixut is required in the input to the syntactic component, this level of abstraction cannot be appropriate as a bilingual construction. It also informs us in the general ongoing debate over the design of reusable syntactic components and their place in the architecture of generators. From a more pragmatic perspective, however, we also provide a set of simple defaults for the generation of smixut based on a simple semantic classification of the relations head-modifier. We evaluate the validity of this classification by constructing input specifications for a corpus of more than 800 comp!ex, noun phrases and regenerating from them. The validation process includes two aspects: (1) we test that human coders agree on the semantic relations they use to label complex NPs; and (2) we verify that the generator's decision to produce a smixut construction corresponds to that observed in the corpus. Preliminary results are provided in Section 4.3. They encourage us to view in the set of semantic relations we propose a useful basis for the design of an interlingual input specification language. In the rest of the paper, we first briefly review the main approaches to the treatment of nouncompounds in English and in Hebrew. In Section 3 we provide descriptive data on the use of smixut in Hebrew. We then describe in Section 4 a first approach to the generation of smixut based on a simple semantic classification similar to that found in [12]. In Section 5, we identify the limitations of such an approach, illustrating that an explanation based on recoverable semantic relations cannot provide sufficient nor necessary conditions for the generation of smixut. However, the preliminary empirical evaluation we present in Section 4.3 demonstrates that the semantic relation approach provides a useful default that works "most of the time." 2 PreviOus Work 2.1 Noun compounds in English Noun compounds in English axe partly "frozen" lexical constructions (e.g., computer science) and partly compositional constructions (e.g., computer equipment, farm equipment, city equipment...). The problematic aspect of this construction is that it seems to be very productive in English, but yet severely constrained (e.g., * science equipment). Compound constructions are also regularly ambiguous. The various approaches developed to explain the construction of noun compounds and their interpretation can be classified in three groups: semantic, pragmatic, and statistical/lexical. • Semantic theories explain the production of a noun compound N1 N2 as a derivation from a semantic relation N1 R NP where the relation • R is elided. The theory of recoverably deletable predicates (RDPs) Of [!2] proposes that only a small set of relations (canse, have, make...) can participate in this process. Because these relations were too general and sometimes vague, and because one can observe many cases of compounds that do not correspond to any of the proposed RDPs, others have proposed to define more precise domain specific models to explain the deletion of certain relations. Recognizing the importance of contextual factors, pragmatic theories predict the use of noun compounding when relations like naming or contrast play a role [6]. For example, when referring to two persons wearing a jacket and a Coat respectively, one can use compounds like the jacket man and the coat man even though, in neutral contexts, it would be difficult to interpret the same compound (i.e., the wear relation is not deletable). In [5], the explanation for compounding is provided in the form of lexical/syntactic knowledge. Generative devices inspired by [14] are found in the lexicon. In addition, statistical knowledge predicts which derivations are the most likely. From a generation perspective, the problem is less acute than for interpretation: we must decide whether to construct a compound as opposed to recover the missing relation between the head and the modifier. The problem has, therefore,• not received heavy attention for English generation. In the past, we used Levi's model in generation [8], but as part of the lexical chooser, and we did not include it within th e syntactic realization component. In Hebrew, however, the smixut Construction is extremely productive (in our corpus, smixut modification accounts for 40% of all modifiers, more than any other type of syntactic modification • in NPs). We, therefore, had to address the issue of when to generate smixut as a priority in the development of the NP grammar for HUGG. Noun compounds in Hebrew The structure of noun compounds in Hebrew -smixut, is marked and, therefore, it has been the focus of Hebrew language studies. The head (called nismax) is marked morphologically and it does not carry a mark of definiteness even when it is semantically definite. [! 0] [2] provide detailed studies of the syntactic constraints on the use of smixut. We provide an overview of the main constraints in Section 3. Although smixut is traditionally treated as a possessive construction, it can express many other relations between head and modifier. Levi [11] has extended her treatment of the noun-noun relation in English [12] and proposed that the same semantic relations can all be expressed by the Hebrew smixut construction. [2] and [10] (Chapter 6) also )rovide similar semantic classifications of the elided relation in a smixut. We build on these studies in our implementation, but also investigate how a semantic account can be integrated with pragmatic and lexical constraints, Noun Compounds in Hebrew: Constraints We briefly present in this section the basic syntactic constraints over the use of smixut in Hebrew. The notion of "smixut" covers three main constructions: [ Second, when definite is used, only the modifier is marked even though the head is understood as definite: aron 'mitbax (cabinet kitchen) (a kitchen cabinet) vs. aron ha-mitbax (cabinet the-kitchen) (the kitchen cabinet). Nominalizations are also built using a smixut construction with a gerund or a denominal as head: Bo ha-role Bo 'o Sel ha-role The arrival of the doctor: Arrive the-doctor Arrive=his of the-doctor We categorize the constraints on the use of smixut in four categories: syntactic, lexical, semantic and pragmatic. 3.1 Syntactic Constraints One of the main constraints on the use• of smixut is that a head can have only one noun modifier (called somex in Hebrew). When several modifiers are attached to a head, this constraint forces other relations to be realized in other syntactic constructions (post-modifier adjective, prepositional phrase or relative clause). For example, when referring to a suit made of leather, the default realization (unmarked) is the smixut beged wor (suit-leather). The alternative realization beged me-wor (suit from-leather) with a qualifier PP is also possible, but less frequent. However, when referring to a bathing-suit in leather 1 the default realization is beged-yam me-wor (suit-sea fromleather). Because the somex (noun modifier) position of the head beged is occupied by the yam (sea) modifier, the second modifier •(leather) is relegated to another (not nominal) position. The head of a smixut must be a noun or a conjunction of nouns and it cannot be a compound itself. This means that smixut only allows right branching 2. This is in contrast with English, which allows right or left branching constructions: (computer communication) system vs. computer (communication system). Pronouns and proper nouns cannot head a smixut, and any pronoun in the modifier position 'has the objective case and is agglutinated to the noun: ben-o son-him (his son). There are several restrictions on the combination •of smixut with different determiner types. Noun phrases in Hebrew are polydefinite --that is, definiteness is marked on several of the constituents in the phrase. Any adjectival modifier is marked with agglutinated definite markers, the same as the head noun. Quantifiers and determiners can be also marked. In smixut, only the 1Very frequent on Israeli beaches. 2That is, if Hebrew is written left to right. mod-n is marked as definite. Therefore, compounded nouns are understood as having the same definiteness value. As a consequence, if definiteness of the head and modifier differs, smixut cannot be reed: Lexical Constraints Not every noun can head a smixut construction: words which are lexical-compounds (cadur-salball-basket -basketball), words of foreign origin, cannot be in nismaz form (i. e. the special inflection of compound nouns), and therefore any modifiers must be realized in another syntactic construction. Several criteria exist to distinguish frozen fromproductive smixut compounds: frozen compounds behave like regular smixut with-respect to plural marking (special morphology inflection). But depending on the level of cohesiveness of the frozen compound, definite marking may differ: beyt-sefer -house-book -(a school) may give ha-beyt-sefer (the school) instead of the predicted beyt ha-sefer for a productive smixut. In addition, for frozen constructs, many additional constraints exist: the head cannot be modified (* beyt sefer kri'a -house book reading), cannot change its number (* beyt sfar-ym -house books), cannot be taken apart ( * bayt Sel sefer -house of book) or be•conjoined to another somex (noun modifier) (beyt sefer ve beyt Hol-im -house book and house patient-s -a school and hospital but * beyt sefer ve-Hol-im -house book and patient-s). Detailed references from linguistic and sociolinguistic aspects are found in [4] and [3] respectively. • 3.3 Semantic Constraints Smixut is often understood as a genitive type of construct, expressing dominantly a possessive relation between the head and the modifier. Very often, however, the relation expressed is not one of possession. The semantic relation realized by the smixut has an influence on the possible paraphrases the smixut can receive [2]: some semantic relations (including possessive) can be realized in a double genitive construction while others can only be realized by a simple smixut. The semantic relation also determines which types of modifiers can be accepted in the smixut construction. In general, when a double genitive construction is not possible, then pronouns cannot appear as modifiers, even in a simple smixut. In the case of gerunds, the only possible structures are compound and double-genitive while the separate construction is not possible. Levi [11] claims that smixut realizes in Hebrew a number of universal semantic processes which exist inother languages, thus extending her original analysis for English [12]. Her ':non predicative modifiers" theory claims that Noun-noun compounding is produced by two •syntactic processes: nominalization or deletion of the predicate : which corresponds to the observed uses of smixut for possessive and gerund constructions. Azar [2] classifies smixut into 15 semantic categories. This classification can be made parallel to Levi's RDPs. Glinert [10] also refers to such a classification in a similar manner. ,I I I 1 il I ;I 3.4 Pragmatic Constraints There are cases, however, when smixut can be constructed with no regard to the semantic set that was identified. Certain contexts license smixut constructions that would not be obtained otherwise for example, contrast or naming [6]. In addition, smixut is associated with style and genre parameters. Seikevicz [16] analyzes transcripts of spoken Hebrew, and finds that smixut is used when using a 'Sel' preposition is not possible. Other pragmatic considerations for the use of smixut include the objective to generate a more compact text and to make of a compound an item available for further anaphorical reference. Finally, decision to compound a head with a plural or singular modifier is related to the genericity of the description and to the habituality of the relation,.as is the case in English [15] (p. 916): The When Can • Smixut Be Generated? Our main objective is to determine what features must be present in the input to the syntactic realization component to decide when to use a smixut construction. We observe that the •production of smixut is semantically constrained, and that the semantic relation holding between head and modifiers determines which syntactic paraphrases are possible (among smixut, double-genitive and separate construct). A set of semantic predicates similar to Levi's RDPs seems to play a role in the decision. On the other hand, being a member of that set is not a sufficient nor necessary condition to generate a smixut. In the SURGE grammar for English, we did not address this decision, and assume that the input includes a predefined syntactic construction (classifier-head). For Hebrew, we must find an alternative approach because: (1) smixut is extremely frequent (40~0 of the noun modifiers in our corpus); (2) smixut is the default realization for many relations but it cannot be used in many syntactic contexts. 4.1 Exploiting a Semantic Classification Our strategy is to provide in the input to HUGG a reliable default indicating that smixut should be used when possible, but making it possible to fall back on an alternative realization (separate or double genitive, or qualifier modification) when smixut is not possible. For instance, lexical-compounds cannot be head a smixut, and, therefore, their modifier must be realized as a PP. The same semantic relation (e.g., material) will be realized in two different ways depending on the lexical property of the head: If the same input is provided, but the property of the head noun is different, a different Construction will be generated: A similar mechanism would determine that a smixut is not possible if the definiteness of the head and the modifier do.not match, as discussed above in the a son o.f the king example. The syntactic realizer also relies on the semantic classification of the relation head-modifier when several modifiers are attached to a single head. In this case, only a single modifier can be realized as a smixut. The others must be realized differently. In this case, the realizer must determine which relation takes priority to become the smixut, and it must also provide an appropriate paraphrase for the non-smixut modifier. For example, the English NP leather house shoe will be generated in one of the following ways: 3 nawal bayit me-wor cat common house shoe ~om-leather Beyond smixut-related decisions, determining a set of semantic relations is also useful to allow HUGG to determine appropriate defaults for prepositions in PP modifiers. For example, in the example above, HUGG can select the default preposition .for in shoe ]or the house because the relation of purpose is specified. The same classification is also useful to determine the order and the syntactic structure of a ' multi-modifier sequence in complex NPs. In general, when several modifiers attach to a single head, a broken (conjuncted) sequence is created [9] (in contrast to English, where a stacking construction is generally used): .4 big white house vs. bayit gadol ve-lavan (house big and white). However, when adjectives realize a semantic relation that could have been reaiized by a smixut, they appear first in the sequence of modifiers and they do not require a conjunction [1]. In this example, makdeHa HaSmalyt gadola is produced instead of * makdeha gadola ve-HaSmalyt because the electric modifier realizes the smixut-licensing relation of instrument. This phenomenon gives a further justification for the use of semantic relations in the input. 4.2 Classification of Semantic Relations Since the syntactic realization component can make good use of a semantic classification in the input, we have designed the classification shown in Table 1, which synthesizes the lists provided by Levi, Glinert and Azar. In the table we present a basic list of relations with its occurrence percentage in our corpus. It can be viewed that some relations are much more productive than others -purpose, has-part. Our classification is finer than Levi's in distinguishing for example among different types of typical possessive relations (human-relator, has-part and ownership). This reflects slight differences in the default way of generation. Human-relator, for instance, is used as construct when the modifier is a pronoun, more often than with other ownership relations. 3VVe discuss below the heuristics HUGG uses to decide between these 2 paraphrases. • I I I I I I i I I I I I I Validation of the Classification To validate empirically the definition of our semantic classification, we gathered a corpus of 853 complex NPs (NPs with more than one modifier) from written Hebrew sources (newspaper and novels). For each NP, we labeled the relations head-modifier in terms of the relations listed in Table 1. Our evaluation covers two aspects: we first verify that human coders agree on the labeling; we then verify that HUGG can generate from a labeled input a realization similar to that observed in the corpus. Preliminary evaluation of the agreement among human judges shows agreement of about 90% between three judges (we are currently extending the number of judges). The percentage agreement includes a category "undecided" which covers about 5% of the cases. This corresponds to cases where judges found the relation ambiguous or unclear. Judges agreed on the labeling of unclear relations. In our corpus, we observed the following distribution in terms of syntactic realization (this takes into account NPs with more than one modifier, explaining that the sum is > 100%): 139% Ismixut 131% ]Pp-qualifier I 34% describer 8% relative clause When regenerating from the labeled input we have determined, I-IUGG's decision to generate a smixut corresponded to that observed in the corpus on more than 95% of the cases. Limitations of a Semantic Account While the semantic account described above provides good results, it cannot be the only mechanism licensing the production of smixut. We discuss in this section the type of interaction that must be allowed between discourse and pragmatic parameters and the syntactic realization component. In [5], the interaction between lexical semantics and pragmatics is explored, and two axioms are proposed to interface between the defaults of the lexical semantic and the arbitrary knowledge of pragmatics: (1) defaults survive and (2) discourse wins. A statistical method is then added in Order to resolve possible interpretations. It isassumed, then, that the grammar/lexicon delimits the range of compounds and indicates conventional interpretations, but that some compounds may only be resolved by pragmatics and that non-conventional contextual interpretations are always available. To provide interpretations , a general schema is encoded in the lexicon leaving undecidable cases to be resolved by pragmatics. Probabilities of possible interpretations are taken from corpus frequencies. Accordingly, a new rule is added: (3) Prefer Frequent Senses, which can still be overridden by contextual factors. From the generation perspective, the interaction between discourse licensed-relations and conventional readings must similarly be controlled by preference rules. For example, when referring to a city destroyed by the Barbarian, discourse readings cannot override the conventional reading in The Barbarian city: discourse cannot force the reading of a city destroyed by the Barbarians. This indicates that a non-monotonic form of reasoning, taking into account preference rules similar to that identified in [5] must be implemented at the pragmatic level. Clearly, this type of reasoning does not belong within the syntactic realization component. Therefore, we conclude that the feature use-smixut remains a necessary part of the input specification language to the syntactic realization component. Conclusion We have presented in this paper basic data on the Hebrew smixut construction. Our strategy to implement smixut in the HUGG syntactic realization is to provide a simple semantic classification in the input. We have demonstrated the many benefits this classification has within the realization process. Two main problems have been traditionally associated with such semantic accounts of nouncompounding: the relations are not well-defined enough and they are not necessary nor sufficient to explain all uses of compounding. We address these two problems in three ways: (1) we provide an empirical evaluation demonstrating high coder agreement when labeling complex NPs with the set of relations we identify; (2) we demonstrate empirically that the default strategy of "generating a smixut when a semantic relation licenses it" corresponds with the observed usage of smixut in more than 95% of the cases; and (3) we allow the pragmatic module to add a feature use-smixut in the input. I I I ! ! ! ! I ! ! I ! ! ! The same set of semantic relations is now being used in an extension to SURGE to allow similar paraphrasing decisions in English. head-N + mod-N definite (The son of the king) ben ha-melex son the-king ben-o Sei ha-rnelex son-his of the-king head-N + mod-N indefinite ben me/ex ben Seg raelez (a son of a king) son king son of king head-N indefinite, mod-N definite benSeI ha-rnelez (a son of the king) son of the king head-N definite, mod-N indefinite ben-o Sel melex (The son of a king) son-his of king modifiers [ material [lax "wor" ] ] I I - ISimlat Hatuna / dress wedding / wedding dress weyney ha-yeled / eyes the-boy / the boy's eyes PirHey midbar / flowers desert / desert flowers wugat tapuHym / cake apples / apple cake " Em habanym / mother the-sons / mother of the sons AruHat Zaharym / meal noon / dinner macat HaSmal / hit electricity / electric shock yetuS kadaHat / mosquitoe malaria/malaria mosquitoe mifwal keramika / factory ceramics / ceramics factory magheZ edim / iron steam / steam ironRelation %corpus Example nominalization purpose has-part location content . human-relator 12.00% 11.24% 11.24% 6.50% 6.21% 6.21% hacHaSat meZi'ut / denial reality / reality denial type owner producer matter material idioms relational name 5.91% 5.91% 5.02% 4.14% 3.84% 3.84% 3:55% 2.95% regeS Ahavah / feeling love / love feeling mytat horay / bed my-parents / my parents' bed reyaH bSamym / scent perfium / perfume scent miSpat reZaH / trial murder / murder trial cise weZ / chair wood / wooden chair cadur ha-AreZ / ball the-land / earth Zevaw ha-baZek / color the-batter / the color of the batter miSpaHat netanyahu / family netanyahu / The Netanyahu family experiencer config-units represented part-of time cause caused-by product instrument 2.95% 1.45% 1.18% 0.88% 0.88% O.88% 0.29% 0.29% 0.29% ce'ev-a ] pain-her-acc/her pain zer praHym / bouquet flowers ] a bouquet of flowers semel yokrah / symbol prestige / prestige symbol nawaley waker / shoe heel / high-heels shoes Table 1 : 1Semantic relations that can produce a smixut The order of adjectives in Hebrew. Talila Atias, IsraelTel Aviv UniversityMaster's thesis. in HebrewTalila Atias. The order of adjectives in Hebrew. Master's thesis, Tel Aviv University, Israel, 1981. (in Hebrew). Classification of hebrew compounds. M Azar, Academic Teaching of Contemporary Hebrew. International Center for University Teaching of Jewish Civilization. R. NitJerusalemin HebrewM. Azar. Classification of hebrew compounds. In R. Nit, editor, Academic Teaching of Contem- porary Hebrew. International Center for University Teaching of Jewish Civilization, Jerusalem, 1985. (in Hebrew). On the lexical degree of the constructs in hebrew. Ruth Berman, Dorit Ravid, Hebrew Computational Linguistics. 24In HebrewRuth Berman and Dorit Ravid. On the lexical degree of the constructs in hebrew. Hebrew Computational Linguistics, (24):5-22, 1986. (In Hebrew). On the morphological parallelism between compounds and constructs. Department of Cognitive Science. Hagit Borer, School of Social SciencesUniversity of California, IrvinneHagit Borer. On the morphological parallelism between compounds and constructs. Depart- ment of Cognitive Science, School of Social Sciences, University of California, Irvinne. Integrating symbolic and statistical representation: The lexicon pragmatics interface. Ann Copestake, Alex Lascarides, Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics. the 35th Annual Meeting of the Association for Computational LinguisticsACLAnn Copestake and Alex Lascarides. Integrating symbolic and statistical representation: The lexicon pragmatics interface. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, pages 136-143. ACL, 1997. On the creation and use of english compound nouns. P Downinig, Language. 534P. Downinig. On the creation and use of english compound nouns. Language, 53(4)~810-842, 1977. Using argumentation to control lexical choice: a unification-based implementation. Michael Elhadad, Computer Science Department, Columbia UniversityPhD thesisMichael Elhadad. Using argumentation to control lexical choice: a unification-based imple- mentation. PhD thesis, Computer Science Department, Columbia University, 1992. Lexical choice for complex noun phrases: Structure, modifiers and determiners. Machine Translation. Michael Elhadad, 11Michael Elhadad. Lexical choice for complex noun phrases: Structure, modifiers and deter- miners. Machine Translation, 11:159-184, 1996. Stacked adjectives and conflgurationality. David Gil, Linguistic Analysis. Grammar of Modern Hebrew. Cambridge UniversityL. Glinert. TheDavid Gil. Stacked adjectives and conflgurationality. Linguistic Analysis, (12):141-158, 1983. L. Glinert. The Grammar of Modern Hebrew. Cambridge University, 1989. A semantic analysis of hebrew compound nominals. J N Levi, Studies in Modern Hebrew syntax and semantics. Peter ColeNorth-Holland, AmsterdamJ.N. Levi. A semantic analysis of hebrew compound nominals. In Peter Cole, editor, Studies in Modern Hebrew syntax and semantics. North-Holland, Amsterdam, 1976. The Syntax and Semantics of Complex Nominals. J N Levi, Academic PressNew YorkJ.N. Levi. The Syntax and Semantics of Complex Nominals. Academic Press, New York, 1978. The Nominal Phrase in Modern Hebrew. Uzi Ornan, JerusalemHebrew UniversityPhD thesisin HebrewUzi Ornan. The Nominal Phrase in Modern Hebrew. PhD thesis, Hebrew University, Jerusalem, 1964: (in Hebrew). The generative lexicon. James D Pustejovsky, Computational Linguistics. 174James D. Pustejovsky. The generative lexicon. Computational Linguistics, 17(4):409-441, December 1991. A comprehensive grammar of the English language. R Quirk, S Greenbaum, G Leech, J Svartvik, LongmanR. Quirk, S. Greenbaum, G. Leech, and J. Svartvik. A comprehensive grammar of the English language. Longman, 1985. The Possessive Construction in Modern Hebrew: A Sociolinguistic Approach. Channa Seikevicz, Washington D.C.Georgetown UniversityPhD thesisChanna Seikevicz. The Possessive Construction in Modern Hebrew: A Sociolinguistic Ap- proach. PhD thesis, Georgetown University, Washington D.C., 1979.

Understanding of unknown medical words

We assume that unknown words with internal structure (affixed words or compounds) can provide speakers with linguistic cues as for their meaning, and thus help their decoding and understanding. To verify this hypothesis, we propose to work with a set of French medical words. These words are annotated by five annotators. Then, two kinds of analysis are performed: analysis of the evolution of understandable and non-understandable words (globally and according to some suffixes) and analysis of clusters created with unsupervised algorithms on basis of linguistic and extralinguistic features of the studied words. Our results suggest that, according to linguistic sensitivity of annotators, technical words can be decoded and become understandable. As for the clusters, some of them distinguish between understandable and non-understandable words. Resources built in this work will be made freely available for the research purposes.

Understanding of unknown medical words 8 September 2017 Natalia Grabar natalia.grabar@univ-lille3.fr Thierry Hamon hamon@limsi.fr UMR 8163 STL CNRS Université Lille 3 59653Villeneuve d'AscqFrance LIMSI CNRS Université Paris-Saclay OrsayFrance Université Paris 13, Sorbonne Paris CitéVilletaneuseFrance Understanding of unknown medical words Proceedings of the Biomedical NLP Workshop associated with RANLP 2017 the Biomedical NLP Workshop associated with RANLP 2017Varna, Bulgaria8 September 201710.26615/978-954-452-044-1_005 We assume that unknown words with internal structure (affixed words or compounds) can provide speakers with linguistic cues as for their meaning, and thus help their decoding and understanding. To verify this hypothesis, we propose to work with a set of French medical words. These words are annotated by five annotators. Then, two kinds of analysis are performed: analysis of the evolution of understandable and non-understandable words (globally and according to some suffixes) and analysis of clusters created with unsupervised algorithms on basis of linguistic and extralinguistic features of the studied words. Our results suggest that, according to linguistic sensitivity of annotators, technical words can be decoded and become understandable. As for the clusters, some of them distinguish between understandable and non-understandable words. Resources built in this work will be made freely available for the research purposes. Introduction Often, people face unknown words, be they neologisms (like in Some of the best effects in my garden have been the result of serendipity.) or technical words from specialized areas (like in Jacques Chirac's historic corruption trial, due to start on Monday is on the verge of collapse, after doctors diagnosed him with "anosognosia"). In both cases, their semantics may be opaque and their understanding not obvious. Several linguistic operations are available for enriching the lexicon, such as affixation, compounding and borrowings (Guilbert, 1971). We are particularly interested in words with internal structure, like anosognosia, because we assume that linguistic regularities (components, affixes, and rules that form their structure) can help speakers in deducing their structure and semantics. Our hypothesis is that if regularities can be observed at the level of linguistic features, they can also be deduced and managed by speakers. Indeed, linguistic understanding is related to factors like: • knowledge and recognition of components of complex words: how to segment words, like anosognosia, in components; • morphological patterns and relations between components: how to organize the components and to construct the word semantics (Iacobini, 2003;Amiot and Dal, 2008). To verify our hypothesis, we propose to work with a set of French medical words. These words are considered out of context for several reasons: 1. when new words appear, they have little and poor contexts, which cannot usually help their understanding; 2. similarly, in specialized areas, the contexts, except some definitional contexts, often bring little help for the understanding of terms; 3. working with words out of context permits to process a bigger set of words and to make observations with larger linguistic material; 4. from another point of view, analysis of words in context corresponds to their perception in extension relying on external clues, while analysis of words out of context corresponds to their perception in intension relying on clues and features internal to these words. For these reasons, we assume that internal structure of unknown words can help their understanding. According to our hypothesis, affixed words and compounds, which are given internal structure, can provide the required linguistic clues. Hence, the speakers may linguistically analyze unknown words thanks to the exploitation of their structure that they are able to detect. Our interest for medical words is motivated by an increasing presence of medical notions in our daily life, while medicine still keeps a lot of mysteries unknown to lay persons because medical knowledge is typically encoded with technical and very specialized terms. In what follows, we present some existing works (section 2), the data which we propose to process (section 3), and the experiments we propose to exploit (sections 4 to 6). We conclude with some orientations for future work (section 7). Existing work We concentrate on work related to text difficulty and understanding. Work on processing of words unknown in dictionaries by automatic applications, although well studied, is not presented. NLP provides a great variety of work and approaches dedicated to understanding and readability of words and texts. The goal of readability is to define whether texts are accessible for readers or not. Readability measures are typically used for evaluation of document complexity. Classical readability measures exploit information on number of characters and syllables of words (Flesch, 1948;Gunning, 1973), while computational measures can involve vectorial models and different features, among which combination of classical measures with terminologies (Kokkinakis and Toporowska Gronostaj, 2006); n-grams of characters (Poprat et al., 2006); stylistic or discursive (Goeuriot et al., 2007) features; lexicon (Miller et al., 2007); morphological information (Chmielik and Grabar, 2011); and combination of various features (Wang, 2006;Zeng-Treiler et al., 2007;Leroy et al., 2008;Gala et al., 2013). In linguistics and psycholinguistics, the question on understanding of lexicon may focus on: • Knowledge of components of complex words and their decomposition. The purpose is to study how complex words (affixed or compounds) are processed and recorded. Several factors may facilitate reading and production of complex words: when these compounds contain hyphens (Bertram et al., 2011) or spaces (Frisson et al., 2008; when they are presented with other morphologically related words (Lüttmann et al., 2011); and when primes (Bozic et al., 2007;Beyersmann et al., 2012), pictures (Dohmes et al., 2004;Koester and Schiller, 2011) or favorable contexts (Cain et al., 2009) are used; • Order of components and variety of morphological patterns. Position of components (head or modifier) proved to be important for processing of complex words (Libben et al., 2003;Holle et al., 2010;Feldman and Soltano, 1999). The notions of semantic transparency and of morphological headedness have been isolated (Jarema et al., 1999;Libben et al., 2003); • Word length and types of affixes (Meinzer et al., 2009); • Frequency of bases and components (Feldman et al., 2004). Our hypothesis on emerging of linguistic rules involved in word formation has also been addressed in psycholinguistics, and it has to face two other hypothesis on acquisition in context and on providing explicit information on semantics of components (Baumann et al., 2003;Kuo and Anderson, 2006;McCutchen et al., 2014). Currently, the importance of morphological structure for word processing seems to be accepted by psycholinguists (Bowers and Kirby, 2010), which supports our hypothesis. Yet, in our work, for verifying this hypothesis, we exploit NLP methods and NLPgenerated features. Hence, we can work with large linguistic data and exploit quantitative and unsupervised methods. Exploited data The data processed are obtained from medical terminology Snomed International (Côté, 1996) in French, which purpose is to describe the medical area. This terminology contains 151,104 terms structured in eleven semantic axes (e.g. disorders and abnormalities, medical procedures, chemicals, leaving organisms, anatomy). We keep terms from five axes (disorders, abnormalities, medical procedures, functions and anatomy), which we consider to be central and frequent. Hence, we do not wish to concentrate on very specialized terms and words, like chemicals or leaving organisms. Nevertheless, such words can be part of terms studied here. The selected terms (104,649) are segmented in words to obtain 29,641 unique words, which are our working material. This set contains compounds (abdominoplastie (abdominoplasty), dermabrasion (dermabrasion)), constructed (cardiaque (cardiac), lipoïde (lipoid)) and simple (fragment) words, as well as abbreviations (AD-Pase, ECoG, Fya) and borrowings (stripping, Conidiobolus, stent, blind). These terms are annotated by five French native speakers, aged from 25 to 60, without medical training and with different social and professional status. Each annotator received a set with randomly ordered 29,641 words. According to the guidelines, the annotators should not use additional information (dictionaries, encyclopedia, etc.), should not change annotations done previously, should manage their time and efforts, and assign each word in one of the three categories: (1) I can understand, containing known words; (2) I am not sure, containing hesitations; (3) I cannot understand, containing unknown words. We assume that our annotators represent moderate readability level (Schonlau et al., 2011), i.e. the annotators have a general language proficiency but no specific knowledge in medical domain, and that we will be able to generalize our observations on the same population. Besides, we assume that these annotations will allow to observe the progression in the understanding of technical words. Manual annotation required from 3 weeks up to 3 months. The inter-annotator agreement (Cohen, 1960) is over 0.730. Manual annotation allows to distinguish several types of words which are difficult to understand: (1) abbreviations (e.g. , OG, VG, PAPS, j, bat, cp); (2) proper names (e.g. , Gougerot, Sjögren, Bentall, Glasgow, Babinski, Barthel, Cockcroft), which are often part of terms meaning disorders and procedures; (3) medications; (4) several medical terms meaning disorders, exams and procedures. These are mainly compounds (e.g. antihémophile (anti haemophilus), sclérodermie (sclerodermia), hydrolase (hydrolasis), tympanectomie (tympanectomia), synesthésie (synesthesia)); (5) borrowings; (6) words related to human anatomy (e.g. cloacal (cloacal), nasopharyngé (nasopharyngal), mitral (mitral), diaphragmatique (diaphragmatic), inguinal (inguinal),érythème (erythema), maxillofacial (maxillo-facial), mésentérique (mesenteric), mésentère (mesentry)). 2. Unsupervised classification of words, analysis of clusters and their comparison with manual annotations (section 6). Progression in word understanding Progression of word understanding corresponds to the rate of understandable and non-understandable words at a given moment t for a given annotator. This permits to observe whether the annotators can become familiar with some components or morphological rules, and improve their understanding of words while the annotation is going on. This analysis is done on the whole set of words and on words with some components. Figure 1 indicates the evolution of the three categories of words. The line corresponding to I cannot understand is in the upper part of the graphs, while the line I can understand is in the lower part. The category I am not sure is always at the bottom. We can distinguish the following tendencies: • Annotators A2, A1 and especially A5 show the tendency to decrease the proportion of unknown words. We assume that they are becoming more familiar with some components and bases, and that they can better manage medical lexicon; • Annotators A1, and in a lesser way A2 and A4, show the tendency to decrease the number of hesitation (category 2). Indeed, the proportion of these words decreases, while the proportion of words felt as known (category 1) increases. Later, the number of known words seems not to increase, except for A5. Besides, this learning effect is especially observable with the top 2,000 words and it mainly affects the transition of hesitation words to known words; • For annotators A3 and A4, after a small increase of proportion of unknown words, this proportion remains stable. We assume that the annotation process of a large lexicon did not allow to gain in understanding of components of the processed technical words. Figures 2 and 3 show the evolution of understanding of words ending with -ite (-itis) (meaning inflammation) and -tomie (-tomy) (meaning removal), respectively. We can see that A5 has difficulty to understand these words: the percentage of unknown words is increasing, while on the whole set of words ( figure 1(e)) this annotator shows the opposite tendency, with the percentage of unknown words decreasing. Annotators A2 and A4 also have understanding difficulties with these words. Figures of other annotators suggest that they make progress in decoding and understanding of words in -ite and -tomie. They first show an improvement in understanding of these words, and later there is another small progression. On the basis of these observations, we can see that, according to types of words, to their linguistic features and to the sensitivity of annotators, it it possible to make progressive improvement in understanding of technical lexicon which a priori is unknown by speakers. As already noticed, we assume that linguistic regularities play an important role in improving of the understanding of new lexicon. We propose to observe now if such regularities can also be detected by unsupervised clustering algorithms. Unsupervised classification of words Unsupervised classification is performed with several algorithms implemented in Weka: SOM (Kohonen, 1989), Canopy (McCallum et al., 2000), Cobweb (Fisher, 1987), EM (Dempster et al., 1977), SimpleKMeans (Witten and Frank, 2005). Excepting SimpleKMeans and EM, it is not necessary to indicate the expected number of clusters. Each word is described with 23 linguistic and extra-linguistic features, which can be grouped in 8 classes (an excerpt is provided in Table 1): • POS-tags. POS-tags and lemmas are computed by TreeTagger (Schmid, 1994) and then checked by Flemm (Namer, 2000). POS-tags are assigned to words within the context of their terms. If a given word receives more than one tag, the most frequent is kept as feature. Among the main tags we find for instance nouns, adjectives, proper names, verbs and abbreviations; • Presence of words in reference lexica. We exploit two French reference lexica: TLFi 1 and lexique.org 2 . TLFi is a dictionary of the French language covering XIX and XX centuries, and contains almost 100,000 entries. POS l 1 l 2 f g f t nb a nb s initial final nb c nb v alarme N + + 73400000 6 1 2 ala,alar,alarm rme,arme,larme 3 3 hépatite N + + 15300000 9 3 3 hép,hépa,hépat ite,tite,atite 4 4 angiocholite N -+ 74700 12 1 5 ang,angi,angio ite,lite,olite 6 6 desmodontose N + -2050 12 1 4 des,desm,desmo ose,tose,ntose 7 5 Table 1: Excerpt with features: POS-tag, presence in reference lexica (TLFI l 1 and lexique.org l 2 ), frequency in search engine f g and terminology f t , number of semantic axes nb a , number of syllables nb s , initial and final substrings (initial, final), number of consonants nb c , number of vowels nb v . lexique.org has been created for psycholinguistic experiments. It contains over 135,000 entries, including inflectional forms. It contains almost 35,000 lemmas. We assume that words that are part of these lexica may be easier to understand; • Frequency of words through a non specialized search engine. For each word, we query the Google search engine in order to know its frequency attested on the web. We assume that words with higher frequency may be easier to understand; • Frequency of words in medical terminology. For the same reason as above, we compute the frequency of words in the medical terminology Snomed International; • Number and types of semantic categories associated to words. We also exploit the information on semantic axes of Snomed International and assume that words which occur in several axes are more central; • Length of words in number of characters and syllables. For each word, we compute the number of its characters and syllables, because we think that longer words may be more difficult to understand; • Number of bases and affixes. Each lemma is analyzed by the morphological analyzer Dérif (Namer and Zweigenbaum, 2004), adapted to the treatment of medical words. It performs decomposition of lemmas into bases and affixes, and provides semantic explanation of the analyzed lexemes. We exploit morphological decomposition, which permits to compute the number of affixes and bases. Here again we focus on complexity of the internal structure of words; • Initial and final substrings. We compute the initial and final substrings of different length, from three to five characters. This allows to isolate some components and possibly the morphological head of words; • Number and percentage of consonants, vowels and other characters. We compute the number and the percentage of consonants, vowels and other characters (i.e. hyphen, apostrophe, comas). We perform experiments with three featuresets: • E c : the whole set with 23 features, • E r : set with features reduced to linguistic properties of words, such as POS-tag, number of syllables, initial and final substrings, which permits to take into account observations from psycholinguistics (Jarema et al., 1999;Libben et al., 2003;Meinzer et al., 2009), • E f : set with linguistic features and frequency collected with the search engine, which permits to consider other psycholinguistic observations (Feldman et al., 2004). With SimpleKMeans and EM, we perform two series of experiments, in which the number of clusters is set to 1,000 and 2,000 (for almost 30,000 individuals to cluster). We expect to find linguistic regularities of words in clusters, according to the features exploited. More specifically, we want to observe whether the content of clusters is related to the understanding of words. Features SOM Canopy Cobweb E c : Full set (23) 5 62 33853 E r : Reduced set (8) 4 28 12577 E f : E r and frequency (9) 4 27 9861 Table 2: Generated clusters In Table 2, we indicate the number of clusters obtained with various sets of features: SOM generates very few clusters, which are big and heterogeneous. For instance, with E f , clusters contain up to 13,088, 4,840, 7,023 and 4,690 individuals; Cobweb generates a lot of clusters among which several singletons. For instance, with E f , we obtain 9,374 clusters out of which 9,861 are singletons; EM and SimpleKMeans generate the required number of clusters, 1,000 and 2,000; Canopy generates between 30 and 60 clusters, according to the features used. We propose to work with clusters obtained with Canopy because it generates reasonnable number of clusters, which number and contents are motivated by features. With features from sets E r and E f , cluster creation is mainly motivated by initial substrings (not always equal to 3 to 5 first or final characters) and in a lesser way by their POS-tags and frequencies. For instance, we can obtain clusters with words beginning by p or a, or clusters grouping phosphats or enzymes ending with -ase. In this last case, clusters with chemicals become interesting for our purpose, although globally the clusters generated on basis of features from sets E r and E f show little interest. We propose to work with clusters obtained with the E c featureset. With Canopy, the size of clusters varies between 1 and 2,823 individuals. Several clusters are dedicated to two main annotation categories. Hence, 30 clusters contain at least 80% of words from the category 1 (I can understand), while 6 clusters contain at least 80% of words from the category 3 (I cannot understand). Among the clusters with understandable words, we can find clusters with: • numerals (mil (thousand), quinzième (fifteen)), verbs (allaite (breast-feed),étend (expand)), and adverbs (massivement (massively), probablement (probably)) grouped according to their POS-tags and sometimes to their final substrings; • grammatical words (du (of), aucun (any), les (the)) grouped on basis of length and POStags; • common adjectives (rudimentaire (rudimentary), prolongé (extended), perméable (permeable), hystérique (hysterical), inadéquat (inadequate), traumatique (traumatic), militaire (military)) grouped according to their POS-tags and frequency; • participial adjectives (inapproprié (inappropriate), stratifié (stratified), relié (related), modifié (modified), localisé (localised), précisé (precise), quadruplé (quadrupled)) grouped according to their POS-tags, frequencies and final substrings; • specialized but frequent adjectives (rotulien (patellar), spasmodique (spasmodic), putréfié (putrefactive), redondant (redundant), tremblant (trembling), vénal (venal), synchrone (synchronous), sensoriel (sensory)), also grouped according to their POStags and frequencies; • specialized frequent nouns (dentiste (dentist), brosse (brush), altitude (altitude), glucose (glucose), fourrure (fur), ankylose (ankylosis), aversion (aversion), carcinome (carci-noma)) grouped according to their POS-tags and frequencies. Among the clusters with non-understandable words, we can find: • chemicals (dihydroxyisovalérate, héparosane-N-sulfate-glucuronate, désoxythymidine-monophosphate, diméthylallyltransférase) grouped according to their POS-tags, types of characters they contain and their frequency; • borrowings (punctum, Saprolegnia, pigmentosum, framboesia, equuli, rubidium, dissimilis, frutescens, léontiasis, materia, mégarectum, diminutus, ghost, immitis, folliclis, musculi) grouped according to their POS-tags, final substrings and frequency; • proper names grouped according to their POS-tags. Within clusters with over 80% of words from the category 3 (I cannot understand), we do not observe understanding progression of annotators. Yet, we have several mixed clusters, that contain words from the two main categories (1 (I can understand) and 3 (I cannot understand)), as well as hesitations. These clusters contain for instance: • chemicals and food (créatinine (creatinine), antitussif (antitussive), céphalosporine (cephalosporine), aubergine (eggplant), carotte (carrot), antidépresseur (antidepressant), dioxyde (dioxide)) grouped according to their final substrings, semantic axes and frequency; • organism functions, disorders and medical procedures (paraparésie (paraparesis), névralgie (neuralgia), extrasystole (extrasystole), myéloblaste (myeloblast), syncope (syncope), psychose (psychosis), spasticité (spasticity)) grouped according to their frequency, final substrings and POS-tags; • more specialized adjectives related to anatomy and disorders (périprostatique (periprostatic), sous-tentoriel (tensor), condylienne (condylar), fibrosante (fibrotic), nécrosant (necrosis)) grouped according to their POS-tags and frequency. Evolution of understanding is observable mainly within this last set of clusters. For instance, a typical example is the cluster containing medical procedures ending in -tomie, which words become less frequently assigned to the category 3 (I cannot understand) and more frequently to the categories 2 (I am not sure) and 1 (I can understand). The content of clusters and our observations suggest that, given an appropriate set of features and unsupervised algorithms, it is possible to create clusters which reflect the readability and understandability of lexicon by lay persons. Besides, within some clusters, it is possible to observe the evolution of annotators in their understanding of technical words. For instance, this effect can typically be observed with words meaning disorders and procedures. Nevertheless, with other types of words (chemicals, borrowings, proper names) no evolution is observable. Notice that the same reference data have been used with supervised categorization algorithms. In this case, automatic algorithms can reproduce the reference categorization with F-measure over 0.80 and up to 0.90, which is higher than the interannotator agreement rate. Besides, in the supervised categorization task, the behaviour of features is different from what we can observe in unsupervised clusters: several individual features can reproduce the reference categories while the best results are obtained with the whole set of features. Conclusion and Future work According to our hypothesis, linguistic regularities, when they occur systematically, can help in decoding and understanding of technical words with internal structure (like compounds or derived words). To test the hypothesis, we work with French medical words. Almost 30,000 words are annotated by five annotators and assigned in one of the three categories I can understand, I am not sure, I cannot understand. For each annotator, the words are ordered randomly. We then perform an analysis of the whole set of words, and of words ending with -ite and -tomie. Our results suggest that several annotators show the learning effect as the annotation is going on, which supports our hypothesis and the findings of psycholinguistic work (Lüttmann et al., 2011). This effect is observed for the whole set of words and for the two analyzed suffixes. Yet, with chemicals, borrowings and proper names, we do not ob-serve the learning effect. These observations have been corroborated with clusters generated using linguistic and extralinguistic features. Several clusters are dedicated to words from either 1 (I can understand) or 3 (I cannot understand) categories. Besides, when clusters contain some semantically homogeneous words (disorders, procedures...) we can observe the expected learning effect. These results are very interesting and confirm our hypothesis, according to which linguistic regularities can help to decode and understand technical and unknown words. Appropriate features can also help to distinguish between understandable and non-understandable words with unsupervised methods. Correlations between social and demographic status and understanding require additional annotations. It will be studied in the future. We have several directions for future work: (1) collect the same type of annotations, but providing semantics of some or of all components, although it will be difficult to verify whether this information is really exploited by annotators; (2) collect the same type of annotations, but permitting the annotators to use external sources of informations (dictionaries, online examples...). Since this approach requires more time and cognitive effort, smaller set of words will be used; (3) analyze the evolution of understanding of words taking into account a larger set of components; (4) validate the observations with tests for statistical significance; (5) exploit the results for training and education of non-experts in order to help them with the understanding of medical notions; (6) exploit the results for simplification of technical texts. For instance, features of words that show understanding difficulties can be used to define classes of words that should be systematically simplified. The resources built in this work are freely available for the research purposes: http://natalia. grabar.free.fr/resources.php#rated. Figure 1 : 1Global evolution of percentage of words per caterogy. Figure 2 :Figure 3 : 23Evolution of percentage of words ending with -ite in each category. 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From ranked words to dependency trees: two-stage unsupervised non-projective dependency parsing

Usually unsupervised dependency parsing tries to optimize the probability of a corpus by modifying the dependency model that was presumably used to generate the corpus. In this article we explore a different view in which a dependency structure is among other things a partial order on the nodes in terms of centrality or saliency. Under this assumption we model the partial order directly and derive dependency trees from this order. The result is an approach to unsupervised dependency parsing that is very different from standard ones in that it requires no training data. Each sentence induces a model from which the parse is read off. Our approach is evaluated on data from 12 different languages. Two scenarios are considered: a scenario in which information about part-of-speech is available, and a scenario in which parsing relies only on word forms and distributional clusters. Our approach is competitive to state-of-the-art in both scenarios.

From ranked words to dependency trees: two-stage unsupervised non-projective dependency parsing Association for Computational LinguisticsCopyright Association for Computational LinguisticsJune 2011. 2011 Anders Søgaard soegaard@hum.ku.dk Center for Language Technology University of Copenhagen From ranked words to dependency trees: two-stage unsupervised non-projective dependency parsing Proceedings of the TextGraphs-6 Workshop the TextGraphs-6 WorkshopPortland, Oregon, USAAssociation for Computational LinguisticsJune 2011. 2011 Usually unsupervised dependency parsing tries to optimize the probability of a corpus by modifying the dependency model that was presumably used to generate the corpus. In this article we explore a different view in which a dependency structure is among other things a partial order on the nodes in terms of centrality or saliency. Under this assumption we model the partial order directly and derive dependency trees from this order. The result is an approach to unsupervised dependency parsing that is very different from standard ones in that it requires no training data. Each sentence induces a model from which the parse is read off. Our approach is evaluated on data from 12 different languages. Two scenarios are considered: a scenario in which information about part-of-speech is available, and a scenario in which parsing relies only on word forms and distributional clusters. Our approach is competitive to state-of-the-art in both scenarios. Introduction Unsupervised dependency parsers do not achieve the same quality as supervised or semi-supervised parsers, but in some situations precision may be less important compared to the cost of producing manually annotated data. Moreover, unsupervised dependency parsing is attractive from a theoretical point of view as it does not rely on a particular style of annotation and may potentially provide insights about the difficulties of human language learning. Unsupervised dependency parsing has seen rapid progress recently, with error reductions on English (Marcus et al., 1993) of about 15% in six years (Klein and Manning, 2004;Spitkovsky et al., 2010), and better and better results for other languages (Gillenwater et al., 2010;Naseem et al., 2010), but results are still far from what can be achieved with small seeds, language-specific rules (Druck et al., 2009) or using cross-language adaptation (Smith and Eisner, 2009;Spreyer et al., 2010). The standard method in unsupervised dependency parsing is to optimize the overall probability of the corpus by assigning trees to its sentences that capture general patterns in the distribution of part-ofspeech (POS). This happens in several iterations over the corpus. This method requires clever initialization, which can be seen as a kind of minimal supervision. State-of-the-art unsupervised dependency parsers, except Seginer (2007), also rely on manually annotated text or text processed by supervised POS taggers. Since there is an intimate relationship between POS tagging and dependency parsing, the POS tags can also be seen as a seed or as partial annotation. Inducing a model from the corpus is typically a very slow process. This paper presents a new and very different approach to unsupervised dependency parsing. The parser does not induce a model from a big corpus, but with a few exceptions only considers the sentence in question. It does use a larger corpus to induce distributional clusters and a ranking of key words in terms of frequency and centrality, but this is computationally efficient and is only indirectly related to the subsequent assignment of dependency structures to sentences. The obvious advantage of not relying on training data is that we do not have to worry about whether the test data reflects the same distribution as the target data (domain adaptation), and since our models are much smaller, parsing will be very fast. The parser assigns a dependency structure to a sequence of words in two stages. It first decorates the n nodes of what will become our dependency structure with word forms and distributional clusters, constructs a directed acyclic graph from the nodes in O(n 2 ), and ranks the nodes using iterative graph-based ranking (Page and Brin, 1998). Subsequently, it constructs a tree from the ranked list of words using a simple O(n log n) parsing algorithm. Our parser is evaluated on the selection of 12 dependency treebanks also used in Gillenwater et al. (2010). We consider two cases: parsing raw text and parsing text with information about POS. Strictly unsupervised dependency parsing is of course a more difficult problem than unsupervised dependency parsing of manually annotated POS sequences. Nevertheless our strictly unsupervised parser, which only sees word forms, performs significantly better than structural baselines, and it outperforms the standard POS-informed DMV-EM model (Klein and Manning, 2004) on 3/12 languages. The full parser, which sees manually annotated text, is competitive to state-of-the-art models such as E-DMV PR AS 140 (Gillenwater et al., 2010). 1 Preliminaries The observed variables in unsupervised dependency parsing are a corpus of sentences s = s 1 , . . . , s n where each word w j in s i is associated with a POS tag p j . The hidden variables are dependency structures t = t 1 , . . . , t n where s i labels the vertices of t i . Each vertex has a single incoming edge, possibly except one called the root of the tree. In this work and in most other work in dependency parsing, we introduce an artificial root node so that all vertices decorated by word forms have an incoming edge. A dependency structure such as the one in Figure 1 is thus a tree decorated with labels and augmented with a linear order on the nodes. Each edge (i, j) is referred to as a dependency between a head word w i and a dependent word w j and sometimes written w i → w j . Let w 0 be the artificial root of the dependency structure. We use → + to denote the transitive closure on the set of edges. Both nodes and edges are typically labeled. Since a dependency structure is a tree, it satisfies the following three constraints: A dependency structure over a sentence s : w 1 , . . . , w n is connected, i.e.: ∀w i ∈ s.w 0 → + w i A dependency structure is also acyclic, i.e.: ¬∃w i ∈ s.w i → + w i Finally, a dependency structure is single-headed, i.e.: ∀w i .∀w j .(w 0 → w i ∧ w 0 → w j ) ⇒ w i = w j If we also require that each vertex other than the artificial root node has an incoming edge we have a complete characterization of dependency structures. In sum, a dependency structure is a tree with a linear order on the leaves where the root of the tree for practical reasons is attached to an artificial root node. The artificial root node makes it easier to implement parsing algorithms. Finally, we define projectivity, i.e. whether the linear order is projective wrt. the dependency tree, as the property of dependency trees that if w i → w j it also holds that all words in between w i and w j are dominated by w i , i.e. w i → + w k . Intuitively, a projective dependency structure contains no crossing edges. Projectivity is not a necessary property of dependency structures. Some dependency structures are projective, others are not. Most if not all previous work in unsupervised dependency parsing has focused on projective dependency parsing, building on work in context-free parsing, but our parser is guaranteed to produce well-formed nonprojective dependency trees. Non-projective parsing algorithms for supervised dependency parsing have, for example, been presented in McDonald et al. (2005) and Nivre (2009). Related work Dependency Model with Valence (DMV) by Klein and Manning (2004) was the first unsupervised dependency parser to achieve an accuracy for manually POS-tagged English above a right-branching baseline. DMV is a generative model in which the sentence root is generated and then each head recursively generates its left and right dependents. For each s i ∈ s, t i is assumed to have been built the following way: The arguments of a head h in direction d are generated one after another with the probability that no more arguments of h should be generated in direction d conditioned on h, d and whether this would be the first argument of h in direction d. The POS tag of the argument of h is generated given h and d. Klein and Manning (2004) use expectation maximization (EM) to estimate probabilities with manually tuned linguistically-biased priors. use contrastive estimation instead of EM, while Smith and Eisner (2006) use structural annealing which penalizes long-distance dependencies initially, gradually weakening the penalty during training. Cohen et al. (2008) use Bayesian priors (Dirichlet and Logistic Normal) with DMV. All of the above approaches to unsupervised dependency parsing build on the linguistically-biased priors introduced by Klein and Manning (2004). In a similar way Gillenwater et al. (2010) try to penalize models with a large number of distinct dependency types by using sparse posteriors. They evaluate their system on 11 treebanks from the CoNLL 2006 Shared Task and the Penn-III treebank and achieve state-of-the-art performance. An exception to using linguistically-biased priors is Spitkovsky et al. (2009) who use predictions on sentences of length n to initialize search on sentences of length n + 1. In other words, their method requires no manual tuning and bootstraps itself on increasingly longer sentences. A very different, but interesting, approach is taken in Brody (2010) who use methods from unsupervised word alignment for unsupervised dependency parsing. In particular, he sees dependency parsing as directional alignment from a sentence (possible dependents) to itself (possible heads) with the modification that words cannot align to themselves; following Klein and Manning (2004) and the subsequent papers mentioned above, Brody (2010) considers sequences of POS tags rather than raw text. Results are below state-of-the-art, but in some cases better than the DMV model. Ranking dependency tree nodes The main intuition behind our approach to unsupervised dependency parsing is that the nodes near the root in a dependency structure are in some sense the most important ones. Semantically, the nodes near the root typically express the main predicate and its arguments. Iterative graph-based ranking (Page and Brin, 1998) was first used to rank webpages according to their centrality, but the technique has found wide application in natural language processing. Variations of the algorithm presented in Page and Brin (1998) have been used in keyword extraction and extractive summarization (Mihalcea and Tarau, 2004), word sense disambiguation (Agirre and Soroa, 2009), and abstractive summarization (Ganesan et al., 2010). In this paper, we use it as the first step in a two-step unsupervised dependency parsing procedure. The parser assigns a dependency structure to a sequence of words in two stages. It first decorates the n nodes of what will become our dependency structure with word forms and distributional clusters, constructs a directed acyclic graph from the nodes in O(n 2 ), and ranks the nodes using iterative graph-based ranking. Subsequently, it constructs a tree from the ranked list of words using a simple O(n log n) parsing algorithm. This section describes the graph construction step in some detail and briefly describes the iterative graph-based ranking algorithm used. The first step, however, is assigning distributional clusters to the words in the sentence. We use a hierarchical clustering algorithm to induce 500 clusters from the treebanks using publicly available software. 2 This procedure is quadratic in the number of clusters, but linear in the size of the corpus. The cluster names are bitvectors (see Figure 1). Edges The text graph is now constructed by adding different kinds of directed edges between nodes. The edges are not weighted, but multiple edges between nodes will make transitions between these nodes in iterative graph-based ranking more likely. The different kinds of edges play the same role in our model as the rule templates in the DMV model, and they are motivated below. Some of the edge assignments discussed below may seem rather heuristic. The edge template was developed on development data from the English Penn-III treebank (Marcus et al., 1993). Our edge selection was incremental considering first an extended set of candidate edges with arbitrary parameters and then considering each edge type at a time. If the edge type was helpful, we optimized any possible parameters (say context windows) and went on to the next edge type: otherwise we disregarded it. 3 Following data set et al. (2010), we apply the best setting for English to all other languages. Vine edges. motivate a vine parsing approach to supervised dependency parsing arguing that language users have a strong preference for short dependencies. Reflecting preference for short dependencies, we first add links between all words and their neighbors and neighbors' neighbors. This also guarantees that the final graph is connected. Keywords and closed class words. We use a keyword extraction algorithm without stop word lists to extract non-content words and the most important content words, typically nouns. The algorithm is a crude simplification of TextRank (Mihalcea and Tarau, 2004) that does not rely on linguistic resources, so that we can easily apply it to low-resource languages. Since we do not use stop word lists, highly ranked words will typically be non-content words, followed by what is more commonly thought of as keywords. Immediate neighbors to top-100 words are linked to these words. The idea is that noncontent words may take neighboring words as arguments, but dependencies are typically very local. The genuine keywords, ranked 100-1000, may be heads of dependents further away, and we therefore add edges between these words w i and their neighboring words w j if |i − j| ≤ 4. Head-initial/head-final. It is standard in unsupervised dependency parsing to compare against a structural baseline; either left-attach, i.e. all words attach to their left neighbor, or right-attach. Which structural baseline is used depends on the language in question. It is thus assumed that we know enough about the language to know what structural baseline performs best. It is therefore safe to incorporate this knowledge in our unsupervised parsers; our parsers are still as "unsupervised" as our baselines. If a language has a strong left-attach baseline, like Bulgarian, the first word in the sentence is likely to be very central for reasons of economy of processing. The language is likely to be head-initial. On the other hand, if a language has a strong right-attach baseline, like Turkish, the last word is likely to be central. The language is likely to be head-final. Some languages like Slovene have strong (< 20%) leftattach and right-attach baselines, however. We incorporate the knowledge that a language has a strong left-attach or right-attach baseline if more than one third of the dependencies are attachments to a immediate left, resp. right, neighbor. Specifically, we add edges from all nodes to the first element in the sentence if a language has a strong left-attach baseline; and from all edges to the last (non-punctuation) element in the sentence if a language has a strong right-attach baseline. Word inequality. An edge is added between two words if they have different word forms. It is not very likely that a dependent and a head have the same word form. Cluster equality. An edge is added between two words if they are neighbors or neighbors' neighbors and belong to the same clusters. If so, the two words may be conjoined. Morphological inequality. If two words w i , w j in the same context (|i − j| ≤ 4) share prefix or suffix, i.e. the first or last three letters, we add an edge between them. Edges using POS Verb edges. All words are attached to all words with a POS tag beginning with "V. . . ". Finally, when we have access to POS information, we do not rely on vine edges besides left-attach, and we do not rely on keyword edges or suffix edges either. Ranking Given the constructed graph we rank the nodes using the algorithm in Page and Brin (1998), also known as PageRank. The input to this algorithm is any directed graph G = E, V and the output is an assignment PR : V → R of a score, also referred to as PageRank, to each vertex in the graph such that all scores sum to 1. A simplified version of PageRank can be defined recursively as: PR(v) = Σ w∈Bv PR(w) L(w) where B v is the set of vertices such that (w, v) ∈ E, and L(w) is the number of outgoing links from w, i.e. |{(u, u ′ )|(u, u ′ ) ∈ E, u = w}|. In addition to this, Page and Brin (1998) introduces a socalled damping factor to reflect that fact that Internet users do not continue crawling web sites forever, but restart, returning to random web sites. This influences centrality judgments and therefore should be reflected in the probability assignment. Since there is no obvious analogue of this in our case, we simplify the PageRank algorithm and do not incorporate damping (or equivalent, set the damping factor to 1.0). Note, by the way, that although our graphs are non-weighted and directed, like a graph of web pages and hyperlinks (and unlike the text graphs in Mihalcea and Tarau (2004), for example), several pairs of nodes may be connected by multiple edges, making a transition between them more probable. Multiple edges provide a coarse weighting of the underlying minimal graph. Example In Figure 1, we see an example graph of word nodes, represented as a matrix, and a derived dependency structure. 4 We see that there are four edges from The to market and six from The to crumbles, for example. We then compute the PageRank of each node using the algorithm described in Page and Brin (1998); see also Figure 1. The PageRank values rank the nodes or the words. In Sect. 3, we describe a method for building a dependency tree from 4 The dependency structure in Figure 1 contains dependency labels such as 'SBJ' and 'ROOT'. These are just included for readability. We follow the literature on unsupervised dependency parsing and focus only on unlabeled dependency parsing. Figure 1: Graph, pagerank (PR) and predicted dependency structure for sentence 5, PTB-III Sect. 23. a ranking of the nodes. This method will produce the correct analysis of this sentence; see Figure 1. This is because the PageRank scores reflect syntactic superiority; the root of the sentence typically has the highest rank, and the least important nodes are ranked lowly. From ranking of nodes to dependency trees Consider the example in Figure 1 again. Once we have ranked the nodes in our dependency structure, we build a dependency structure from it using the parsing algorithm in Figure 2. The input of the graph is a list of ranked words π = n 1 , . . . , n m , where each node n i corresponds to a sentence position n pr2ind(i) decorated by a word form w pr2ind (i) , where pr2ind : {1, . . . , m} → {1, . . . , m} is a mapping from rank to sentence position. The interesting step in the algorithm is the head selection step. Each word is assigned a head taken from all the previously used heads and the word to which a head was just assigned. Of these words, we simply select the closest head. If two possible heads are equally close, we select the one with highest PageRank. Our parsing algorithm runs in O(n log n), since it runs over the ranked words in a single pass considering only previously stored words as possible heads, and guarantees connectivity, acyclicity and single-headedness, and thus produces well-formed non-projective dependency trees. To see this, remember that wellformed dependency trees are such that all nodes but the artificial root nodes have a single incoming edge. This follows immediately from 1: π = n 1 , . . . , n m # the ranking of nodes 2: H = n 0 # possible heads 3: D = ∅ # dependency structure 4: pr2ind : {1, . . . , m} → {1, . . . , m} # a mapping from rank to sentence position 5: for n i ∈ π do the fact that each node is assigned a head (line 11). Furthermore, the dependency tree must be acyclic. This follows immediately from the fact that a word can only attach to a word with higher rank than itself. Connectivity follows from the fact that there is an artificial root node and that all words attach to this node or to nodes dominated by the root node. Finally, we ensure single-headedness by explicitly disregarding the root node once we have attached the node with highest rank to it (line 6-7). Our parsing algorithm does not guarantee projectivity, since the iterative graph-based ranking of nodes can permute the nodes in any order. Experiments We use exactly the same experimental set-up as Gillenwater et al. (2010). The edge model was developed on development data from the English Penn-III treebank (Marcus et al., 1993), and we evaluate on Sect. 23 of the English treebanks and the test sections of the remaining 11 treebanks, which were all used in the CoNLL-X Shared Task (Buchholz and Marsi, 2006). Gillenwater et al. (2010) for some reason did not evaluate on the Arabic and Chinese treebanks also used in the shared task. We also follow Gillenwater et al. (2010) in only evaluating our parser on sentences of at most 10 non-punctuation words and in reporting unlabeled attachment scores excluding punctuation. Strictly unsupervised dependency parsing We first evaluate the strictly unsupervised parsing model that has no access to POS information. Since we are not aware of other work in strictly unsupervised multi-lingual dependency parsing, so we compare against the best structural baseline (left-attach or right-attach) and the standard DMV-EM model of Klein and Manning (2004). The latter, however, has access to POS information and should not be thought of as a baseline. Results are presented in Figure 3. It is interesting that we actually outperform DMV-EM on some languages. On average our scores are significantly better (p < 0.01) than the best structural baselines (3.8%), but DMV-EM with POS tags is still 3.0% better than our strictly unsupervised model. For English, our system performs a lot worse than Seginer (2007). Unsupervised dependency parsing (standard) We then evaluate our unsupervised dependency parser in the more standard scenario of parsing sentences annotated with POS. We now compare ourselves to two state-of-the-art models, namely DMV PR-AS 140 and E-DMV PR-AS 140 (Gillenwater et al., 2010). Finally, we also report results of the IBM model 3 proposed by Brody (2010) Our results are on average significantly better than DMV PR-AS 140 (2.5%), and better than DMV PR-AS 140 on 8/12 languages. E-DMV PR-AS 140 is slightly better than our model on average (1.3%), but we still obtain better results on 6/12 languages. Our results are a lot better than IBM-M3. Naseem et al. (2010) report better results than ours on Portuguese, Slovene, Spanish and Swedish, but worse on Danish. Error analysis In our error analysis, we focus on the results for German and Turkish. We first compare the results of the strictly unsupervised model on German with the results on German text annotated with POS. The main difference between the two models is that more links to verbs are added to the sentence graph prior to ranking nodes when parsing text annotated with POS. For this reason, the latter model improves considerably in attaching verbs compared to the strictly unsupervised model: While the strictly unsupervised model is about as Figure 5: Predicted dependency structures for sentence 4 in the German test section; strictly unsupervised (above) and standard (below) approach. Red arcs show wrong decisions. good at attaching nouns as the model with POS, it is much worse attaching verbs. Since more links to verbs are added, verbs receive higher rank, and this improves f-scores for attachments to the artificial root node: This is also what helps the model with POS when parsing the example sentence in Figure 5. The POSinformed parser also predicts longer dependencies. The same pattern is observed in the Turkish data, but perhaps less dramatically so: acc strict-unsup unsup Noun 43% 42% Verb 41% 51% The increase in accuracy is again higher with verbs than with nouns, but the error reduction was higher for German. The parsers predict more long dependencies for Turkish than for German; precision is generally good, but recall is very low. Conclusion We have presented a new approach to unsupervised dependency parsing. The key idea is that a depen- dency structure also expresses centrality or saliency, so by modeling centrality directly, we obtain information that we can use to build dependency structures. Our unsupervised dependency parser thus works in two stages; it first uses iterative graphbased ranking to rank words in terms of centrality and then constructs a dependency tree from the ranking. Our parser was shown to be competitive to state-of-the-art unsupervised dependency parsers. Figure 2 : 2′ = arg min n j ∈H[c:] |pr2ind (i) − pr2ind (j)| # select head of w j 12: H = n i ∪ H # make n i a possible head 13: D = {(w pr2ind (i) ← w pr2ind (j ′ ) )} ∪ D # add new edge to D 14: end for 15: return D Parsing algorithm. Figure 3 : 3Unlabeled attachment scores (in %) on raw text. (EM baseline has access to POS.) posal we are aware of that departs significantly from the DMV model. The results are presented in Figure 4. Figure 4 : 4Unlabeled attachment scores (in %) on text annotated with POS. for unsupervised dependency parsing, since this is the only recent pro-baseline EM ours Bulgarian 37.7 37.8 41.9 Czech 32.5 29.6 28.7 Danish 43.7 47.2 43.7 Dutch 38.7 37.1 33.1 English 33.9 45.8 36.1 German 27.2 35.7 36.9 Japanese 44.7 52.8 56.5 Portuguese 35.5 35.7 35.2 Slovene 25.5 42.3 30.0 Spanish 27.0 45.8 38.4 Swedish 30.6 39.4 34.5 Turkish 36.6 46.8 45.9 AV 34.5 41.3 38.3 DMV PR-AS 140 E-DMV PR-AS 140 ours IBM-M3Bulgarian 54.0 59.8 52.5 Czech 32.0 54.6 42.8 Danish 42.4 47.2 55.2 41.9 Dutch 37.9 46.6 49.4 35.3 English 61.9 64.4 50.2 39.3 German 39.6 35.7 50.4 Japanese 60.2 59.4 58.3 Portuguese 47.8 49.5 52.8 Slovene 50.3 51.2 44.1 Spanish 62.4 57.9 52.1 Swedish 38.7 41.4 45.5 Turkish 53.4 56.9 57.9 AV 48.4 52.2 50.9 Naseem et al. (2010) obtain slightly better results, but only evaluate on six languages. 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Competitive Grammar Writing *

Just as programming is the traditional introduction to computer science, writing grammars by hand is an excellent introduction to many topics in computational linguistics. We present and justify a well-tested introductory activity in which teams of mixed background compete to write probabilistic context-free grammars of English. The exercise brings together symbolic, probabilistic, algorithmic, and experimental issues in a way that is accessible to novices and enjoyable.

Competitive Grammar Writing * Jason Eisner Department of Computer Science Johns Hopkins University Baltimore 21218MDUSA Noah A Smith nasmith@cs.cmu.edu Language Technologies Institute Carnegie Mellon University Pittsburgh 15213PAUSA Competitive Grammar Writing * Just as programming is the traditional introduction to computer science, writing grammars by hand is an excellent introduction to many topics in computational linguistics. We present and justify a well-tested introductory activity in which teams of mixed background compete to write probabilistic context-free grammars of English. The exercise brings together symbolic, probabilistic, algorithmic, and experimental issues in a way that is accessible to novices and enjoyable. Introduction We describe a hands-on group activity for novices that introduces several central topics in computational linguistics (CL). While the task is intellectually challenging, it requires no background other than linguistic intuitions, no programming, 1 and only a very basic understanding of probability. The activity is especially appropriate for mixed groups of linguists, computer scientists, and others, letting them collaborate effectively on small teams and learn from one another. A friendly competition among the teams makes the activity intense and enjoyable and introduces quantitative evaluation. Task Overview Each 3-person team is asked to write a generative context-free grammar that generates as much of En- * This work was supported by NSF award 0121285, "ITR/IM+PE+SY: Summer Workshops on Human Language Technology: Integrating Research and Education," and by a Fannie and John Hertz Foundation Fellowship to the second author. We thank David A. Smith and Markus Dreyer for coleading the lab in 2004-2007 and for implementing various improvements in 2004-2007 and for providing us with data from those years. The lab has benefited over the years from feedback from the participants, many of whom attended the JHU summer school thanks to the generous support of NAACL. We also thank the anonymous reviewers for helpful comments. 1 In our setup, students do need the ability to invoke scripts and edit files in a shared directory, e.g., on a Unix system. glish as possible (over a small fixed vocabulary). Obviously, writing a full English grammar would take years even for experienced linguists. Thus each team will only manage to cover a few phenomena, and imperfectly. To encourage precision but also recall and linguistic creativity, teams are rewarded for generating sentences that are (prescriptively) grammatical but are not anticipated by other teams' grammars. This somewhat resembles scoring in the Boggle word game, where players are rewarded for finding valid words in a grid that are not found by other players. A final twist is that the exercise uses probabilistic context-free grammars (PCFGs); the actual scoring methods are based on sampling and cross-entropy. Each team must therefore decide how to allocate probability mass among sentences. To avoid assigning probability of zero when attempting to parse another team's sentences, a team is allowed to "back off" (when parsing) to a simpler probability model, such as a part-of-speech bigram model, also expressed as a PCFG. Setting We have run this activity for six consecutive years, as a laboratory exercise on the very first afternoon of an intensive 2-week summer school on various topics in human language technology. 2 We allot 3.5 hours in this setting, including about 15 minutes for setup, 30 minutes for instructions, 15 minutes for evaluation, and 30 minutes for final discussion. The remaining 2 hours is barely enough time for team members to get acquainted, understand the requirements, plan a strategy, and make a small dent in the problem. Nonetheless, participants consistently tell us that the exercise is enjoyable and pedagogically effective, almost always voting to stay an extra hour to make further progress. Our 3-person teams have consisted of approximately one undergraduate, one junior graduate student, and one more senior graduate student. If possible, each team should include at least one member who has basic familiarity with some syntactic phenomena and phrasal categories. Teams that wholly lack this experience have been at a disadvantage in the time-limited setting. Resources for Instructors We will maintain teaching materials at http: //www.clsp.jhu.edu/grammar-writing, for both the laboratory exercise version and for homework versions: scripts, data, instructions for participants, and tips for instructors. While our materials are designed for participants who are fluent in English, we would gladly host translations or adaptations into other languages, as well as other variants and similar assignments. Why Grammar Writing? A computer science curriculum traditionally starts with programming, because programming is accessible, hands-on, and necessary to motivate or understand most other topics in computer science. We believe that grammar writing should play the same role in computational linguistics-as it often did before the statistical revolution 3 -and for similar reasons. Grammar writing remains central because many theoretical and applied CL topics center around grammar formalisms. Much of the field tries to design expressive formalisms (akin to programming languages); solve linguistic problems within them (akin to programming); enrich them with probabilities; process them with efficient algorithms; learn them from data; and connect them to other modules in the linguistic processing pipeline. 3 The first author was specifically inspired by his experience writing a grammar in Bill Woods's NLP course at Harvard in 1987. An anonymous reviewer remarks that such assignments were common at the time. Our contributions are to introduce statistical and finite-state elements, to make the exercise into a game, and to provide reusable instructional materials. Of course, there are interesting grammar formalisms at all levels of language processing. One might ask why syntax is a good level at which to begin education in computational linguistics. First, starting with syntax establishes at the start that there are formal and computational methods specific to natural language. Computational linguistics is not merely a set of applied tasks to be solved with methods already standardly taught in courses on machine learning, theory of computation, 4 or knowledge representation. Second, we have found that syntax captures students' interest rapidly. They quickly appreciate the linguistic phenomena, see that they are non-trivial, and have little trouble with the CFG formalism. Third, beginning specifically with PCFGs pays technical dividends in a CL course. Once one understands PCFG models, it is easy to understand the simpler finite-state models (including n-gram models, HMMs, etc.) and their associated algorithms, either by analogy or by explicit reduction to special cases of PCFGs. CFGs are also a good starting point for more complex syntactic formalisms (BNF, categorial grammars, TAG, LFG, HPSG, etc.) and for compositional semantics. Indeed, our exercise motivates these more complex formalisms by forcing students to work with the more impoverished PCFG formalism and experience its limitations. Educational Goals of the Exercise Our grammar-writing exercise is intended to serve as a touchstone for discussion of many subsequent topics in NLP and CL (which are italicized below). As an instructor, one can often refer back later to the exercise to remind students of their concrete experience with a given concept. Generative probabilistic models. The first set of concepts concerns language models. These are easiest to understand as processes for generating text. Thus, we give our teams a script for generating random sentences from their grammar and their backoff model-a helpful way to observe the generative capacity and qualitative behavior of any model. Of course, in practice a generative grammar is most often run "backwards" to parse an observed sentence or score its inside probability, and we also give the teams a script to do that. Most teams do actually run these scripts repeatedly to test their grammars, since both scripts will be central in the evaluation (where sentences are randomly generated from one grammar and scored with another grammar). It is common for instructors of NLP to show examples of randomly-generated text from an n-gram model (e.g., Jurafsky and Martin, 2000, pp. 202-203), yet this amusing demonstration may be misinterpreted as merely illustrating the inadequacy of ngram models. Our use of a hand-crafted PCFG combined with a bigram-based (HMM) backoff grammar demonstrates that although the HMM is much worse at generating valid English sentences (precision), it is much better at robustly assigning nonzero probability when analyzing English sentences (recall). Finally, generative models do more than assign probability. They often involve linguistically meaningful latent variables, which can be recovered given the observed data. Parsing with an appropriate PCFG thus yields a intuitive and useful analysis (a syntactic parse tree), although only for the sentences that the PCFG covers. Even parsing with the simple backoff grammar that we initially provide yields some coarser analysis, in the form of a partof-speech tagging, since this backoff grammar is a right-branching PCFG that captures part-of-speech bigrams (for details see §1.1, §4.1, and Table 2). In fact, parsing with the backoff PCFG is isomorphic to Viterbi decoding in an HMM part-of-speech tagger, a topic that is commonly covered in NLP courses. Modeling grammaticality. The next set of concepts concerns linguistic grammaticality. During the evaluation phase of our exercise (see below), students must make grammaticality judgments on other teams' randomly generated sentences-which are usually nonsensical, frequently hard for humans to parse, and sometimes ungrammatical. This concrete task usually prompts questions from students about how grammaticality ought to be defined, both for purposes of the task and in principle. It could also be used to discuss why some of the sentences are so hard for humans to understand (e.g., garden-path and frequency effects) and what parsing strategies humans or machines might use. The exercise of modeling grammaticality with the CFG formalism, a formalism that appears elsewhere in the computer science curriculum, highlights some important differences between natural languages and formal languages. A natural language's true grammar is unknown (and may not even exist: perhaps the CFG formalism is inadequate). Rather, a grammar must be induced or constructed as an approximate model of corpus data and/or certain native-speaker intuitions. A natural language also differs from a programming language in including ambiguous sentences. Students observe that the parser uses probabilities to resolve ambiguity. Linguistic analysis. Grammar writing is an excellent way to get students thinking about linguistic phenomena (e.g., adjuncts, embedded sentences, wh-questions, clefts, point absorption of punctuation marks). It also forces students to think about appropriate linguistic formalisms. Many phenomena are tedious to describe within CFGs (e.g., agreement, movement, subcategorization, selectional restrictions, morphological inflection, and phonologicallyconditioned allomorphy such as a vs. an). They can be treated in CFGs only with a large number of repetitive rules. Students appreciate these problems by grappling with them, and become very receptive to designing expressive improvements such as feature structures and slashed categories. Parameter tuning. Students observe the effects of changing the rule probabilities by running the scripts. For example, teams often find themselves generating unreasonably long (or even infinite) sentences, and must damp down the probabilities of their recursive rules. Adjusting the rule probabilities can also change the score and optimal tree that are returned by the parser, and can make a big difference in the final evaluation (see §5). This appreciation for the role of numerical parameters helps motivate future study of machine learning in NLP. Quantitative evaluation. As an engineering pursuit, NLP research requires objective evaluation measures to know how well systems work. Our first measure is the precision of each team's probabilistic grammar: how much of its probability mass is devoted to sentences that are truly grammatical? Estimating this requires human grammaticality judgments on a random sample C of sentences generated from all teams' grammars. These binary judgments are provided by the participants themselves, introducing the notion of linguistic annotation (albeit of a very simple kind). Details are in §4.3.3. Our second measure is an upper-bound approximation to cross-entropy (or log-perplexity-in effect, the recall of a probability model): how well does each team's probabilistic model (this time including the backoff model of §1.1) anticipate unseen data that are truly grammatical? (Details in §4.3.3.) Note that in contrast to parsing competitions, we do not evaluate the quality of the parse trees (e.g., PARSEVAL). Our cross-entropy measure evaluates only the grammars' ability to predict word strings (language modeling). That is because we impose no annotation standard for parse trees: each team is free to develop its own theory of syntax. Furthermore, many sentences will only be parsable by the backoff grammar (e.g., a bigram model), which is not expected to produce a full syntactic analysis. The lesson about cross-entropy evaluation is slightly distorted by our peculiar choice of test data. In principle, the instructors might prepare a batch of grammatical sentences ahead of time and split them into a test set (used to evaluate cross-entropy at the end) and a development set (provided to the students at the start, so that they know which grammatical phenomena are important to handle). The activity could certainly be run in this way to demonstrate proper experimental design for evaluating a language model (discussed further in §5 and §6). We have opted for the more entertaining "Bogglestyle" evaluation described in §1.1, where teams try to stump one another by generating difficult test data, using the fixed vocabulary. Thus, we evaluate each team's cross-entropy on all grammatical sentences in the collection C, which was generated ex post facto from all teams' grammars. Important Details Data A few elements are provided to participants to get them started on their grammars. Vocabulary. The terminal vocabulary Σ consists of words from early scenes of the film Monty Python and the Holy Grail along with some inflected forms and function words, for a total vocabulary of 220 words. For simplicity, only 3rd-person pronouns, nouns, and verbs are included. All words are casesensitive for readability (as are the grammar nonterminals), but we do not require or expect sentenceinitial capitalization. All teams are restricted to this vocabulary, so that the sentences that they generate will not frustrate other teams' parsers with out-of-vocabulary words. However, they are free to use words in unexpected ways (e.g., using castle in its verbal sense from chess, or building up unusual constructions with the available function words). Initial lexicon. The initial lexical rules take the form T → w, where w ∈ Σ + and T ∈ T , with T being a set of six coarse part-of-speech tags: Noun: 21 singular nouns starting with consonants Misc: 183 other words, divided into several commented sections in the grammar file Students are free to change this tagset. They are especially encouraged to refine the Misc tag, which includes 3rd-person plural nouns (including some proper nouns), 3rd-person pronouns (nominative, accusative, and genitive), additional 3rd-person verb forms (plural present, past, stem, and participles), verbs that cannot be used transitively, modals, adverbs, numbers, adjectives (including some comparative and superlative forms), punctuation, coordinating and subordinating conjunctions, wh-words, and a few miscellaneous function words (to, not, 's). The initial lexicon is ambiguous: some words are associated with more than one tag. Each rule has weight 1, meaning that a tag T is equally likely to rewrite as any of its allowed nonterminals. Initial grammar. We provide the "S1" rules in Table 1, so that students can try generating and parsing Table 1: The S1 rules: a starting point for building an English grammar. The start symbol is S1. The weights in the first column will be normalized into generative probabilities; for example, the probability of expanding a given NP with NP → Det Nbar is actually 20/(20 + 1). sentences right away. The S1 and lexical rules together implement a very small CFG. Note that no Misc words can yet be generated. Indeed, this initial grammar will only generate some simple grammatical SVO sentences in singular present tense, although they may be unboundedly long and ambiguous because of recursion through Nbar and PP. Initial backoff grammar. The provided "S2" grammar is designed to assign positive probability to any string in Σ * (see §1.1). At least initially, this PCFG generates only right-branching structures. Its nonterminals correspond to the states of a weighted finite-state machine, with start state S2 and one state per element of T (the coarse parts of speech listed above). Table 2 shows a simplified version. From each state, transition into any state except the start state S2 is permitted, and so is stopping. These rules can be seen as specifying the transitions Arthur is the king . Arthur rides the horse near the castle . riding to Camelot is hard . do coconuts speak ? what does Arthur ride ? who does Arthur suggest she carry ? are they suggesting Arthur ride to Camelot ? Guinevere might have known . it is Sir Lancelot who knows Zoot ! neither Sir Lancelot nor Guinevere will speak of it . the Holy Grail was covered by a yellow fruit . do not speak ! Arthur will have been riding for eight nights . Arthur , sixty inches , is a tiny king . Arthur and Guinevere migrate frequently . he knows what they are covering with that story . the king drank to the castle that was his home . when the king drinks , Patsy drinks . in a bigram hidden Markov model (HMM) on partof-speech tags, whose emissions are specified by the lexical rules. Since each rule initially has weight 1, all part-of-speech sequences of a given length are equally likely, but these weights could be changed to arbitrary transition probabilities. Start rules. The initial grammar S1 and the initial backoff grammar S2 are tied together by a single symbol START, which has two production rules: 99 START → S1 1 START → S2 These two rules are obligatory, but their weights may be changed. The resulting model, rooted at START, is a mixture of the S1 and S2 grammars, where the weights of these two rules implement the mixture coefficients. This is a simple form of backoff smoothing by linear interpolation (Jelinek and Mercer, 1980). The teams are warned to pay special attention to these rules. If the weight of START → S1 is decreased relative to START → S2, then the model relies more heavily on the backoff model-perhaps a wise choice for keeping crossentropy small, if the team has little faith in S1's ability to parse the forthcoming data. Sample sentences. A set of 27 example sentences in Σ + (subset shown in Table 3) is provided for linguistic inspiration and as practice data on which to run the parser. Since only 2 of these sentences can be parsed with the initial S1 and lexical rules, there is plenty of room for improvement. A further development set is provided midway through the exercise ( §4.3.2). Computing Environment We now describe how the above data are made available to students along with some software. Scripts We provide scripts that implement two important capabilities for PCFG development. Both scripts are invoked with a set of grammar files specified on the command line (typically all of them, " * .gr"). A PCFG is obtained by concatenating these files and stripping their comments, then normalizing their rule weights into probabilities (see Table 1), and finally checking that all terminal symbols of this PCFG are legal words of the vocabulary Σ. The random generation script prints a sample of n sentences from this PCFG. The generator can optionally print trees or flat word sequences. A start symbol other than the default S1 may be specified (e.g., NP, S2, START, etc.), to allow participants to test subgrammars or the backoff grammar. 5 The parsing script prints the most probable parse tree for each sentence read from a file (or from the standard input). A start symbol may again be specified; this time the default is START. The parser also prints each sentence's probability, total number of parses, and the fraction of the probability that goes to the best parse. Tree outputs can be pretty-printed for readability. Collaborative Setup Teams of three or four sit at adjacent workstations with a shared filesystem. The scripts above are publicly installed; a handout gives brief usage instructions. The instructor and teaching assistant roam the room and offer assistance as needed. Each team works in its own shared directory. The Emacs editor will warn users who are simultaneously editing the same file. Individual participants tend to work on different sub-grammar files; all of a team's files can be concatenated (as * .gr) when the scripts are run. (The directory initially includes separate files for the S1 rules, S2 rules, and lexical rules.) To avoid unexpected interactions among these grammar fragments, students are advised to divide work based on nonterminals; e.g., one member of a team may claim jurisdiction over all rules of the form VP plural → · · ·. Activities Introductory Lecture Once students have formed themselves into teams and managed to log in at adjacent computers, we begin with an 30-minute introductory lecture. No background is assumed. We explain PCFGs simply by showing the S1 grammar and hand-simulating the action of the random sentence generator. We explain the goal of extending the S1 grammar to cover more of English. We explain how each team's precision will be evaluated by human judgments on a sample, but point out that this measure gives no incentive to increase coverage (recall). This motivates the "Boggle" aspect of the game, where teams must also be able to parse one another's grammatical sentences, and indeed assign them as high a probability as possible. We demonstrate how the parser assigns a probability by running it on the sentence that we earlier generated by hand. 6 We describe how the parser's probabilities are turned into a cross-entropy measure, and discuss strategy. Finally, we show that parsing a sentence that is not covered by the S1 grammar will lead to infinite cross-entropy, and we motivate the S2 backoff grammar as an escape hatch. Midpoint: Development data Once or more during the course of the exercise, we take a snapshot of all teams' S1 grammars and sample 50 sentences from each. The resulting collection of sentences, in random order, is made available to all teams as a kind of development data. While we do not filter for grammaticality as in the final evaluation, this gives all participants an idea of what they will be up against when it comes time to parse other teams' sentences. Teams are on their honor not to disguise the true state of their grammar at the time of the snapshot. Evaluation procedure Grammar development ends at an announced deadline. The grammars are now evaluated on the two measures discussed in §3. The instructors run a few scripts that handle most of this work. First, we generate a collection C by sampling 20 sentences from each team's probabilistic grammar, using S1 as the start symbol. (Thus, the backoff S2 grammar is not used for generation.) We now determine, for each team, what fraction of its 20-sentence sample was grammatical. The participants play the role of grammaticality judges. In our randomized double-blind procedure, each individual judge receives (in his or her team directory) a file of about 20 sentences from C, with instructions to delete the ungrammatical ones and save the file, implying coarse Boolean grammaticality judgments. 7 The files are constructed so that each sentence in C is judged by 3 different participants; a sentence is considered grammatical if ≥ 2 judges thinks that it is. We define the test corpusĈ to consist of all sentences in C that were judged grammatical. Each team's full grammar (using START as the start symbol to allow backoff) is used to parseĈ. This gives us the log 2 -probability of each sentence inĈ; the cross-entropy score is the sum of these log 2probabilities divided by the length ofĈ. Group discussion While the teaching assistant is running the evaluation scripts and compiling the results, the instructor leads a general discussion. Many topics are possible, according to the interests of the instructor and participants. For example: What linguistic phenomena did the teams handle, and how? Was the CFG formalism adequately expressive? How well would it work for languages other than English? What strategies did the teams adopt, based on the evaluation criteria? How were the weights chosen? How would you build a better backoff grammar? 8 How would you organize a real long-term effort to build a full English grammar? What would such a grammar be good for? Would you use any additional tools, data, or evaluation criteria? Table 4 shows scores achieved in one year (2002). Outcomes A valuable lesson for the students was the importance of backoff. None but the first two of the example sentences (Table 3) are parseable with the small S1 grammar. Thus, the best way to reduce perplexity was to upweight the S2 grammar and perhaps spend a little time improving its rules or weights. Teams that spent all of their time on the S1 grammar may have learned a lot about linguistics, but tended to score poorly on perplexity. Indeed, the winning team in a later year spent nearly all of their effort on the S2 grammar. They placed almost all their weight on the S2 grammar, whose rules they edited and whose parameters they estimated from the example sentences and development data. As for their S1 grammar, it generated only a small set of grammatical sentences with ob-scure constructions that other teams were unlikely to model well in their S1 grammars. This gave them a 100% precision score on grammaticality while presenting a difficult parsing challenge to other teams. This team gamed our scoring system, exploiting the idiosyncrasy that S2 would be used to parse but not to generate. (See §3 for an alternative system.) We conducted a post hoc qualitative survey of the grammars from teams in 2002. Teams were not asked to provide comments, and nonterminal naming conventions often tend to be inscrutable, but the intentions are mostly understandable. All 10 teams developed more fine-grained parts of speech, including coordinating conjunctions, modal verbs, number words, adverbs. 9 teams implemented singular and plural features on nouns and/or verbs, and 9 implemented the distinction between base, past, present, and gerund forms of verbs (or a subset of those). 7 teams brought in other features like comparative and superlative adjectives and personal vs. possessive pronouns. 4 teams modeled pronoun case. Team C created a "location" category. 7 teams explicitly tried to model questions, often including rules for do-support; 3 of those teams also modeled negation with do-insertion. 2 teams used gapped categories (team D used them extensively), and 7 teams used explicitX nonterminals, most commonly within noun phrases (following the initial grammar). Three teams used a rudimentary subcategorization frame model, distinguishing between sentence-taking, transitive, and intransitive verbs, with an exploded set of production rules as a result. Team D modeled appositives. The amount of effort teams put into weights varied, as well. Team A used 11 distinct weight values from 1 to 80, giving 79 rules weights > 1 (next closest was team 10, with 7 weight values in [1, 99] and only 43 up-weighted rules). Most teams set fewer than 25 rules' weights to something other than 1. Use as a Homework Assignment Two hours is not enough time to complete a good grammar. Our participants are ambitious but never come close to finishing what they undertake; Table 4 reflects incomplete work. Nonetheless, we believe that the experience still successfully fulfills many of the goals of §2-3 in a short time, and the participants enjoy the energy in a roomful of peers racing toward a deadline. The fact that the task is open-ended and clearly impossible keeps the competition friendly. An alternative would be to allot 2 weeks or more as a homework assignment, allowing teams to go more deeply into linguistic issues and/or backoff modeling techniques. A team's grade could be linked to its performance. In this setting, we recommend limiting the team size to 1 or 2 people each, since larger teams may not be able to find time or facilities to work side-by-side for long. This homework version of our exercise might helpfully be split into two assignments: Part 1 (non-competitive, smaller vocabulary). "Extend the initial S1 grammar to cover a certain small set of linguistic phenomena, as illustrated by a development set [e.g., Table 3]. You will be evaluated on the cross-entropy of your grammar on a test set that closely resembles the development set [see §3], and perhaps also on the acceptability of sentences sampled from your grammar (as judged by you, your classmates, or the instructor). You will also receive qualitative feedback on how correctly and elegantly your grammar solves the linguistic problems posed by the development set." Part 2 (competitive, full 220-word vocabulary). "Extend your S1 grammar from Part 1 to generate phenomena that stump other teams, and add an S2 grammar to avoid being stumped by them. You will be evaluated as follows . . . [see §4.3.3]." We have already experimented with simpler noncompetitive grammar-writing exercises (similar to Part 1) in our undergraduate NLP courses. Given two weeks, even without teammates, many students do a fine job of covering several non-trivial syntactic phenomena. These assignments are available for use by others (see §1.3). In some versions, students were asked to write their own random generator, judge their own sentences, explain how to evaluate perplexity, or guess why the S2 grammar was used. Conclusion We hope that other instructors can make use of these materials or ideas. Our competitive PCFG-writing game touches upon many core CL concepts, is challenging and enjoyable, allows collaboration, and is suitable for cross-disciplinary and intro courses. 8 singular proper nouns denoting people (including multiwords such as Sir Lancelot) VerbT: 6 3rd-person singular present transitive verbs Table 2 : 2The S2 rules (simplified here where T = {Noun, Misc}): a starting point for a backoff grammar. The start symbol is S2. The Noun nonterminal generates those phrases that start with Nouns. Its 3 rules mean that following a Noun, there is 1/3 probability each of stopping, continuing with another Noun (via Noun), or continuing with a Misc word (via Misc). Table 3 : 3Example sentences. Only the first two can be parsed by the initial S1 and lexical rules. Table 4 : 4Teams' evaluation scores in one year, and the number of new rules (not including weight changes) that they wrote. Only teams A and H modified the relative weights of the START rules (they used 80/20 and 75/25, respectively), giving them competitive perplexity scores. (Cross-entropy in this year was approximated by an upper bound that uses only the probability of each sentence's single best parse.) This 2-week course is offered as a prelude to the Johns Hopkins University summer research workshops, sponsored by the National Science Foundation and the Department of Defense. In recent years the course has been co-sponsored by the North American ACL. Courses on theory of computation do teach pushdown automata and CFGs, of course, but they rarely touch on parsing or probabilistic grammars, as this exercise does. Courses on compilers may cover parsing algorithms, but only for restricted grammar families such as unambiguous LR(1) grammars. For some PCFGs, the stochastic process implemented by the script has a nonzero probability of failing to terminate. This has not caused problems to date. The probability will be tiny, as a product of many rule probabilities. But it may be higher than expected, and students are challenged to guess why: there are additional parses beyond the one we hand-generated, and the parser sums over all of them. Judges are on their honor to make fair judgments rather than attempt to judge other teams' sentences ungrammatical. Moreover, such an attempt might be self-defeating, as they might unknowingly be judging some of their own team's sentences ungrammatical. E.g., training the model weights, extending it to trigrams, or introducing syntax into the S2 model by allowing it to invoke nonterminals of the S1 grammar. Interpolated estimation of Markov source parameters from sparse data. F Jelinek, R L Mercer, Proc. of Workshop on Pattern Recognition in Practice. of Workshop on Pattern Recognition in PracticeF. Jelinek and R. L. Mercer. 1980. Interpolated estima- tion of Markov source parameters from sparse data. In Proc. of Workshop on Pattern Recognition in Practice. Speech and Language Processing. D Jurafsky, J H Martin, Prentice HallD. Jurafsky and J.H. Martin. 2000. Speech and Lan- guage Processing. Prentice Hall.

Machine Learning for Enhancing Dementia Screening in Ageing Deaf Signers of British Sign Language May 2020 Xing Liang x.liang@greenwich.ac.uk Bencie Woll b.woll@ucl.ac.uk Epaminondas Kapetanios Anastasia Angelopoulou Reda Al IoT and Security Research Group Cognitive Computing Research Lab University of Greenwich UK Deafness Cognition and Language Research Centre University of Westminster UK University College London UK Machine Learning for Enhancing Dementia Screening in Ageing Deaf Signers of British Sign Language Proceedings of the 9th Workshop on the Representation and Processing of Sign Languages the 9th Workshop on the Representation and Processing of Sign LanguagesMarseilleMay 2020135Real-Time Hand TrackingFacial AnalysisBritish Sign LanguageDementiaConvolutional Neural Network Real-time hand movement trajectory tracking based on machine learning approaches may assist the early identification of dementia in ageing deaf individuals who are users of British Sign Language (BSL), since there are few clinicians with appropriate communication skills, and a shortage of sign language interpreters. In this paper, we introduce an automatic dementia screening system for ageing Deaf signers of BSL, using a Convolutional Neural Network (CNN) to analyse the sign space envelope and facial expression of BSL signers recorded in normal 2D videos from the BSL corpus. Our approach involves the introduction of a sub-network (the multi-modal feature extractor) which includes an accurate real-time hand trajectory tracking model and a real-time landmark facial motion analysis model. The experiments show the effectiveness of our deep learning based approach in terms of sign space tracking, facial motion tracking and early stage dementia performance assessment tasks. Introduction British Sign Language (BSL), is a natural human language, which, like other sign languages, uses movements of the hands, body and face for linguistic expression. Identifying dementia in BSL users, however, is still an open research field, since there is very little information available about the incidence or features of dementia among BSL users. This is also exacerbated by the fact that there are few clinicians with appropriate communication skills and experience working with the BSL-using population. Diagnosis of dementia is subject to the quality of cognitive tests and BSL interpreters alike. Hence, the Deaf community currently receives unequal access to diagnosis and care for acquired neurological impairments, with consequent poorer outcomes and increased care costs (Atkinson et al., 2002). In this context, we propose a methodological approach to initial screening that comprises several stages. The first stage of research focuses on analysing the motion patterns of the sign space envelope in terms of sign trajectory and sign speed by deploying a real-time hand movement trajectory tracking model (Liang et al., 2019) based on Open-Pose 1 library. The second stage involves the extraction of the facial expressions of deaf signers by deploying a realtime facial analysis model based on dlib library 2 to identify active and non-active facial expressions. Based on the differences in patterns obtained from facial and trajectory motion data, the further stage of research implements both VGG16 (Simonyan and Zisserman, 2015) and ResNet-50 (He et al., 2016) networks using transfer learning from image recognition tasks to incrementally identify and improve recognition rates for Mild Cognitive Impairment (MCI) (i.e. pre-dementia). Performance evaluation of the research work is based on data sets available from the Deafness 1 https://github.com/CMU-Perceptual-Computing-Lab/openpose 2 http://dlib.net/ Cognition and Language Research Centre (DCAL) at UCL, which has a range of video recordings of over 500 signers who have volunteered to participate in research. Figure 1 shows the pipeline and high-level overview of the network design. The paper is structured as follows: Section 2 gives an overview of the related work. Section 3 outlines the methodological approach followed by Section 4 with the discussion of experimental design and results. A conclusion provides a summary of the key contributions and results of this paper. Related Work Recent advances in computer vision and greater availability in medical imaging with improved quality have increased the opportunities to develop machine learning approaches for automated detection and quantification of diseases, such as Alzheimer's and dementia (Pellegrini et al., 2018). Many of these techniques have been applied to the classification of MR imaging, CT scan imaging, FDG-PET scan imaging or the combined imaging of above, by comparing patients with early stage disease to healthy controls, to distinguish different types or stages of disease and accelerated features of ageing (Spasova et al., 2019;Lu et al., 2018;Huang et al., 2019). In terms of dementia diagnosis (Astell et al., 2019), there have been increasing applications of various machine learning approaches, most commonly with imaging data for diagnosis and disease progression (Negin et al., 2018;Iizuka et al., 2019) and less frequently in non-imaging studies focused on demographic data, cognitive measures (Bhagyashree et al., 2018), and unobtrusive monitoring of gait patterns over time (Dodge et al., 2012). These and other real-time measures of function may offer novel ways of detecting transition phases leading to dementia, which could be another potential research extension to our toolkit, since the real-time hand trajectory tracking submodel has the potential to track a patient's daily walking Methodology In this paper, we present a multi-modal feature extraction sub-network inspired by practical clinical needs, together with the experimental findings associated with the subnetwork. The input to the system is short term clipped videos. Different extracted motion features are fed into the CNN network to classify a BSL signer as healthy or atypical. Performance evaluation of the research work is based on data sets available from the BSL Corpus 3 at DCAL UCL, a collection of 2D video clips of 250 Deaf signers of BSL from 8 regions of the UK; and two additional data sets: a set of data collected for a previous funded project 4 , and a set of signer data collected for the present study. Dataset From the video recordings, we selected 40 case studies of signers (20M, 20F) aged between 60 and 90 years; 21 are signers considered to be healthy cases based on their scores on the British Sign Language Cognitive Screen (BSL-CS); 9 are signers identified as having Mild Cognitive Impairment (MCI) on the basis of the BSL-CS; and 10 are signers diagnosed with MCI through clinical assessment. We consider those 19 cases as MCI (i.e. early dementia) cases, whether identified through the BSL-CS or clinically. As the video clip for each case is about 20 minutes in length, we segmented each into 4-5 short video clips -4 minutes in length -and fed the segmented short video clip to the multimodal feature extraction sub-network. In this way, we were able to increase the size of the dataset from 40 to 162 clips. Of the 162, 79 have MCI, and 83 are cognitively healthy. Real-time Hand Trajectory Tracking Model OpenPose, developed by Carnegie Mellon University, is one of the state-of-the-art methods for human pose estimation, processing images through a 2-branch multi-stage CNN (Cao et al., 2017). The real-time hand movement 3 BSL Corpus Project, https://bslcorpusproject.org/. 4 Overcoming obstacles to the early identification of dementia in the signing Deaf community A detailed evaluation of tracking performance is discussed in (Liang et al., 2019). The inputs to the system are brief clipped videos. We assume that the subjects are in front of the camera with only the head, upper body and arms visible; therefore, only 14 upper body parts in the image are outputted from the tracking model. These are: eyes, nose, ears, neck, shoulders, elbows, wrists, and hips. The hand movement trajectory is obtained via wrist joint motion trajectories. The curve of the hand movement trajectory is connected by the location of the wrist joint keypoints to track left-and right-hand limb movements across sequential video frames in a rapid and unique way. Figure 2, demonstrates the tracking process for the sign FARM. As shown in Figure 3, left-and right-hand trajectories obtained from the tracking model are also plotted by wrist location X and Y coordinates over time in a 2D plot. Figure 3 shows how hand motion changes over time, which gives a clear indication of hand movement speed (X-axis speed based on 2D coordinate changes, and Y-axis speed based on 2D coordinate changes). A spiky trajectory indicates more changes within a shorter period, thus faster hand movement. Real-time Facial Analysis Model The facial analysis model was implemented based on a facial landmark detector inside the Dlib library, in order to analyse a signer's facial expressions (Kazemi and Sullivan, 2014). The face detector uses the classic Histogram of Oriented Gradients (HOG) feature combined with a linear classifier, an image pyramid, and a sliding window detection scheme. The pre-trained facial landmark detector is used (Figure 4). The facial analysis model extracts subtle facial muscle movement by calculating the average Euclidean distance differences between the nose and right brow as d1, nose and left brow as d2,and upper and lower lips as d3 for a given signer over a sequence of video frames (Figure 4). The vector [d1, d2, d3] is an indicator of a signer's facial expression and is used to classify a signer as having an active or non-active facial expression. d1, d2, d3 = T t=1 |d t+1 − d t | T(1) where T = Total number of frames that facial landmarks are detected. Experiments and Analysis Experiments In our approach, we have used VGG16 and ResNet-50 as the base models, with transfer learning to transfer the parameters pre-trained for the 1000 object detection task on the ImageNet dataset to recognise hand movement trajectory images for early MCI screening. We run the experiments on a Windows desktop with two Nvidia GeForce GTX 1080Ti adapter cards and 3.3 GHz Intel Core i9-7900X CPU with 16 GB RAM. In the training process, videos of 40 participants have been segmented into short clips with 162 segmented cases, split into 80% for the training set and 20% for the test set. To validate the model performance, we also kept 6 cases separate (1 MCI and 5 healthy signers), segmented into 24 cases for performance validation. Due to the very small dataset, we train ResNet-50 as a classifier alone and fine tune the VGG 16 network by freezing the Convolutional (Covn) layers and two Fully Connected (FC) layers, and only retrain the last two layers. Subsequently, a softmax layer for binary classification is applied to discriminate the two labels: Healthy and MCI, producing two numerical values of which the sum becomes 1.0. During training, dropout was deployed in fully connected layers and EarlyStopping was used in both networks to avoid overfitting. Results Discussion During test and validation, accuracies and receiver operating characteristic (ROC) curves of the classification were calculated, and the network with the highest accuracy and area under ROC (AUC), that is VGG 16, was chosen as the final classifier. Table 1 summarises the results over 46 participants from both networks. The best performance metrics are achieved by VGG16 with test set accuracy of 87.8788%, which matches validation set accuracy of 87.5%. In Figure 5, feature extraction results show that in a greater number of cases a signer with MCI produces a sign trajectory that resembles a straight line rather than the spiky trajectory characteristic of a healthy signer. In other words, signers with MCI produced more static poses/pauses during signing, with a reduced sign space envelope as indicated by smaller amplitude differences between the top and bottom peaks of the X, Y trajectory lines. At the same time, the Euclidean distance d3 of healthy signers is larger than that of MCI signers, indicating active facial movements by healthy signers. This proves the clinical observation concept of differences between signers with MCI and healthy signers in the envelope of sign space and face movements, with the former using smaller sign space and limited facial expression. Conclusions We have outlined a methodological approach and developed a toolkit for an automatic dementia screening system for signers of BSL. As part of our methodology, we report the experimental findings for the multi-modal feature extractor sub-network in terms of sign trajectory and facial motion together with performance comparisons between different CNN models in ResNet-50 and VGG16. The experiments show the effectiveness of our deep learning based approach for early stage dementia screening. The results are validated against cognitive assessment scores with a test set performance of 87.88%, and a validation set performance of 87.5% over sub-cases. Figure 1 : 1The Proposed Pipeline for Dementia Screening pattern and pose recognition as well. Figure 2 : 2Real-Time Hand Trajectory Tracking for the Sign FARM trajectory tracking model is developed based on the Open-Pose Mobilenet Thin model (OpenPoseTensorFlow, 2019). Figure 3 :Figure 4 : 342D Left-and Right-Hand Trajectory of a Signer Facial Motion Tracking of a Signer to estimate the location of 68 (x,y) coordinates that map to facial features Astell, A., Bouranis, N., Hoey, J., Lindauer, A., Mihailidis, A., Nugent, C., and Robillardi, J. (2019). Technology and dementia: The future is now. In: Dementia and Geriatric Cognitive Disorders, 47(3):131-139. Figure 5 : 5Experiment Finding Table 1 : 1Performance Evaluation over VGG16 and RestNet-50 for early MCI screeningMethod 40 Participants 21 Healthy, 19 Early MCI 6 Participants 5 Healthy, 1 Early MCI Train Result (129 segmented cases) Test Result (33 segmented cases) Validation Result (24 segmented cases) ACC ACC ROC ACC ROC VGG 16 87.5969% 87.8788% 0.93 87.5% 0.96 ResNet-50 69.7674% 69.6970% 0.72 66.6667% 0.73 When sign language breaks down: Deaf people's access to language therapy in the uk. J Atkinson, J Marshall, A Thacker, B Woll, Deaf Worlds. 18Atkinson, J., Marshall, J., Thacker, A., and Woll, B. (2002). When sign language breaks down: Deaf people's access to language therapy in the uk. In: Deaf Worlds, 18:9-21. Diagnosis of dementia by machine learning methods in epidemiological studies: a pilot exploratory study from south india. S I Bhagyashree, K Nagaraj, M Prince, C Fall, Krishna , M , Social Psychiatry and Psychiatric Epidemiology. 531Bhagyashree, S. I., Nagaraj, K., Prince, M., Fall, C., and Krishna, M. (2018). Diagnosis of dementia by machine learning methods in epidemiological studies: a pilot ex- ploratory study from south india. In: Social Psychiatry and Psychiatric Epidemiology, 53(1):77-86. Realtime multi-person 2d pose estimation using part affinity fields. Z Cao, T Simon, S Wei, Y Sheikh, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Cao, Z., Simon, T., Wei, S., and Sheikh, Y. (2017). Real- time multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Com- puter Vision and Pattern Recognition (CVPR). In-home walking speeds and variability trajectories associated with mild cognitive impairment. H Dodge, N Mattek, D Austin, T Hayes, Kaye , J , Neurology78Dodge, H., Mattek, N., Austin, D., Hayes, T., and Kaye, J. (2012). In-home walking speeds and variability tra- jectories associated with mild cognitive impairment. In: Neurology, 78(24):1946-1952. Deep residual learning for image recognition. K He, X Zhang, S Ren, J Sun, Proceedings of Computer Vision and Pattern Recognition (CVPR). Computer Vision and Pattern Recognition (CVPR)He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep resid- ual learning for image recognition. In: Proceedings of Computer Vision and Pattern Recognition (CVPR). Diagnosis of alzheimer's disease via multi-modality 3d convolutional neural network. Y Huang, J Xu, Y Zhou, T Tong, X Zhuang, Adni , In: Front Neuroscience. 50913Huang, Y., Xu, J., Zhou, Y., Tong, T., Zhuang, X., and ADNI. (2019). Diagnosis of alzheimer's disease via multi-modality 3d convolutional neural network. In: Front Neuroscience, 13(509). . T Iizuka, M Fukasawa, M Kameyama, Iizuka, T., Fukasawa, M., and Kameyama, M. (2019). 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Phonemic transcription of low-resource tonal languages

Transcription of speech is an important part of language documentation, and yet speech recognition technology has not been widely harnessed to aid linguists. We explore the use of a neural network architecture with the connectionist temporal classification loss function for phonemic and tonal transcription in a language documentation setting. In this framework, we explore jointly modelling phonemes and tones versus modelling them separately, and assess the importance of pitch information versus phonemic context for tonal prediction. Experiments on two tonal languages, Yongning Na and Eastern Chatino, show the changes in recognition performance as training data is scaled from 10 minutes to 150 minutes. We discuss the findings from incorporating this technology into the linguistic workflow for documenting Yongning Na, which show the method's promise in improving efficiency, minimizing typographical errors, and maintaining the transcription's faithfulness to the acoustic signal, while highlighting phonetic and phonemic facts for linguistic consideration.

Phonemic transcription of low-resource tonal languages 2017. Dec 2017 Oliver Adams Trevor Cohn Graham Neubig Alexis Michaud Oliver Adams Trevor Cohn Graham Neubig Alexis Michaud Oliver Adams Computing and Information Systems The University of Melbourne Australia Trevor Cohn Computing and Information Systems The University of Melbourne Australia Graham Neubig Language Technologies Institute Carnegie Mellon University USA Alexis Michaud CNRS-LACITO National Center for Scientific Research France Phonemic transcription of low-resource tonal languages Brisbane, Australia2017. Dec 2017Submitted on 5 Dec 2017HAL Id: halshs-01656683 https://halshs.archives-ouvertes.fr/halshs-01656683 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Distributed under a Creative Commons Attribution -NonCommercial -ShareAlike| 4.0 International License To cite this version: Transcription of speech is an important part of language documentation, and yet speech recognition technology has not been widely harnessed to aid linguists. We explore the use of a neural network architecture with the connectionist temporal classification loss function for phonemic and tonal transcription in a language documentation setting. In this framework, we explore jointly modelling phonemes and tones versus modelling them separately, and assess the importance of pitch information versus phonemic context for tonal prediction. Experiments on two tonal languages, Yongning Na and Eastern Chatino, show the changes in recognition performance as training data is scaled from 10 minutes to 150 minutes. We discuss the findings from incorporating this technology into the linguistic workflow for documenting Yongning Na, which show the method's promise in improving efficiency, minimizing typographical errors, and maintaining the transcription's faithfulness to the acoustic signal, while highlighting phonetic and phonemic facts for linguistic consideration. Introduction Language documentation involves eliciting speech from native speakers, and transcription of these rich cultural and linguistic resources is an integral part of the language documentation process. However, transcription is very slow: it often takes a linguist between 30 minutes to 2 hours to transcribe and translate 1 minute of speech, depending on the transcriber's familiarity with the language and the difficulty of the content. This is a bottleneck in the standard documentary linguistics workflow: linguists accumulate considerable amounts of speech, but do not transcribe and translate it all, and there is a risk that untranscribed recordings could end up as "data graveyards" (Himmelmann, 2006, 4,12-13). There is clearly a need for "devising better ways for linguists to do their work" (Thieberger, 2016, 92). There has been work on low-resource speech recognition (Besacier et al., 2014), with approaches using cross-lingual information for better acoustic modelling (Burget et al., 2010;Vu et al., 2014;Xu et al., 2016;Müller et al., 2017) and language modelling (Xu and Fung, 2013). However, speech recognition technology has largely been ineffective for endangered languages since architectures based on hidden Markov models (HMMs), which generate orthographic transcriptions, require a large pronunciation lexicon and a language model trained on text. These speech recognition systems are usually trained on a variety of speakers and hundreds of hours of data (Hinton et al., 2012, 92), with the goal of generalisation to new speakers. Since large amounts of text are used for language model training, such systems often do not incorporate pitch information for speech recognition of tonal languages (Metze et al., 2013), as they can instead rely on contextual information for tonal disambiguation via the language model (Le and Besacier, 2009;Feng et al., 2012). In contrast, language documentation contexts often have just a few speakers for model training, and little text for language model training. However, there may be benefit even in a system that overfits to these speakers. If a phonemic recognition tool can provide a canvas transcription for manual correction and linguistic analysis, it may be possible to improve the leverage of linguists. The data collected in this semi-automated workflow can then be used as training data for further re-finement of the acoustic model, leading to a snowball effect of better and faster transcription. In this paper we investigate the application of neural speech recognition models to the task of phonemic and tonal transcription in a resourcescarce language documentation setting. We use the connectionist temporal classification (CTC) formulation (Graves et al., 2006) for the purposes of direct prediction of phonemes and tones given an acoustic signal, thus bypassing the need for a pronunciation lexicon, language model, and time alignments of phonemes in the training data. By drastically reducing the data requirements in this way, we make the use of automatic transcription technology more feasible in a language documentation setting. We evaluate this approach on two tonal languages, Yongning Na and Eastern Chatino (Cruz and Woodbury, 2006;Michaud, 2017). Na is a Sino-Tibetan language spoken in Southwest China with three tonal levels, High (H), Mid (M) and Low (L) and a total of seven tone labels. Eastern Chatino, spoken in Oaxaca, Mexico, has a richer tone set but both languages have extensive morphotonology. Overall estimates of numbers of speakers for Chatino and Na are similar, standing at about 40,000 for both (Simons and Fennig, 2017), but there is a high degree of dialect differentiation within the languages. The data used in the present study are from the Alawa dialect of Yongning Na, and the San Juan Quiahije dialect of Eastern Chatino; as a rule-of-thumb estimate, it is likely that these materials would be intelligible to a population of less than 10,000 (for details on the situation for Eastern Chatino, see Cruz (2011, 18-23)). Though a significant amount of Chatino speech has been transcribed (Chatino Language Documentation Project, 2017), its rich tone system and opposing location on the globe make it a useful point of comparison for our explorations of Na, the language for which automatic transcription is our primary practical concern. Though Na has previously had speech recognition applied in a pilot study (Do et al., 2014), phoneme error rates were not quantified and tone recognition was left as future work. We perform experiments scaling the training data, comparing joint prediction of phonemes and tones with separate prediction, and assessing the influence of pitch information versus phonemic context on phonemic and tonal prediction in the CTC-based framework. Importantly, we qualitatively evaluate use of this automation in the transcription of Na. The effectiveness of the approach has resulted in its incorporation into the linguist's workflow. Our open-source implementation is available online. 1 Model The underlying model used is a long shortterm memory (LSTM) recurrent neural network (Hochreiter and Schmidhuber, 1997) in a bidirectional configuration (Schuster and Paliwal, 1997). The network is trained with the connectionist temporal classification (CTC) loss function (Graves et al., 2006). Critically, this alleviates the need for alignments between speech features and labels in the transcription which we do not have. This is achieved through the use of a dynamic programming algorithm that efficiently sums over the probability of neural network output label that correspond to the gold transcription sequence when repeated labels are collapsed. The use of an underlying recurrent neural network allows the model to implicitly model context via the parameters of the LSTM, despite the independent frame-wise label predictions of the CTC network. It is this feature of the architecture that makes it a promising tool for tonal prediction, since tonal information is suprasegmental, spanning many frames (Mortensen et al., 2016). Context beyond the immediate local signal is indispensable for tonal prediction, and long-ranging context is especially important in the case of morphotonologically rich languages such as Na and Chatino. Past work distinguishes between embedded tonal modelling, where phoneme and tone labels are jointly predicted, and explicit tonal modelling, where they are predicted separately (Lee et al., 2002). We compare several training objectives for the purposes of phoneme and tone prediction. This includes separate prediction of 1) phonemes and 2) tones, as well as 3) jointly predict phonemes and tones using one label set. Figure 1 presents an example sentence from the Na corpus described in §3.1, along with an example of these three objectives. 0 sec 2.7 sec /tʰi˩˥ | go˧mi˧-dʑo˥ | tʰi˩˥ , ɑ˩ʁo˧ dʑo˩ tsɯ˩ | mv̩ ˩. |/ Quant à la soeur, elle demeurait à la maison, dit-on. As for the sister, she stayed at home. 而妹妹呢，留在家里。 Target label sequence: 1. tʰ i g o m i dʑ o tʰ i ɑ ʁ o dʑ o t s ɯ m v̩ 2. ˩˥ ˧ ˧ ˥ ˩˥ ˩ ˧ ˩ ˩ ˩ 3. tʰ i ˩˥ g o ˧ m i ˧ d ʑ o ˥ tʰ i ˩˥ ɑ ˩ ʁ o ˧ dʑ o ˩ t s ɯ ˩ m v̩ ˩ Experimental Setup We designed the experiments to answer these primary questions: 1. How do the error rates scale with respect to training data? 2. How effective is tonal modelling in a CTC framework? 3. To what extent does phoneme context play a role in tone prediction? 4. Does joint prediction of phonemes and tones help minimize error rates? We assess the performance of the systems as training data scales from 10 minutes to 150 minutes of a single Na speaker, and between 12 and 50 minutes for a single speaker of Chatino. Experimenting with this extremely limited training data gives us a sense of how much a linguist needs to transcribe before this technology can be profitably incorporated into their workflow. We evaluate both the phoneme error rate (PER) and tone error rate (TER) of models based on the same neural architecture, but with varying input features and output objectives. Input features include log Filterbank features 2 ( ), pitch features of Ghahremani et al. (2014) ( ), and a 2 41 log Filterbank features along with their first and second derivatives combination of both ( ). These input features vary in the amount of acoustic information relevant to tonal modelling that they include. The output objectives correspond to those discussed in §2: tones only ( ), phonemes only ( ), or jointly modelling both ( ). We denote combinations of input features and target labellings as ⟨ ⟩⇒⟨ ⟩. In case of tonal prediction we explore similar configurations to that of phoneme prediction, but with two additional points of comparison. The first is predicting tones given one-hot phoneme vectors ( ) of the gold phoneme transcription ( ⇒ ). The second predicts tones directly from pitch features ( ⇒ ). These important points of comparison serve to give us some understanding as to how much tonal information is being extracted directly from the acoustic signal versus the phoneme context. Data We explore application of the model to the Na corpus that is part of the Pangloss collection (Michailovsky et al., 2014). This corpus consists of around 100 narratives, constituting 11 hours of speech from one speaker in the form of traditional stories, and spontaneous narratives about life, family and customs (Michaud, 2017, 33). Several hours of the recordings have been phonemically transcribed, and we used up to 149 minutes of this for training, 24 minutes for validation and 23 minutes for testing. The total number of phoneme and tone labels used for automatic transcription was 78 and 7 respectively. For Chatino, we used data of Ćavar et al. (2016) from the GORILLA language archive for Eastern Chatino of San Juan Quiahije, Oaxaca, Mexico for the purposes of comparing phoneme and tone prediction with Na when data restriction is in place. We used up to 50 minutes of data for training, 8 minutes for validation and 7 minutes for testing. The phoneme inventory we used consists of 31 labels along with 14 tone labels. For both languages, preprocessing involved removing punctuation and any other symbols that are not phonemes or tones such as tone group delimiters and hyphens connecting syllables within words. Figure 2 shows the phoneme and tone error rates for Na and Chatino. Error rate scaling Error rates decrease logarithmically with training data. The best methods reliably have a lower than 30% PER with 30 minutes of training data. We believe it is reasonable to expect similar trends in other languages, with these results suggesting how much linguists might need to transcribe before semi-automation can become part of their workflow. Quantitative Results In the case of phoneme-only prediction, use of pitch information does help reduce the PER, which is consistent with previous work (Metze et al., 2013). Tonal modelling TER is always higher than PER for the same amount of training data, despite there being only 7 tone labels versus 78 phoneme labels in our Na experiment. This is true even when pitch features are present. However, it is unsurprising since the tones have overlapping pitch ranges, and can be realized with vastly different pitch over the course of a single sentence. This suggests that context is more important for predicting tones than phonemes, which are more contextindependent. ⇒ and ⇒ are vastly in-ferior to other methods, all of which are privy to phonemic information via training labels or input. However, combining the fbank and pitch input features ( ⇒ ) makes for the equal best performing approach for tonal prediction in Na at maximum training data. This indicates both that these features are complementary and that the model has learnt a representation useful for tonal prediction that is on par with explicit phonemic information. Though tonal prediction is more challenging than phoneme prediction, these results suggest automatic tone transcription is feasible using this architecture, even without inclusion of explicit linguistic information such as constraints on valid tone sequences which is a promising line of future work. Phoneme context To assess the importance of context in tone prediction, ⇒ gives us a point of comparison where no acoustic information is available at all. It performs reasonably well for Na, and competitively for Chatino. One likely reason for its solid performance is that long-range context is modelled more effectively by using phoneme input features, since there are vastly fewer phonemes per sentence than speech frames. The rich morphotonology of Na and Chatino means context is important in the realisation of tones, explaining why ⇒ can perform almost as well as methods using acoustic features. Joint prediction Interestingly, joint prediction of phonemes and tones does not outperform the best methods for separate phoneme and tone prediction, except in the case of Chatino tone prediction, if we discount ⇒ . In light of the celebrated successes of multitask learning in various domains (Collobert et al., 2011;Deng et al., 2013;Girshick, 2015;Ramsundar et al., 2015;Ruder, 2017), one might expect training with joint prediction of phonemes and tones to help, since it gives more relevant contextual information to the model. Na versus Chatino The trends observed in the experimentation on Chatino were largely consistent with those of Na, but with higher error rates owing to less training data and a larger tone label set. There are two differences with the Na results worth noting. One is that ⇒ is more competitive in the case of Chatino, suggest- ing that phoneme context plays a more important role in tonal prediction in Chatino. The second is that ⇒ outperforms ⇒ , and that adding pitch features to Filterbank features offers less benefit than in Na. Figure 3 shows the most common tone substitution mistakes for ⇒ in the test set. Proportions were very similar for other methods. The most common tonal substitution errors were those between between M and L. Acoustically, M and L are neighbours; as mentioned above, in Na the same tone can be realised with a different pitch at different points in a sentence, leading to overlapping pitch ranges between these tones. Moreover, M and L tones were by far the most common tonal labels. Error Types Qualitative Discussion The phoneme error rates in the above quantitative analysis are promising, but is this system actually of practical use in a linguistic workflow? We discuss here the experience of a linguist in applying this model to Na data to aid in transcription of 9 minutes and 30 seconds of speech. Recognition Errors The phonemic errors typically make linguistic sense: they are not random added noise and often bring the linguist's attention to phonetic facts that are easily overlooked because they are not phonemically contrastive. One set of such errors is due to differences in articulation between different morphosyntactic classes. For example, the noun 'person' /hĩ˥/ and the relativizer suffix /-hĩ˥/ are segmentally identical, but the latter is articulated much more weakly than the former and it is often recognized as /ĩ/ in automatic transcription, without an initial /h/. Likewise, in the demonstrative /ʈʂʰɯ˥/ the initial consonant /ʈʂʰ/ is often strongly hypo-articulated, resulting in its recognition as a fricative /ʂ/, /ʐ/, or /ʑ/ instead of an aspirated affricate. As a further example, the negation that is transcribed as /mõ˧/ in Housebuilding2.290 instead of /mɤ˧/. This highlights that the vowel in that syllable is probably nazalised, and acoustically unlike the average /ɤ/ vowel for lexical words. The extent to which a word's morphosyntactic category influences the way it is pronounced is known to be languagespecific (Brunelle et al., 2015); the phonemic transcription tool indirectly reveals that this influence is considerable in Na. A second set is due to loanwords containing combinations of phonemes that are unattested in the training set. For example /ʑɯ˩pe˧/, from Mandarin rìběn (日本 , 'Japan'). /pe/ is otherwise unattested in Na, which only has /pi/; accordingly, the syllable was identified as /pi/. In documenting Na, Mandarin loanwords were initially transcribed with Chinese characters, and thus cast aside from analyses, instead of confronting the issue of how different phonological systems coexist and interact in language use. A third set of errors made by the system result in an output that is not phonologically well formed, such as syllables without tones and sequences with consonant clusters such as /kgv̩ /. These cases are easy for the linguist to identify and amend. The recognition system currently makes tonal mistakes that are easy to correct on the basis of elementary phonological knowledge: it produces some impossible tone sequences such as M+L+M inside the same tone group. Very long-ranging tonal dependencies are not harnessed so well by the current tone identification tool. This is consistent with quantitative indications in §4 and is a case for including a tonal language model or refining the neural architecture to better harness longrange contextual information. Benefits for the Linguist Using this automatic transcription as a starting point for manual correction was found to confer several benefits to the linguist. Faithfulness to acoustic signal The model produces output that is faithful to the acoustic signal. In casual oral speech there are repetitions and hesitations that are sometimes overlooked by the transcribing linguist, who is engrossed in a holistic process involving interpretation, translation, anno-tation, and communication with the language consultant. When using an automatically generated transcription as a canvas, there can be full confidence in the linearity of transcription, and more attention can be placed on linguistically meaningful dialogue with the language consultant. Typographical errors and the transcriber's mindset Transcriptions are made during fieldwork with a language consultant and are difficult to correct down the line based only on auditory impression when the consultant is not available. However, such typographic errors are common, with a large number of phoneme labels and significant use of combinations of keys (Shift, Alternative Graph, etc). By providing a high-accuracy first-pass automatic transcription, much of this manual data entry is entirely avoided. Enlisting the linguist solely for correction of errors also allows them to embrace a critical mindset, putting them in "proofreading mode", where focus can be entirely centred on assessing the correctness of the system output without the additional distracting burden of data entry. Speed Assessing automatic transcription's influence on the speed of the overall language documentation process is beyond the scope of this paper and is left to future work. Language documentation is a holistic process. Beyond phonemic transcription, documentation of Na involves other work that happens in parallel: translating, discussing with a native speaker, copying out new words into the Na dictionary, and being constantly on the lookout for new and unexpected linguistic phenomena. Further complicating this, the linguist's proficiency of the language and speed of transcription is dynamic, improving over time. This makes comparisons difficult. From this preliminary experiment, the efficiency of the linguist was perceived to be improved, but the benefits lie primarily in the advantages of providing a transcript faithful to the recording, and allowing the linguist to minimize manual entry, focusing on correction and enrichment of the transcribed document. The snowball effect More data collection means more training data for better ASR performance. The process of improving the acoustic model by training on such semi-automatic transcriptions has begun, with the freshly transcribed Housebuild-ing2 used in this investigation now available for subsequent Na acoustic modelling training. As a first example of output by incorporating automatic transcription into the Yongning Na documentation workflow, transcription of the recording Housebuilding was completed using automatic transcription as a canvas; this document is now available online. 3 Conclusion We have presented the results of applying a CTCbased LSTM model to the task of phoneme and tone transcription in a resource-scarce context: that of a newly documented language. Beyond comparing the effects of various training inputs and objectives on the phoneme and tone error rates, we reported on the application of this method to linguistic documentation of Yongning Na. Its applicability as a first-pass transcription is very encouraging, and it has now been incorporated into the workflow. Our results give an idea of the amount of speech other linguists might aspire to transcribe in order to bootstrap this process: as little as 30 minutes in order to obtain a sub-30% phoneme error rate as a starting point, with further improvements to come as more data is transcribed in the semi-automated workflow. There is still much room for modelling improvement, including incorporation of linguistic constraints into the architecture for more accurate transcriptions. Figure 2 : 2Phoneme error rate (PER) and tone error rate (TER) on test sets as training data is scaled for Na (left) and Chatino (right). The legend entries are formatted as ⟨ ⟩ ⇒ ⟨ ⟩ to indicate input features to the model and output target labels. https://github.com/oadams/mam http://lacito.vjf.cnrs.fr/pangloss/corpus/show_text_en .php?id=crdo-NRU_F4_HOUSEBUILDING2_SOUND &idref=crdo-NRU_F4_HOUSEBUILDING2 Automatic speech recognition for under-resourced languages: A survey. 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SUPP.AI: Finding Evidence for Supplement-Drug Interactions Association for Computational LinguisticsCopyright Association for Computational LinguisticsJuly 5 -July 10, 2020. 2020 Lucy Lu Wang lucyw@allenai.org Allen Institute for AI Seattle 98103WA Oyvind Tafjord oyvindt@allenai.org Allen Institute for AI Seattle 98103WA Arman Cohan armanc@allenai.org Allen Institute for AI Seattle 98103WA Sarthak Jain sarthakj@allenai.org Allen Institute for AI Seattle 98103WA Sam Skjonsberg sams@allenai.org Allen Institute for AI Seattle 98103WA Carissa Schoenick carissas@allenai.org Allen Institute for AI Seattle 98103WA Nick Botner nickb@allenai.org Allen Institute for AI Seattle 98103WA Waleed Ammar waleeda@allenai.org Allen Institute for AI Seattle 98103WA SUPP.AI: Finding Evidence for Supplement-Drug Interactions Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics the 58th Annual Meeting of the Association for Computational LinguisticsAssociation for Computational LinguisticsJuly 5 -July 10, 2020. 2020362 Dietary supplements are used by a large portion of the population, but information on their pharmacologic interactions is incomplete. To address this challenge, we present SUPP.AI, an application for browsing evidence of supplement-drug interactions (SDIs) extracted from the biomedical literature. We train a model to automatically extract supplement information and identify such interactions from the scientific literature. To address the lack of labeled data for SDI identification, we use labels of the closely related task of identifying drug-drug interactions (DDIs) for supervision. We fine-tune the contextualized word representations of the RoBERTa language model using labeled DDI data, and apply the fine-tuned model to identify supplement interactions. We extract 195k evidence sentences from 22M articles (P=0.82, R=0.58, F1=0.68) for 60k interactions. We create the SUPP.AI application for users to search evidence sentences extracted by our model. SUPP.AI is an attempt to close the information gap on dietary supplements by making upto-date evidence on SDIs more discoverable for researchers, clinicians, and consumers. Introduction More than half of US adults use dietary supplements (Kantor et al., 2016). Supplements include vitamins, minerals, enzymes, and other herbal and animal products. Supplements and pharmaceutical drugs, when taken together, can cause adverse interactions (Sprouse and van Breemen, 2016;Asher et al., 2017;Ronis et al., 2018). Some studies describe the prevalence of supplement-drug interactions (SDIs) in the hospital setting (Levy et al., 2016(Levy et al., , 2017a or among groups such as patients with cancer (Alsanad et al., 2014), cardiac disease (Karny-Rahkovich et al., 2015), HIV/AIDS (Jalloh et al., 2017), or Alzheimer's disease (Spence et al., 2017). However, these studies largely rely on manual curation of the literature, and are slow and expensive to produce and update. It is also difficult to aggregate their results, and researchers, clinicians, and consumers can lack appropriate upto-date information to make informed decisions about supplement use. A resource that provides experimental evidence for SDIs could serve as a good intermediary tool, allowing experts to quickly access information and translate it for healthcare providers and consumers. Such a tool could ease the bottleneck of manual curation by directing researcher attention to the most pertinent and novel interactions appearing in recent trials and case reports. Our goal is to create such a resource using state-of-the-art methods in NLP and IE, and allow users to better identify appropriate uses of supplements as well as risks for SDIs. Automated approaches have been used to extract drug-drug interactions (DDIs) from literature and other documents (Tari et al., 2010;Percha et al., 2011;Segura-Bedmar et al., 2011;Kim et al., 2014;Noor et al., 2017;Lim et al., 2018), complementing broadly-used but primarily manual methods (Grizzle et al., 2019). We expand upon this work to automatically extract evidence for SDIs, as well as supplement-supplement interactions (SSIs), from a large corpus of 22M biomedical and clinical texts derived from Semantic Scholar. 1 We leverage labeled datasets for DDI identification for supervision, and train a model that transfers to the related task of identifying supplement interactions. We surface the resulting evidence on SUPP.AI for browsing and search. To summarize, our contributions are: 1. A model for identifying SDI/SSI evidence 2. A dataset of 195k evidence sentences supporting supplement interactions, publicly accessi-ble for download or via a web API, and 3. SUPP.AI, an application for browsing and searching the extracted evidence. Supplement interaction browser Information on supplement interactions have immediate implications on public health, which can only be realized by making the data easily accessible to any interested researcher, clinician or consumer. We note that many medical providers in developing countries do not have subscriptions to clinical databases such as TRC 2 and UpToDate, 3 and may lack an easy way to identify possible supplement interactions before prescribing drugs to their patients. To fill this gap, we develop SUPP.AI (available at https://supp.ai/), an application for browsing evidence of supplement interactions extracted from clinical and biomedical literature. SUPP.AI allows users to: • Search for supplements or drugs, • Search through potential interactions, • Browse evidence sentences with supplement and drug entities highlighted, • Navigate links to source papers We design SUPP.AI to be a rapid way for users to access and search extracted SDI and SSI evidence. Our goal for this application is to provide a high quality, broadly-sourced, up-to-date, and easily accessible platform for searching through SDI and SSI evidence, while providing sufficient information for users to judge the quality of each piece of evidence. In Section 3, we describe the NLP pipeline used to extract evidence from scientific papers. Below, we describe the user interface and data features of SUPP.AI. User interface Besides the main search page seen by users when they first navigate to the site, SUPP.AI consists of two other types of pages: entity and interaction pages. Entity pages provide information about one supplement or drug, and a list of potential interacting entities, sorted by quantity of evidence. We provide information such as synonyms, drug trade names, and definitions about each entity upon hover over or expansion. Interaction pages display all discovered pieces of evidence supporting an interaction between a pair of entities. The evidence is sorted by additional features extracted from source papers, such as the level of evidence and recency, discussed in Section 2.2. Figure 1 shows the interface, with results for the ginkgo supplement. Results on the entity page (left) list 140 possible interactions to entities such as Warfarin and Nitric Oxide. When a result is selected, the interaction page is displayed (right), showing evidence sentences supporting the interaction along with metadata and links to each source paper. Spans linked to supplement and drug entities in evidence sentences are highlighted. To see more context or detail about the interaction, the user can navigate to the source paper to continue reading. Supporting data for search We extract additional paper metadata as a way to judge evidence quality. From Semantic Scholar, we retrieve the paper title, authors, publication venue, and year of publication. Medical Subject Headings (MeSH) tags associated with each paper are used to determine whether its results are derived from clinical trials, case reports, or animal studies. We also attempt to identify the retraction status of each paper, again using MeSH tags. Evidence sentences are ordered and presented based on associated paper metadata, prioritizing non-retracted studies, clinical trials, human studies, and recency (year of publication). Using the RxNorm relationship has_tradename via the Unified Medical Language System (UMLS) Metathesaurus (Bodenreider, 2004), we derive trade names associated with drug ingredients, e.g. Prozac and Sarafem are trade names of the ingredient fluoxetine. Trade drugs are associated with active drug ingredients and indexed for search. Users can query a trade name rather than an active ingredient and be directed to the relevant interactions. Data & API Data on the site are periodically updated as new papers are incorporated into the Semantic Scholar corpus. Snapshots of the data are available for download at https://api.semanticscholar. org/supp/. Live data on the site, which is updated more frequently, can be accessed through our search API, documented at https://supp.ai/ docs/api. Additionally, we provide training data, evaluation data, and the curated drug/supplement identifier lists (discussed in Section 3) used to produce the dataset of interactions at https:// github.com/allenai/sdi-detection. We encourage others to reuse our data and model to improve information availability around supplement interactions and safety. Methods An overview of our NLP pipeline is given in Figure 2. We first retrieve Medline-indexed articles using the Semantic Scholar API, 4 and pre-process the text to generate candidate evidence sentences (Section 3.1). We then use our DDI-detection model, a neural network classifier based on BERT (Devlin et al., 2018) and fine-tuned on labeled DDI data from Ayvaz et al. (2015) (Section 3.2), to classify sentences for the existence of an interaction. Sentences classified as positive by our model are collated and surfaced on SUPP.AI (Section 2). Generating candidate evidence Approximately 22M Medline-indexed articles are downloaded using the Semantic Scholar API. The scispaCy library (Neumann et al., 2019) is used to perform sentence tokenization, NER, and entity linking over all paper abstracts. Entity mentions are linked to Concept Unique Identifiers (CUIs) from the UMLS Metathesaurus. An example sentence from Vaes and Hendeles (2000) Of these linked entities, we preserve entities on a list of curated supplements and drugs (entities in blue). We generate these curated lists in a semi-automatic fashion, by querying the children of UMLS supplement and drug classes and performing fuzzy name matching to known supplements or drugs crawled from the web. We also perform clustering of similar entities to reduce redundancy in the final dataset, e.g., combining several variants of Vitamin D together into a single entity. Details on identifier curation and clustering are given in Appendix A. We retain all sentences containing at least two entity mentions. For each sentence, we generate candidate evidence as each combination of two entity spans from that sentence. DDI-detection model We train a DDI-detection model to predict whether a given candidate sentence provides evidence of an interaction between two drug entities. Our DDI-detection model uses pre-trained BERT models (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2018) to encode input sequences. These models have been shown to be effective at domain transfer, and are able to achieve high performance using small amounts of task-specific annotated data. In particular, we use the large version of the pre-trained RoBERTa model, a further-optimized BERT model, that has approximately 340M parameters (Liu et al., 2019). We fine-tune the pre-trained embeddings of the RoBERTa language model using labeled data for DDI classification, and we call the resulting model RoBERTa-DDI. Input layer: The input layer consists of the sequence of byte-pair encoding word pieces (Radford et al., 2019) in a sentence. We replace entity mention spans with the special tokens [Arg1] and [Arg2]. This helps generalization by preventing the model from memorizing entity pairs with positive interactions in the training set. For example: [CLS] Combination [Arg1] may also decrease the plasma concentration of [Arg2]. [SEP] where [Arg1] and [Arg2] replace the spans "hormonal contraceptives" and "acetaminophen" respectively. We add special tokens [CLS] and [SEP] at the beginning and end of each sentence to leverage their representations learned in pretraining. At prediction time, candidate sentences are masked similarly and fed to the trained model. Model architecture: As the name implies, RoBERTa-DDI uses the pre-trained RoBERTa representations (Liu et al., 2019) to encode input sequences. We refer readers to Liu et al. (2019), Devlin et al. (2018), and Vaswani et al. (2017) for more details on BERT and transformer architecture. For the RoBERTa-DDI model, we add a dropout layer followed by one feedforward (output) layer with a softmax non-linearity, which takes the representation of the [CLS] token at the top transformer layer as input and outputs probabilities for labels {0, 1}, where 1 indicates an interaction. Model training: Due to similarities between DDIs and SDIs/SSIs, we hypothesize that a classifier trained to identify DDI evidence should perform well in identifying SDI and SSI evidence. We therefore take advantage of existing labeled data for categorizing DDIs to fine-tune the model. We use pre-trained weights distributed by the authors of Liu et al. (2019), and further fine-tune the model parameters (as well as parameters of the output layer) using labeled DDI data from the Merged-PDDI dataset (Ayvaz et al., 2015). In particular, we use training data from the DDI-2013 (Segura-Bedmar et al., 2013) and NLM-DailyMed (Stan et al., 2014) datasets, as they are relatively large and contain evidence sentences with annotated drug mention spans. The DDI-2013 dataset consists of sentences extracted from Drug-Bank and Medline; the NLM-DailyMed dataset draws sentences from cardiovascular drug product labels retrieved from DailyMed. Both datasets contain multi-class labels for different types of interactions. We distinguish between detection, a binary classification problem where the goal is to determine whether an interaction exists or not, and multi-class classification, where the goal is to determine the type of interaction. In this work, we focus on detection, but provide results for a variant of our model trained on classification that obtains SOTA performance compared to prior work. For detection, we collapse labels corresponding to all interaction types (e.g., mechanism, advise, effect, etc.) into binary labels of 0 and 1, where 0 means no interaction, and 1 means an interaction of some type exists. Collapsing the positive labels is necessary for training one DDI-detection model on both the DDI-2013 and NLM-DailyMed datasets, since the two datasets are annotated with inconsistent interaction types. We preserve the train/test splits used in Ayvaz et al. (2015), and create a development set from the training set for iteration on model design and tuning. A sentence from the training data can contain multiple drug entities. For training, we generate pairwise combinations of drug mention spans in each sentence. We note that many sentences are seen multiple times by our model with different labeled spans. Due to combinatorial explosion, and to prevent our model from learning excessively from a few instances containing lots of entity mentions, we restrict the training data to sentences containing less than or equal to 100 pairwise entity combinations. Table 1 shows the resulting data splits for the two datasets. Results & evaluation Of the 22M articles we retrieve, around 4.6M abstracts contain candidate sentences. After initial filtering, 33.0M candidate sentences containing supplement entity mentions are classified by RoBERTa-DDI. Around 625k (1.9%) of these sentences are classified as positive for an interaction. We perform entity normalization across positive sentences based on CUI clusters, and perform additional ad hoc filtering of evidence to eliminate incorrectly detected spans resulting from poor NER and linking, such as the span "retina" linking to Vitamin A (C0040845). The resulting 195k sentences contain mentions of 2044 unique supplements and 2772 unique drugs, and provide evidence sentences for 60k interactions sourced from 133k papers. Comparisons of model variants on DDI classification and detection (including SOTA results on both tasks) are given in Appendix B. To evaluate the transferability of DDI detection to the related task of SDI/SSI detection, we use a test set consisting of 500 sentences annotated for the presence or absence of a supplement interaction. To obtain a balanced test set despite the rare presence of a positive interaction, we sample half the instances from the set of sentences labeled as positive by a previous variant of our model based on fine-tuning BERT-large, and the other half from those labeled as negative. After manual annotation, 40% of the sampled instances were positive for an interaction. Annotation was performed by two authors without seeing model predictions, with an inter-annotator agreement of 94%. This test set was used for final evaluation, and never for model development or tuning. Table 2 shows the performance of RoBERTa-DDI on the DDI and supplement test sets. Performance on the SDI test set has precision 0.82, recall 0.58, and F1-score 0.68. Although there is performance degradation during transfer, the precision of detection remains high at 0.82. Decrease in recall can be attributed to a larger percentage of positive instances in the SDI test set (roughly 40%, compared to 20% in the DDI training data). Another factor is the presence of incorrectly labeled entity spans in the supplements test set due to NER/linking errors. To better understand this second source of errors, we attempt to evaluate the performance of the scispaCy entity linker. Processing each sentence from the two DDI training sets using scispaCy, we determine that only 80% of drug entities from DDI-2013 and 76% from NLM-DailyMed are recognized and linked. The likelihood of supplement entities being successfully linked is likely lower, due to sparse training data for supplement NER and linking. These numbers provide an estimate of the global ceiling on recall for our model. In future work, we aim to explore ways to improve NER and linking and assess their impact on the results of SDI detection. SDI/SSI sentences in our output set can also be labeled by biomedical expert annotators and used to further tune the model for SDI/SSI detection. Evaluation set Prec. Rec. Discussion Information describing the safety and efficacy of dietary supplements can be difficult to find. The inability to locate evidence of SDIs can challenge clinician ability to advise patients and cause risks for consumers of dietary supplements. It is our hope that extracting evidence for SDIs/SSIs from a large corpus of scientific literature and making the evidence available through an easily accessible search interface can offset some of these risks. This work demonstrates how NLP techniques can be extraordinarily useful for extracting information and relationships specific to an application domain in healthcare. Re-purposing existing labeled data from related domains (that would be expensive to generate in a new domain) can be a way to derive maximum utility from curation efforts. Continuing, we look to investigate fine-grained interaction types, and provide better classification of the level of evidence provided by each sentence or document towards a particular SDI or SSI. We also aim to leverage similar techniques for identifying evidence of indications, contraindications, and side effects of dietary supplements from the biomedical and clinical literature, and make these discoverable on SUPP.AI. Related Work Consumer-facing websites such as the NIH Office of Dietary Supplements 5 or WebMD 6 provide facts about common supplements, but this information can be incomplete and may not support researcher or clinician needs. TRC Natural Medicines 7 and UpToDate 8 , two dedicated clinical resources, contain high-quality, curated evidence, but may not be broadly accessible due to their subscription format. Drug databases like DrugBank (Wishart et al., 2018), RxNorm (Nelson et al., 2011), and the National Drug File Reference Terminology (NDFRT) (Simonaitis and Schadow, 2010) contain only partial coverage of supplement terminology (Manohar et al., 2015b), and primarily focus on aggregating drug information. Several prior studies have experimented with extracting safety information of supplements and supplement interactions from various forms of text. Zhang et al. (2015) employ machine learning techniques to filter supplement interaction relationships in SemMedDB, a database of relationships extracted from Medline articles. Jiang et al. (2017) develop a model for identifying adverse effects related to dietary supplements as reported by consumers on Twitter, and discover 191 adverse effects pertaining to 4 dietary supplements. Fan et al. (2016) and Fan and Zhang (2018) analyze unstructured clinical notes to predict whether a patient started, continued or discontinued a dietary supplement, which can be useful as a building block for identifying adverse effects in clinical notes (as attempted by the same authors in Fan et al. (2017) for the drug warfarin). proposes using topic models to analyze the adverse effects of dietary supplements as mentioned in the Dietary Supplement Label Database, and finds that Latent Dirichlet Allocation models (Blei et al., 2003) can be used to group dietary supplements with similar adverse effects based on their labels. As far as we know, there are no other studies investigating the task of sentence-level identification of SDI/SSI evidence from the scientific literature. No previous work has investigated the utility of using labeled DDI data for transfer learning to SDI/SSI identification. Limitations There are several limitations of this work. First, we distinguish between supplements and drugs. Both supplements and drugs are pharmacologic entities, with their separate classification more attributable to marketing and social pressures rather than functional differences. However, due to this somewhat arbitrary distinction, supplement entities are not well represented in databases of pharmaceutical entities, and less information is publicly available on their interactions. We also use UMLS CUIs as a way of identifying supplement and drug entities. The lack of a standardized terminology to describe dietary supplements is discussed in Manohar et al. (2015a) and , which estimate UMLS coverage of these terms to be between 14-54%. This limitation prevents us from identifying many supplement entities. Lastly, our dependence on NLP-pipeline tools sets a performance ceiling due to unsolved problems in NER and linking. Although scispaCy is performant and detects a large number of relevant entities, our evaluations show that many supplement and drug entities are missed. A system such as MetaMapLite (Demner-Fushman et al., 2017) has higher recall, but performance is slow and there are practical challenges to using it to process large numbers of documents. Conclusion Insufficient regulation in the supplement space introduces dangers for the many users of these supplements. Claims of interactions are difficult to validate without links to source evidence. We create an NLP pipeline to detect SDI/SSI evidence from scientific literature, leveraging UMLS identifiers, scispaCy for NER and entity linking, BERT-based language models for classification, and labeled data from a related domain for training. We use this pipeline to extract evidence from 22M biomedical and clinical articles with high precision. The extracted SDI/SSI evidence are made search-able through a public web interface, SUPP.AI, where we integrate additional metadata about source papers to help users make decisions about the reliability of evidence. Our dataset and web interface can be leveraged by researchers, clinicians, and curious individuals to increase understanding about supplement interactions. We hope to encourage additional research to improve the safety and benefits of dietary supplements for their consumers. such as "Dietary Supplement" (NCIT: C1505, CUI: C0242295), "Vascular Plant" (NCIT: C14336, CUI: C0682475), and "Antioxidant" (NCIT: C275, CUI: C0003402) as likely parents of supplement terms. We recursively extract child entities of these parent classes from UMLS, deriving an initial list of supplements. To improve recall, we extract supplement names from the TRC Natural Medicines database, 9 perform fuzzy string matching to entities in UMLS, and add any identified CUIs to our list of supplements. The list is manually reviewed to remove non-supplement entities, those for which we could not identify any marketed supplement or medicinal uses. Following curation, we retain 2139 unique supplement entities. Similarly, we generate a corresponding list of drug CUIs from parent entity "Pharmacologic Substance" (NCIT: C1909, CUI: C1254351) and any UMLS entity with a DrugBank identifier. Fuzzy name matching between drugs on drugs.com 10 and UMLS entities is used to identify drugs and experimental chemicals missed through UMLS search alone. Due to the significantly larger number of drugs compared to supplements, manual curation of this list is impractical at this time. This process generates a list of 15252 unique drug CUIs. Any entity that is identified as both a supplement and a drug is categorized exclusively as a supplement for the purposes of this work. Similar supplement and drug entities are merged, such as those with overlapping names, e.g., entities corresponding to UMLS C0006675, C0006726, C0596235, and C3540037 all describe variants of Calcium and are merged under the supplement entity C3540037 ("Calcium Supplement"). The Model Reference F1 (classification) F1 (detection) Bi-LSTM (w/ max and attentive pooling) Sahu and Anand (2017) 0.69 (macro-F1) -Hierarchical Bi-LSTM + Attention + dependency path 0.73 (unspecified) -Bi-LSTM (w/ attention and negative instance filtering) Zheng et al. (2017) 0.77 ( canonical CUI representing a cluster is selected manually. Drug, supplement, and canonical mappings are provided in our data repository. B DDI model performance We train RoBERTa-DDI on a combination of DDI-2013 and NLM-DailyMed training data. In Table 3, we report the F1-scores of model variants on the test data. We show the performance of the final variant of RoBERTa-DDI (trained on both DDI-2013 and NLM-DailyMed) as well as a variant trained only on DDI-2013 training data (last column), which performs best on the DDI-2013 test set, but suffers when tested on NLM-DailyMed. We also further break down performance on the DrugBank and Medline sub-corpora within DDI-2013. The DDI-2013 dataset is used as a benchmark dataset for DDI detection and classification, and is part of the BLUE benchmark suite (Peng et al., 2019). RoBERTa-DDI outperforms recentlyreported SOTA performance on DDI detection in the DDI-2013 dataset using BioBERT (Lee et al., 2019) (F1 = 0.87) (Chauhan et al., 2019). Peng et al. (2019) also report SOTA performance on the DDI-2013 classification task, achieving 0.79 micro-F1 using a tuned BERT-large model. For comparison, we show the results of RoBERTa-DDI trained on DDI-2013 multi-class classification, which achieves 0.82 micro-F1 on DDI-2013 classification. We provide previously reported SOTA performance metrics on DDI-2013 in Table 4. We note that because the interaction classes are unbalanced in the DDI-2013 dataset, reported classification micro-and macro-F1-scores in previous work are not directly comparable. The inclusion of the NLM-DailyMed corpus increases training data diversity and should improve generalization for the task of detecting SDI/SSI evidence. Thus, although RoBERTa-DDI trained on DDI-2013 has the highest performance on the DDI-2013 test set, RoBERTa-DDI trained over all training data performs the best overall, and we use this model variant to classify evidence for SUPP.AI. Figure 1 : 1Top results for interactions with Ginkgo (left), and top evidence sentences for the SDI between Ginkgo and Warfarin (right). Source paper metadata are given below each evidence sentence. Figure 2 : 2Pipeline for identifying sentences containing evidence of SDIs and SSIs. is shown with linked entity mentions:Hemorrhage C0019080 and tendencies were noted in four cases C0868928 with ginkgo C0330205 use and in three 4 https://api.semanticscholar.org/ cases C0868928 with garlic C0017102 ; in none of these cases C0868928 were patients C0030705 receiving warfarin C0043031 . Table 2 : 2The RoBERTa-DDI model (trained on drug- drug interaction labels) is evaluated on two DDI evalu- ation sets (first two rows) and our supplement interac- tion evaluation set (last row). Table 3 : 3F1-scores of RoBERTa-DDI trained using different training data. Test data contains all pairwise combinations of entities in test sentences. Table 4 : 4Baseline models for DDI detection and reported performance on the DDI-2013 test set. 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The IATE Project -Towards a Single Terminology Database For the European Union PART ONE: CURRENT STATUS OF THE IATE PROJECT

The IATE project was launched in early 2000 for the creation of a single central terminology database for all the institutions, agencies and other bodies of the European Union. By mid-2001, it had reached the prototype phase. It is evident that the attempt of uniting the terminology that has been created in different institutions, with different approaches to terminology and different working cultures, was not an easy task. Although the implementation of the system has, from a technical point of view, already reached a rather advanced stage it is predictable that user feedback during the prototype and pilot phases will still lead to a number of changes. The biggest challenge of the project, however, lies in its introduction in the terminology and translation workflow of the participating bodies. This is illustrated in the second part of the paper by the example of the European Parliament's Translation Service.

The IATE Project -Towards a Single Terminology Database For the European Union PART ONE: CURRENT STATUS OF THE IATE PROJECT 30 November 2001 D Rummel Translation Centre for Bodies of the EU 1, r. du Fort ThuengenL-1499Luxembourg &amp; S Ball sball@europarl.eu.int European Parliament Translation Service L-2929Luxembourg The IATE Project -Towards a Single Terminology Database For the European Union PART ONE: CURRENT STATUS OF THE IATE PROJECT Translating and the Computer 2330 November 2001 The IATE project was launched in early 2000 for the creation of a single central terminology database for all the institutions, agencies and other bodies of the European Union. By mid-2001, it had reached the prototype phase. It is evident that the attempt of uniting the terminology that has been created in different institutions, with different approaches to terminology and different working cultures, was not an easy task. Although the implementation of the system has, from a technical point of view, already reached a rather advanced stage it is predictable that user feedback during the prototype and pilot phases will still lead to a number of changes. The biggest challenge of the project, however, lies in its introduction in the terminology and translation workflow of the participating bodies. This is illustrated in the second part of the paper by the example of the European Parliament's Translation Service. INTRODUCTION Since 1995 high level representatives of the translation services of the European Union's Institutions and agencies have met in the Interinstitutional Committee for Translation. This committee was set up to formalise contacts and cooperation between these partners that had already existed on an informal level previously. The mandate that governs its activities clearly stresses two aspects: that the partners involved share to a large extent the same problems and face similar challenges; they should thus thrive to find common solutions; whilst underlining, on the other hand, that one translation service is not like another; that due to the role each of the bodies of the Union plays in the life of the Community the practicalities of translation work may differ strongly from one service to another. Still, one of the fields where close cooperation was not only considered in the interest of complying with ever tighter budget lines, but also as offering substantial advantages for the linguistic staff, was terminology. The availability of terminological resources in the translation services of the Union as such was (and is) far from what one would call unsatisfactory. The "big three", Commission, European Parliament and Council, have each built up powerful terminology databases: the Commission's Eurodicautom, as the oldest and biggest of the institutional databases contains about 1.4 million multilingual concepts. It offers, as do the Council's TIS and the EP's EUTERPE, web-based search interfaces and thus gives access to a vast store of linguistic information to a wide public inside and outside the institutions. The picture looks less bright for the smaller institutions and agencies who in some cases use internal databases (usually in MultiTerm 95) or make do with glossaries in word processor formats. Cooperation on terminological questions and the sharing of information is far from evident even for bodies that need to work closely together like, e.g. the decentralised agencies and the Translation Centre. There are yet more drawbacks to this situation. The absence of a single point of access to all terminological data makes the lives of translators and other people searching for terminological information difficult. In order to access all available information from the big three databases you would have to learn and use three different interfaces. Attempts to import data from TIS and Euterpe into Eurodicautom to overcome this difficulty have given only unsatisfactory results. Not only do the technical difficulties of the process make regular updates impossible. The difference in the data structures, expression of different terminological cultures and working methods, also lead to a loss of data in the import process. This fact points to another, more general problem that goes beyond pure convenience for the end-user. The existence of parallel, independent approaches for the creation and maintenance of terminology have made cooperation between institutions and agencies difficult if not impossible. There is no easy way of standardising the usage of terminology between institutions. Problems of inconsistency, redundancy in the data and duplication of work result from this "balkanisation" of the terminology in the European Union. A study carried out by the IT consultancy company ATOS in 1998 clearly analysed the shortcomings of this situation and concluded that the best remedy was the creation of a single interinstitutional terminology database. After the definition of a common data format all data collected by the different institutions should be merged into this database. But the recommendations of the report went yet further: they stressed the need for wider interinstitutional cooperation in the field of terminology, the reorganisation of terminology activity, reinforcement of staffing where necessary and the build-up of an infrastructure that would allow for cooperative data management. Acting on the recommendations of the ATOS study, the Translation Centre launched the "IATE" ("Inter-Agency Terminology Exchange") project in 1999; its initial objective was to create an infrastructure for the management of terminology for the Centre and the decentralised agencies of the Union. The other European Institutions later joined this initiative and gave the project its truly interinstitutional status. The implementation of the IATE project started in January 2000. A consortium of the Greek IT company Quality&Reliability and the Danish government research institute Center for Sprogteknologi (CST) developed -together with institutional participants -the technical and functional specifications of the European Union's terminology database. In summer 2001 the tests of the prototype of this system were performed. Concepts that have been developed by the participants of various work groups in the phase of system analysis and design have become usable features of the prototype: interactive on-line data entry, a flexible validation system, tools for monitoring, reporting and auditing, advanced user management and modules for large scale data management are operational. However, when we speak of a prototype we should be aware that there is still some way to go until this database will be accessible for institutional users and a wider public. The first version of the EU term base made it possible for a group of test users to carry out functional tests, i.e. to check whether the underlining concepts have been implemented correctly and whether they were correct in the first place. A number of aspects of the system, especially the design of the user interfaces, will be subject to considerable modifications in the near future. The screen shots reproduced in this paper are taken from the prototype and should thus be seen as what they are: a glimpse of work in progress and not as a final product. Another two pilot test phases, scheduled for the first two quarters of 2002, will reflect the experience gathered during the prototype test phase and brings us much closer to a system that hopefully combines functionality and user-friendliness. It is beyond the scope of this paper to give a detailed account of all the different features and modules that have been implemented so far. I will concentrated on following three major aspects of this development: • One common database for all institutions and agencies containing all legacy data; • Interactivity, i.e. the possibility for user to carry out modifications, to add entries directly on the central database and to allow thus their colleague to profit from this work immediately; • In-build validation procedures to ensure quality. Other features, that can be discussed only briefly, include: • management tools, e.g. for user and data administration; • reporting tools; • messaging systems as communication mechanisms between the actors in the terminology workflow. LEGACY DATA Merging the terminology of the existing institutional databases into one single database was a major challenge of the first phase of the project. So far the following databases have been imported into the EU term base: Eurodicautom (Commission), TIS (Council), Euterpe (EP), Euroterms (Translation Centre) and CDCTERM (Court of Auditors). Data from the Court of Justice and the European Investment Bank will be added during a second phase of data loading scheduled for the beginning of 2002. The resources of other European bodies can be added at a later stage as the need arises. The first achievement of the IATE project is that the legacy data has been physically merged into one relational database 1 without serious loss or corruption of data. This task was challenging not only because of the tremendous amount of data that had to be treated (Eurodicautom alone contains about 1.2 million multilingual concepts); a bigger problem than the actual number of entries was the content of the different databases and the ways in which it is structured: different philosophies of terminology and different historical backgrounds that are expressed in the data stored had to be reconciled. This process involved, in a first step, the definition of mapping rules between the data structures of the existing databases and the new format of the interinstitutional database. This data structure takes into consideration the evolving standards in the field (SALT/MARTIF, GENETER). It adopted a concept-oriented approach; the mono-and multilingual information on each aspect of a concept can be expressed on four interrelated levels of the data structure of the terminological entries: Figure 1: Basic data structure of the EU term base 1. the language-independent level can contain all information that relates to the entire concept. "Domain" is the classic example of that type of information. But the database also makes it possible to be more exhaustive: the user can add a domain note in cases when the classification system for domains does not contain a suitable descriptor; collection, problem language, cross references to other entries, origin of the concept and -as we are living in an age of multimedia -links to images complete the language independent level. 2. Beneath this top level, information like definition, explanation and comments can be stored in and for each of the languages the entry contains. This level is enriched by the possibility to add notes on several fields, references to source documents and, again, multimedia files. 3. Each language level may refer to several terms -synonyms of the same concept or abbreviations. A large variety of information can be associated with each of the terms: term type, reference, regional usage, context, customers, links to homonyms etc. 4. Finally the system includes the option to add linguistic information, like part of speech or gender, for each term or each of the words constituting a term. The first step in the conversion of the legacy data was, as mentioned above, the mapping of all data fields in the existing term bases to a corresponding field in the [ATE structure. It is evident that this was not always a straightforward process. In some cases the idiosyncratic use of certain data fields in the legacy databases made it necessary to apply complex algorithms to come to a satisfactory result. The participants in the project are aware that this first import of data, although successful to a large degree, will have to be repeated as remaining difficulties become apparent. A specific part of the project deals with the problems related to the consolidation and standardisation of legacy data. The following aspects have been detected so far as being crucial for the quality of the legacy data: • detecting and dealing with perfect duplicates; • detecting and dealing with partial duplicates (e.g. same subject domain and same term, abbreviation or expression in at least one language); • identifying and dealing with data of poor quality (e.g. with no definition); • harmonising/normalising references; • harmonising the use of standard values for certain fields. BASIC AND ADVANCED SEARCHING IN THE EU TERM BASE Creating a "single point of access" to all terminology resources of the Union -one of the major objectives of the LATE project -means providing a user friendly search interface to the central database. During the design phase of the project it became clear that this is not as straightforward a task as it may seem. Not only is "user-friendliness" a term that allows for very different interpretations; but the EU term base must also cater for the needs of very different user groups: translators, terminologists and the European citizen. It also has to take into consideration that for a database of this size very basic search criteria may quickly prove insufficient. Figure 2: Basic search screen Minimal search criteria are the language of the search term and the term itself. The user can also specify the target language he or she is interested in. The order in which these languages are selected is repeated in the display of the query results. Other search criteria can and should be added to refine the search results: Domain classification: The IATE work group responsible for questions related to the content of the database (Data Content Group) decided to adopt the EuroVoc thesaurus for the domain classification of entries in the EU term base. The alternative proposition, the Lenoch classification that is used in the Eurodicautom database, was regarded as a complex, very rich, fine-grained system, that allows for a very precise classification of concepts. This positive characterisation is at the same time the reason why, after some discussions, it was decided to vote for EuroVoc: Lenoch demands expertise in classification. Translators would be able to enter first-level codes, but the allocation of lower-level codes would have to be done by experts. EuroVoc was regarded as offering several other advantages: it exists in all official languages of the Union, includes a list of keywords in natural language and benefits from the support of an interinstitutional mechanism for maintaining and enhancing content. In addition, it is based on the corpus of texts that are created by the Union, i.e. it is centred on our fields of interest. Matching: Different match operators allow to specify the degree of correspondence requested between search term and matching database entry, e.g. "Containing any of the search terms", "Containing all of the search terms (independently of order)", "exact match", "fuzzy match" and "partial match". Entry Type: Each entry in IATE belongs to a specific category, e.g. "Term only", "Phrase", "Abbreviation", and "Formula". Institution: The idea of "ownership" of data, that might be seen as being a contradiction to the basic idea of one common database, is maintained in EU term base. This is true both for legacy data and for newly created entries. "Institution" as a search and sorting criteria allows translators to focus on the terminology that is used and confirmed in his or her institution if necessary. Other criteria that allow for fine-tuning the search: Reliability, Validation status. As any of these criteria may, in any combination, be used by translators for different tasks -different document types, customers or subjects -the system provides for a simple possibility of saving query settings in named profiles. This makes it possible to quickly restore or switch between even very complex search criteria. The result of a query is a hit list containing some basic information on the concept retrieved: domain, languages, the matching term and its translations. Hyperlinks give access to more specific levels of information. A detailed result display shows all fields of an entry that contain linguistic data. From this screen it is possible to access even more fine-grained elements of the entry. Besides this basic search facility the EU term base also has to satisfy the need of expert terminologist. An "Advanced search" screen can for example be used to search for entries that contain a specific term and a specific translation in one or two other search languages. Finally the system allows for the formulation of search requests in structured query language (SQL). INTERACTIVITY The above mentioned ATOS feasibility study confirmed a common-place observation: the usability of a terminology database in the translation process cannot, or at least not exclusively, be expressed in number of entries stored in the database. Translators complaining that they cannot find a given new term in, for instance, Eurodicautom was reported as being a frequent phenomenon by the authors of the study. 1.4 million entries becomes a figure of purely academic virtue if you cannot find a valid solution for the one word that gives you a headache in the translation of an urgent document. Two aspects play a role when it comes to unsatisfactory coverage in the existing databases. Either a specific subject domain is marginal in the activities of the Union and thus the need to generate systematic glossaries was never felt. Or new political and social questions come up, bring along with them a new vocabulary that needs yet to find its binding expression in all languages of the Union. The critical, uncertain, phase for the translators lies between the appearance of vocabulary in reality and the time when, once it is mastered, it has become common. As early as possible in this phase a terminology database should offer a solution to speed up this process. The ATOS study clearly analysed a lack of inter-activity in the terminology arrangements of the institutions as the main obstacle that prevents the terminology production cycle from being faster. In many cases valuable terminology work done by translators in the course of their daily work remains unknown to their colleagues, as most databases do not allow direct write access for a larger population of users. Often terminology is hidden in private MultiTerm databases or waits on the "to do" lists of a few privileged colleagues who actually have the right to add something to a general database. It was not only technical limitations of the early database systems that made the people in charge of the terminology resources of the Union reluctant to grant write access too freely to colleagues who are -although language experts -not necessarily trained terminologists. It was also the fear that if everybody can contribute directly and unfiltered to a terminological collection chaos will break loose. Given an easy user interface people may well abandon the paper glossaries, hand written cards etc. to make the results of their reflections available to their colleagues immediately. More pertinently, terminology would be circulating and give valuable aid in the day-to-day work of the Institution's translators. Still -what about the reliability of the translations proposed? What about the completeness of the terminological entries created this way? What about a certain ideal of terminological quality and coherence that should not be easily dismissed as "academic"? The key question for each system that chooses interactive feeding by a large population of contributors is how to ensure a certain quality standard in the data collected and published. In our case: how can we avoid creating a huge, uncontrollable interinstitutional terminology scratchpad that might satisfy some ad-hoc needs, rather than a reliable database that a wider, non-professional public can turn to in confidence? However there need not be a clash of terminologists vs. translators -the underlining question is how to reconcile two potentially opposing requirements: the pragmatic need to disseminate information as quickly as possible to avoid redundancy of work, to make colleagues aware that someone has already taken care of a problem, and -more than that --to what extend a problem has been solved. Terminology that is created by non-experts as problems arise may be fraught with a number of shortcomings: time constraints may simply not make if possible to provide complete documentation for a new term, the specific knowledge necessary to create a complete terminological entry may be lackingjust think of the labyrinthine complexity of some domain classification systems to see the point. Question such as the above had already been discussed in the ATOS study. Two interinstitutional workgroups dealing with the integration of the term base into the workflow of the different institutions and the problem of data validation helped to develop a number of strategies that should make it possible to reconcile the need to produce terminology ever faster and the requirements of high quality standards. RICHNESS VS. COMPLEXITY The backbone of each terminology database is its data structure; it defines the degree of detail and complexity the database allows to maintain. And it may well be the first obstacle to efficient interactive data entry by non-expert users. Figure 4: Data entry screen The brief and deliberately incomplete description of the data structure of IATE given in the above figure is an excellent illustration of the dilemma mentioned above: this structure definitely caters for the creation of very complete, self sufficient terminological entries -but who will ever have the time and the know-how to fill in all the information this structure could hold? The problem also has a very practical side to it: how can a user interface present all these possibilities in a user friendly way -i.e. without scaring the translator's away from the product and thus reducing the notion of inter-activity to a purely theoretical status? A modern database system offers of course quite a number of features that can assist users in the phase of data entry: a rather small sub-set of the data structure will be defined as mandatory and will thus be presented in a user interface accordingly, i.e. mandatory information will be made easily accessible on the interface whereas more exotic elements will be hidden in sub-screens; the system will check on the presence of these mandatory fields to avoid incomplete information being accidentally stored. Where appropriate lists of closed value-sets will be used to avoid inconsistent usage of attributes. An interinstitutional work group is in the process of comparing the writing rules for terminological entries that have been developed in the different institutions. The result of this work will, where possible, lead to new automatic checks and to the addition of an online help system that will give valuable hints to non-expert users for each type of information that can be entered. But what would be the information that is considered mandatory in this context? When a new terminological entry has been created it should fulfil two requirements: it should be meaningful for other users of the database who search for information on a given term. And it should contain sufficient elements to allow somebody to evaluate and if necessary improve the quality of the information given. The evident elements that spring to one's mind for the mandatory fields are: domain, language, the term itself, the source of the term and an example of its usage. VALIDATION WORKFLOW The above already indicates that from the outset of the project it had been envisaged to integrate procedures that would support the review of new or modified terminology. This meant basically supplying technical solutions for the formalisation of the proofreading of terminology. But it goes beyond the good practice of having a new entry checked by a colleague: the concept of a "validation workflow" was developed that would organise the cooperation of different actors (translators, linguists, terminologist and domain experts) in the terminology production cycle. The process would take into consideration the specific competencies of the people involved and would cater for a review of terminological entries on different levels: spelling, content, coherence, exhaustiveness etc. In an early phase of the project a two level validation workflow was foreseen: The first one would be an internal review: the validation mechanism would route an new entry to another member of the same institution for an initial check; once the entry had passed this stage it would be sent, in the second phase, to a pool of domain experts from all participating institutions and, possibly, external organisations. This approach was rejected as some institutions wished to maintain complete control over their data and would not accept validation by others; it became also clear that a fixed two-stage approach would not be suitable for all institutions. Today the EU term base offers a fairly flexible system of validation that allows for the definition of different validation cycles for each participating institution whilst not ruling out the option of interinstitutional cooperation in this field. A validation cycle is the sequence of validation stages. The number of stages, the actors of each stage and the type of checks that they should perform can be defined by each institution. An example will help to make the basic idea clearer: a simple validation cycle could contain the following three stages: • Stage 1: formal check. This stage is launched directly after the creation or modification of an entry. The system will send the entry to a colleague who has the competencies to check its formal correctness, i.e. the spelling. • Stage 2: content check. Once the formal check is accomplished the entry will be routed to a domain expert who will verify the contents and enrich it when appropriate. • Stage 3: final check. A final coherence check by a terminologist terminates the validation process. The system makes it possible to add validation phases (up to nine at the moment) if necessary but also to reduce the validation cycle to a single stage. The recommendation of the work group on this question was of course to have a least one validation stage. As each institution is free to handle this question according to their needs and possibilities it was necessary to introduce an element that would guarantee a certain coherence. A common set of validation statuses was thus defined that links the different institutional practises to each other and indicates to the users the degree and type of validation a terminological entry has undergone. As an entry will be visible, i.e. retrievable, even when it has not yet been validated this information is an essential indicator for the assessment of the entry's reliability. This sequential approach to validation, that aims to benefit as much as possible from the competencies of a large population of participants, has the obvious advantage of providing the basis for a thorough, in-depth review of data. On the other hand it may hold the risk of creating an unbearable administrative overhead on terminology co-ordinators and thus turn what is supposed to be a workflow into a dead end. To avoid a situation where hundreds of entries remain non-validated a strategy had to be developed that would allow for the automatic distribution of validation work to the appropriate people. This strategy had to take into account the fact that many actions in the validation cycle are closely linked to language competencies and domain expertise, i.e. attributes that are specific to individual users of the database. This kind of information will be maintained in the interinstitutional database for each user. This "user profile" also contains administrative information like the user's name, email address, postal address, password, the institution the user works for etc. The fact that each user is known to the database system allows also for keeping track of individual preferences for the various activities that the database offers, as for example the search language or the sorting criteria for query results. The essential element of the user profile for the validation process is the user's role. Roles offer the opportunity to group different users with common characteristics together. All users belonging to the same role share the same access rights to the system, i.e. they are allowed to perform the same type of actions. The rights for certain roles can be very restricted; the role "Guest" could, for example, only grant read access to the database. Other roles, like "Translator", "Expert Translator", "Terminologist", "Domain Expert", could make if possible to add, modify or delete entries. Roles also have an impact on access to the various sub-systems of the database. The right to launch specific administrative modules, e.g. large scale data export or import, is governed by the user roles. Here again the underlining concept in the implementation of this functionality was to provide flexibility for the needs of the participating institutions. Each partner of the project is free to define the roles they consider necessary for their organisation. Figure 5: User management in the EU term base The combination of individual user profile and the general role the user is assigned to is used to manage the validation process. The stages in the validation cycle are associated with specific roles, but they may also depend on language competence or domain expertise. Based on this information the system can distribute work to a suitable validator for each new or modified entry. The role of the author of a term is also taken into consideration for the triggering of validation cycles: it determines whether a complete validation cycle has to be performed, or if the role that users belong to allows to reduce the number of stages. Based on the experience with the existing databases we can assume that a few thousand entries will be added or modified each month. Given the amount of validation work that this might imply each user of the database should also be an actor in the validation process. The system provides a simple on-line user interface, an "inbox", that displays a list of terminological entries that have been assigned to users of a specific role. The list contains information on the type of changes that have triggered the validation process, e.g. "new term", "formal change", "content change". The validates can use the information to prioritise their work. The potential complexity of the validation workflow -just keep in mind that the system allows for a flexible set-up of all the elements involved -made it necessary to provide a number of tools that would help system administrators to monitor the process and to intervene if problems occur. Such problems could be the disruption of the workflow if no user with the required competencies can be found. The various reports make it possible to monitor the following parameters: validation work per validator and stage, comparison of two stages or cycles, bottlenecks in validation stages or cycles and dead ends in the validation process for specific entries. Dead ends and bottlenecks can thus be detected and managed. A specific interface allows administrators at any time to change the assignments of the system manually. Figure 6: Example of a report on a bottleneck in a validation stage COMMUNICATION MECHANISMS Validation as it is implemented in the EU term base is a strongly formalised way of cooperation between colleagues. A specific event -the creation or modification of an entry in the database -triggers a pre-defined sequence of stages that lead to a clearly defined goal: attribution of the label "Finally validated" to the entry in question. Another kind of cooperation, less formalised, one might even say deliberately open to improvisation, is the direct communication between users of the database. A database user might come across entries that he or she wishes to comment upon. This comments can be extremely useful if they are directed to the right persons. The IATE system uses so called "marks" to support this kind of activity. Marks can be attached to each entry and can be send to individual users or users groups (e.g. the terminology group of a specific language division) -again the system takes advantage of the information stored in user profiles and role definitions to simplify this task. Usually the contents of the marks will be an exchange of information and opinions. It could be the information that two specific entries, that represent the same concept, should be merged. Or that an entry is lacking essential information. As long as a mark has not been removed by a competent colleague -i.e. once the described problem has been fixed -the mark text will be visible to all users of the database and inform them on ongoing work or help them evaluate the suitability of a entry for the problem they are working on. Besides the marks the database also offers an internal messaging system that can be used to communicate problems of a more general nature -i.e. comments that are not related to single entries -to other users of the database. REPORTS AND AUDITING Besides the above mentioned reports on the status of the validation process the EU term base will offer a considerable number of other monitoring tools to help administrators with the task of managing the database. These reports include statistics on the work activity of users belonging to specific roles or institutions. Tools that make it possible to extract statistical information on the growth and the current state of the database have also been integrated. Finally, basic operational statistics on the usage of the system can easily be created. A complete audit trail on the linguistic information stored in the EU database makes it possible to follow-up on the modifications carried out on each entry and -if necessaryto restore previous versions. The auditing records the type and content of a modification and keeps also information on the user(s) of the database who have changed an entry. CONCLUSION Perhaps the best words to sum up the different strategies used in the EU term base to ensure both quality of the terminological data and more efficient integration of terminology work into the translation workflow are communication and cooperation: the former by "showing" a modified entry to other users (as in validation) or by providing the technical facilities to make sure that comments end up with the right people; the latter by offering a platform that allows actors of the same or different bodies to share their competencies. Although there is still some way to go until the database will actually be accessible to the general public, both within and outside the institutions, the prototype has already shown that the database of the Union is indeed becoming a reality. Although, the implementation of the system has, from a technical point of view, already reached a rather advanced stage it is predictable that user feedback during the prototype and pilot phases will still lead to a number of changes. The biggest challenge of the project, however, lies in its introduction in the terminology and translation workflow of the participating bodies. This is illustrated in the second part of the paper by the example of the European Parliament's Translation Service. PART TWO -A CASE STUDY: ADAPTING TERMINOLOGY ORGANISATION AND PRACTICE AT THE EUROPEAN PARLIAMENT TO IATE This part of the paper will describe the current terminology scene at the EP, the attractions of the IATE project for us and the challenges of adapting both to the IATE structure as envisaged and to changes in terminology practice. Section 1 will describe current practice, section 2, the strategic attractions of IATE for the Translation Service, section 3, the attraction of IATE for EP translators and terminologists, section 4 will give a brief overview of our problems with IATE to date and section 5 will present some conclusions and thoughts for the future. 1.CURRENT TERMINOLOGY ORGANISATION AND PRACTICE AT THE EP The EP has had a terminology service in some form since the 1960s. After many years of organisation on traditional lines as an independent unit with terminologists for all official languages researching and publishing thematic glossaries and an in-house journal, it was reorganised in the early 1990s as one part of a larger department called the SILD Division (for Division du Support informatique, linguistique et documentaire or "IT, Language and Documentation Support Division"). The title refers intentionally to "language support" as opposed to terminology alone since for a number of years now activities have also included other support services for translation, i.e. the introduction of and on-going support for the Trados Translator's Workbench, text alignment, translation memories, speech recognition and a Parliament-wide document-production system (DocEP). The current terminology team comprises five terminologists (almost all of whom also have other tasks as part of their job description) and one secretary. It is still responsible not only for managing but also for initiating almost all terminology activity within the translation directorate, although a number of translation divisions act as service providers for languages not covered by the SILD team and translation divisions occasionally initiate terminology projects. The main on-going project covering all 11 EU languages involves monitoring and logging the terminology used in the Official Journal of the European Communities (OJ), which is an ideal source for EU translators since it exists in all languages in parallel text and is most often the terminology that we are obliged to use. To take account of other needs, since the EP is an essentially political institution, we also collect and collate topical terminology in an attempt to anticipate our translators' queries. All terminology work, whether in SILD or translation divisions, is carried out in MultiTerm 95 database management software, which is compatible with the Translator's Workbench, with a data structure adapted to our particular needs. To provide greater flexibility we are about to offer translation and other staff not familiar with MultiTerm the possibility of supplying terms via a simple intranet form for further processing by a terminologist. Terminology records, whether created in MultiTerm or the new interface, are included in the EP terminology database, EUTERPE (for Exploitation unifiée de la terminologie au Parlement européen or "European Parliament one-stop terminology management system") which currently contains approximately 260000 records in some or all of the EU languages, with acronyms or abbreviations where relevant, and sometimes with Latin (for scientific terms) or non-EU languages (for political parties, national or regional institutions, etc.). A typical EUTERPE entry from the OJ gives subject-domain information, a reference to the document where it occurred and the publication details for the OJ, information about whether the concept is defined in the OJ and terms in all languages. Figure 7: A typical EUTERPE entry Providing the translator sets his own language as the target, this approach is designed to give all the relevant information at a glance, without cluttering the screen. THE STRATEGIC ATTRACTION OF IATE FOR THE TRANSLATION SERVICE As the first part of the paper has described, IATE was originally a Translation Centre project. However, it is safe to say that, from the point of view of the major EU institutions in general and the EP's Translation Service in particular, it was a project whose time had come. In the current climate where resources are scarce and a major enlargement of the EU is just over the horizon, it makes economic sense. How can anybody justify funding at least three major terminology databases and a number of smaller ones, when a single database could cover all the institutions' needs and, taking all the institutions together, cost less? I have to say "taking all the institutions together" because there are few direct financial savings for the EP, since MultiTerm is bundled with the Translator's Workbench available on all translation staff's PCs and almost all management of our database is the responsibility of in-house staff. However, if you look back at my example of the "closed-door tour" and take account of the fact that on Eurodicautom there is not one but a number of entries for that self-same concept all from the same source, the scope for reducing duplication of effort is obvious. Figure 8: Entries for closed-door tour on Eurodicautom In a world where each institution works quite independently, results like the one above are understandable but they remain regrettable, nonetheless, particularly since TIS, too, includes a record for the same concept. Moreover, as I will show shortly, there should be savings in staffing terms for the EP as well as for the other institutions and, in an era when every post counts because we are gearing up from 11 languages to perhaps 21, that is a very significant economic incentive. This is not to suggest that our senior management is opposed to terminology activity as such or views terminologists as unproductive because it is more difficult to quantify their productivity than it is for translators. Indeed, raising the profile of terminologists and public awareness of their activity and effectiveness is seen as one of the major positive points of the whole exercise. THE ATTRACTION OF IATE FOR EP TRANSLATORS AND TERMINOLOGISTS For our translators -at least those who combine terminology activity with translation, or would like to -the attraction of the IATE database is that it is designed from the outset as an interactive system. For a number of years we have had sufficient problems with MultiTerm in our specific environment to restrict access to EUTERPE to the core team within SILD. All other translators consult a fixed copy of the database (updated regularly) and either write to buffer databases from which terminology is taken over into EUTERPE by SILD staff or e-mail their proposals to us (or as I said before, they can use the new intranet terminology form). They find this unsatisfactory because they, often rightly, view us as too slow to react and, with a small team with many other responsibilities, we find it difficult to keep up with the workload and verify some of the changes proposed. Once IATE goes live all our translators will, in principle, be able to propose new terminology records in their working languages with equivalents, if relevant in their mother tongue. They will do their work directly in the single database and all institutional users of the database will have immediate access to it. The first check on the correctness of a new record will be made by a reviser or senior translator with the same mother tongue and an expert knowledge of the language from which the source term came. A terminologist will then intervene to mark the new record for the attention of other divisions who ought to add their languages and, perhaps, where the subject domain lies outside the EP's realm of competence, an outside expert who can provide concept-level validation. As the final stage of validation the terminologist will check that the record complies with IATE standards in general and, except in the rare event of information being confidential, it will then be on general release. The flow chart illustrates how this should work in practice. Figure 9: The IATE input and validation cycle as envisaged at the EP From both the translator's and the terminologist's point of view this will push terminology activity to an earlier stage in the translation cycle, making it more useful to the translation community while ensuring that, over time, any ad hoc solutions which are less than satisfactory are revised. (The IATE structure allows for terms to be marked as deprecated and thus not available to the general public.) At the EP, of course, since much of our work involves Commission proposals on which the Parliament and Council comment or take decisions after they have been discussed at Commission level and a certain amount of terminology established, we hope that we will also have timely access to work done in the other institutions as well, to eliminate duplication of the type illustrated earlier. As for the Official Journal terminology project, this should be taken over by the IATE central management, using state-of-the-art terminology extraction software. Furthermore, an approach of this type is essentially based in translation divisions with a central unit required only for coordination and harmonisation. It is therefore very economical in terms of staff and resources, allowing for scaling up in both languages and areas of activity without an increase in the number of terminologists in the central unit. Since the IATE system includes two forms of communication, one for general use and one for use between terminologists, we should also be able to improve communication with translation divisions involved in terminology activity and feedback from other terminology users. The e-mail system will be available to all users, although we hope that they will restrict it to terminology issues. Of course, since the system is parametrically defined, if it is abused we can request the administrator to suspend users' rights. The other communication system, known as "marks" and described in part one, concerns records rather than users alone. All users will be able to read marks attached to records, so that if they see something incomplete they will be made aware that updating is ongoing, but terminology coordinators and terminologists will also be able to create them and, eventually, to delete them, once action has been taken. When they log on to the system they will be informed of the number and type of marks marked for the attention of their unit, so that they can prioritise their work and, if necessary, distribute it among colleagues to reduce response time. It will also be possible to address marks to external collaborators. You may recall that, in commenting on the input, revision and validation cycle foreseen, I referred earlier to the opportunity of external validation for terms outside the EP's area of competence. This is something for which we have never had the resources in the past, although we have all taken (often unfair) advantage of our friends, families and acquaintances with specialist knowledge on occasion. One of the aims of IATE is to build up a network of national and international experts able to validate information and provide input for their field, which will then be accessible to all database users. This will allow us to structure best practice, to everybody's benefit. IATE PROBLEMS AND CHALLENGES So far, so good, but as with all interinstitutional projects, not everything is wonderful, at least not yet. Since we are only at the prototype phase, it would be wrong to insist on aesthetic shortcomings, although a brief look at part of my standard example in IATE format gives an idea of what I mean. Figure 10: Part of the IATE bilingual display of "closed-door tour" Far more difficult for us, at least at the present time, is coming to grips with the changes in approach required. One of the advantages of having a very small team to manage EUTERPE in MultiTerm has been administrative simplicity. For hands-on terminologists it is rather daunting to change over to a situation where, quite simply, you are so dependent on other system users, even for something so easily centrally managed as deletion of redundant entries or the merge function, which works very simply in MultiTerm but, at the moment, seems much more complicated in IATE. However, being realistic, that would have to change too, quite soon, even in MultiTerm. With 21 languages no one terminologist would be able to recognise whether all terms were singular or plural, let alone whether they represented the same concept, so that merging and deletion would have to be reorganised. We also have internal problems with the differences in structure between EUTERPE and IATE, some of which are dependent on MultiTerm 95 as such, some relate to our data structure and some to more profound differences of interpretation. For the problems of the first type which do not lend themselves to automated solutions (primarily the presence of multiple synonymous abbreviations and terms in any language for the same concept) we will have to request some type of "validation holiday" in the interim period between final loading of data and the system being regarded as live, to avoid triggering a validation avalanche or tsunami when we sort them into the correct term groups. Some of the problems of the second type will require the same treatment, primarily for terms and abbreviations from non-EU languages, but others will hopefully lend themselves to automated solutions once we have implemented changes to data presentation which are currently under assessment. The last type is the most intractable, but is something which we ought to have addressed long ago. Quite simply, in our data structure where abbreviations are entered in separate indexes (for Latin and Greek characters) and not with terms, we have allowed users to obscure the difference between an abbreviation being used in a particular language (or even at the lowest level occurring in a text in a particular language) and being a term which belongs to the language concerned. Figure 11: A EUTERPE entry with multiple abbreviations In this example we would have no less than five Spanish terms once the conversion to the IATE structure is complete. Would that be the right solution? The practice came about partly because of a major shortcoming of MultiTerm, which does not lend itself readily to cross-language searching and partly because, in an environment where Translation has little control over input, we wanted to ensure that as many possible searches as possible for obscure abbreviations would be successful. They usually are, but I am sure that the terminology actors at the EP who work on data consolidation will have years of work as a result. However, all of these problems pale almost into insignificance when compared with that of getting the IATE system up and running in time. The time plan (kick-off meeting to live system in 171/2 months) was always hopelessly optimistic, but the project is now way behind schedule and we have to hope that there is no further significant loss of time if we are to have a new system bedded in by the next enlargement of the EU. The new terminology input and validation model requires cooperation from all existing divisions to work efficiently. They will need experience before new languages are added. CONCLUSIONS We remain optimistic that we will solve our problems in time to allow our current terminology users to gain the necessary experience of the IATE system of cooperative working towards common goals before enlargement. We are confident that once they have learned to use the full functionality of the new system they will see it as a vast improvement over the past. Moreover, we are sure that the availability of all our terminology throughout the EU and beyond will be appreciated by translators everywhere. Does this mean that, once the development phase of the project has successfully been accomplished by mid 2002, the future of the terminology in the EU will be all bright, i.e. free of shortcomings like inadequate coverage or disappointing quality? Unfortunately the EU term base alone will not do this magic trick. The system that is being developed at the moment comprises a number of features that go beyond what is "state of the art" in the field today. But independently of the features that the system will offer, it remains only a tool. It may be powerful, it will hopefully be user friendly, but it will definitely be most efficient if used by well-trained colleagues who see systematic terminology work, both the creation and validation of new concepts, as part of their profession. This approach, that was also a recommendation of the ATOS study, demands reinforcement of training efforts and a wider awareness of the crucial place terminology holds in the working cultures of the institutions. But then again, the EU term base could well become more than a tool. It will hopefully become a vehicle that will promote the idea of interinstitutional cooperation in the field of terminology. The discussions in various working parties of the IATE project in the last few month show that the enthusiasm for such cooperation is clearly increasing. Figure 3 : 3From hit list to low level detail Table 1 : 1The prototype of the EU term base in figures The technical implementation of the EU term base is based on Oracle 8I RDBMS using Oracle Intermedia for the indexing. The data is stored in Unicode (UTF8). List of Abbreviations Die terminologische Datenbank der Europäischen Union. A Almeida, Festschrift Joachim Göschel, Beiträge zu Linguistik und Phonetik, Ed. Angelika BraunStuttgartSupplement to the Zeitschrift für Dialektologie und LinguistikAlmeida, A. (2001), 'Die terminologische Datenbank der Europäischen Union' in Festschrift Joachim Göschel, Beiträge zu Linguistik und Phonetik, Ed. Angelika Braun, Supplement to the Zeitschrift für Dialektologie und Linguistik, Stuttgart The European Parliament's Euterpe Database: An Introduction. S I Ball, TKE 93: Terminology and Knowledge Engineering. Cologne, Index VerlagBall, S.I. (1993), 'The European Parliament's Euterpe Database: An Introduction' in TKE 93: Terminology and Knowledge Engineering, Cologne, Index Verlag, pp. 308-315 In the Beginning Was the Glossary: the development of integrated language support services at the European Parliament. S I Ball, EII, Mons, BelgiumPaper presented at Au commencement était le terme : la terminologie au service des entreprisesBall, S.I. (1996), 'In the Beginning Was the Glossary: the development of integrated language support services at the European Parliament' (Paper presented at Au commencement était le terme : la terminologie au service des entreprises, EII, Mons, Belgium) Terminology Activity at the European Parliament' in EAFT Update. S I Ball, Ball, S.I. (2001), Terminology Activity at the European Parliament' in EAFT Update March-April 2001 IATE -Services for the Development of an Interactive Terminology Database System, Open Call for Tenders. DGIII/99/050-IDA-101.02/01/IATE1European Commission, Translation Centre. European Commission, Translation Centre (1999), IATE -Services for the Development of an Interactive Terminology Database System, Open Call for Tenders DGIII/99/050-IDA-101.02/01/IATE1, Luxembourg/Brussels Validation and Quality Control Issues in a new Web-Based, Interactive Terminology Database for the Institutions and Agencies of the European Union' in Translating and the computer 22. I Johnson, M.-J Palos-Caravina, Aslib, LondonJohnson, I., Palos-Caravina, M.-J. (2000), 'Validation and Quality Control Issues in a new Web-Based, Interactive Terminology Database for the Institutions and Agencies of the European Union' in Translating and the computer 22, Aslib, London IATE -Development of a Single Central Terminology Database for the Institutions and Agencies of the European Union. I Johnson, A Macphail, Workshop on Terminology Resources and Computation, LREC 2000 Conference. Athens, GreeceJohnson, I. and MacPhail, A.(2000), IATE -Development of a Single Central Terminology Database for the Institutions and Agencies of the European Union, Workshop on Terminology Resources and Computation, LREC 2000 Conference, Athens, Greece A Macphail, IATE Project Validation Work Group Proposal. Reliability S.A.Paris, France Quality; Athens, GreeceIATE -Inter-Agency Terminology ExchangeMacPhail, A. (2000), IATE -Inter-Agency Terminology Exchange, Conference for a Terminology Infrastructure in Europe, Paris, France Quality and Reliability S.A. (2000), IATE Project Validation Work Group Proposal, Athens, Greece J-L Vidick, C Defrise, Interinstitutional Terminology Database: Feasibility Study. Brussels, Belgium147Vidick J-L. and Defrise C. (1999), Interinstitutional Terminology Database: Feasibility Study, Atos, Brussels, Belgium, 147 pp.

A Robust Retrieval Engine for Proximal and Structural Search

A Robust Retrieval Engine for Proximal and Structural Search Katsuya Masuda kmasuda@is.s.u-tokyo.ac.jp Department of Computer Science Graduate School of Information Science and Technology University of Tokyo Hongo 7-3-1, Bunkyo-ku113-0033TokyoJapan Takashi Ninomiya ninomi@is.s.u-tokyo.ac.jp Department of Computer Science Graduate School of Information Science and Technology University of Tokyo Hongo 7-3-1, Bunkyo-ku113-0033TokyoJapan CREST JST (Japan Science and Technology Corporation Honcho 4-1-8, Kawaguchi-shi332-0012SaitamaJapan Yusuke Miyao yusuke@is.s.u-tokyo.ac.jp Department of Computer Science Graduate School of Information Science and Technology University of Tokyo Hongo 7-3-1, Bunkyo-ku113-0033TokyoJapan Tomoko Ohta Department of Computer Science Graduate School of Information Science and Technology University of Tokyo Hongo 7-3-1, Bunkyo-ku113-0033TokyoJapan CREST JST (Japan Science and Technology Corporation Honcho 4-1-8, Kawaguchi-shi332-0012SaitamaJapan ‡ Jun&apos;ichi Tsujii Department of Computer Science Graduate School of Information Science and Technology University of Tokyo Hongo 7-3-1, Bunkyo-ku113-0033TokyoJapan CREST JST (Japan Science and Technology Corporation Honcho 4-1-8, Kawaguchi-shi332-0012SaitamaJapan A Robust Retrieval Engine for Proximal and Structural Search Introduction In the text retrieval area including XML and Region Algebra, many researchers pursued models for specifying what kinds of information should appear in specified structural positions and linear positions (Chinenyanga and Kushmerick, 2001;Wolff et al., 1999;Theobald and Weilkum, 2000;Clarke et al., 1995). The models attracted many researchers because they are considered to be basic frameworks for retrieving or extracting complex information like events. However, unlike IR by keywordbased search, their models are not robust, that is, they support only exact matching of queries, while we would like to know to what degree the contents in specified structural positions are relevant to those in the query even when the structure does not exactly match the query. This paper describes a new ranked retrieval model that enables proximal and structural search for structured texts. We extend the model proposed in Region Algebra to be robust by i) incorporating the idea of rankedness in keyword-based search, and ii) expanding queries. While in ordinary ranked retrieval models relevance measures are computed in terms of words, our model assumes that they are defined in more general structural fragments, i.e., extents (continuous fragments in a text) proposed in Region Algebra. We decompose queries into subqueries to allow the system not only to retrieve exactly matched extents but also to retrieve partially matched ones. Our model is robust like keyword-based search, and also enables us to specify the structural and linear positions in texts as done by Region Algebra. The significance of this work is not in the development of a new relevance measure nor in showing superiority of structure-based search over keyword-based search, but in the proposal of a framework for integrating proximal and structural ranking models. Since the model treats all types of structures in texts, not only ordinary text structures like "title," "abstract," "authors," etc., but also semantic tags corresponding to recognized named entities or events can also be used for indexing text fragments and contribute to the relevance measure. Since extents are treated similarly to keywords in traditional models, our model will be integrated with any ranking and scalability techniques used by keyword-based models. We have implemented the ranking model in our retrieval engine, and had preliminary experiments to evaluate our model. Unfortunately, we used a rather small corpus for the experiments. This is mainly because there is no test collection of the structured query and tag-annotated text. Instead, we used the GENIA corpus (Ohta et al., 2002) as structured texts, which was an XML document annotated with semantics tags in the filed of biomedical science. The experiments show that our model succeeded in retrieving the relevant answers that an exact-matching model fails to retrieve because of lack of robustness, and the relevant answers that a nonstructured model fails because of lack of structural specification. A Ranking Model for Structured Queries and Texts This section describes the definition of the relevance between a document and a structured query represented by the region algebra. The key idea is that a structured query is decomposed into subqueries, and the relevance of the whole query is represented as a vector of relevance measures of subqueries. The region algebra (Clarke et al., 1995) is a set of operators, which represent the relation between the extents (i.e. regions in texts). In this paper, we suppose the region algebra has seven operators; four containment operators (£, ¡, £, ¡) representing the containment relation between the extents, two combination operators ( , ) corresponding to "and" and "or" operator of the boolean model, and ordering operator (Q) representing the order of words or structures in the texts. For convenience of explanation, we represent a query as a tree structure as ¢ £ ! " $ # % & ¢ ¤ £ ¦ ¥ ¦ ' ( " ) # 0 ¢ ¤ 1 2 # 3 4 1 5 $ # 7 6 ¦ 8 0 " 9 ¢ ¤ £ 3 § © @ % ¢ £ ¦ ¥ ¦ § © © A ¢ ¤ £ 3 ( ' " $ # % ¢ £ ¦ ¥ ¦ ! " $ # ¢ B 1 2 # A 4 1 5 $ # 7 6 ¦ 8 % " C ¢ ¤ £ 0 ¥ ¦ § A ¢ ¤ £ 3 D E " $ # & ¢ ¤ £ ¦ ¥ ¦ ' ( " ) # 0 ¢ ¤ £ 3 D E " $ # & ¢ ¤ £ ¦ ¥ ¦ ' ( " ) # 0 ¡ ¡ ¡ ¡ ¢ £ 3 ! " $ # F ¢ © £ 0 ¥ 0 ! " $ # % ¢ B 1 2 # @ 4 1 5 $ # 6 ¦ 8 " C ¡ ¡ ¡ ¡ ¢ £ 3 ! " $ # F ¢ © £ 0 ¥ 0 ! " $ # % ¢ B 1 2 # @ 4 1 5 $ # 6 ¦ 8 " C G I H Q P S R F T U W V S R Y X a b V d c U e V f R g X @ h i p q r s t q u q v q w x y q x Figure 1: Subqueries of the query '[book] £ ([title] £ "retrieval")' shown in Figure 1 1 . This query represents 'Retrieve the books whose title has the word "retrieval." ' Our model assigns a relevance measure of the structured query as a vector of relevance measures of the subqueries. In other words, the relevance is defined by the number of portions matched with subqueries in a document. If an extent matches a subquery of query q, the extent will be somewhat relevant to q even when the extent does not exactly match q. Figure 1 shows an example of a query and its subqueries. In this example, even when an extent does not match the whole query exactly, if the extent matches "retrieval" or '[title]£"retrieval"', the extent is considered to be relevant to the query. Subqueries are formally defined as following. Definition 1 (Subquery) Let q be a given query and n 1 , ..., n m be the nodes of q. Subqueries q 1 , ..., q m of q are the subtrees of q. Each q i has node n i as a root node. When a relevance σ(q i , d) between a subquery q i and a document d is given, the relevance of the whole query is defined as following. Definition 2 (Relevance of the whole query) Let q be a given query, d be a document and q 1 , ..., q m subqueries of q. The relevance vector Σ(q, d) of d is defined as follows: Σ(q, d) = σ(q 1 , d), σ(q 2 , d), ..., σ(q m , d) A relevance of a subquery should be defined similarly to that of keyword-based queries in the traditional ranked retrieval. For example, TFIDF, which is used in our experiments in Section 3, is the most simple and straightforward one, while other relevance measures recently proposed in (Robertson and Walker, 2000) can be applied. TF value is calculated using the number of extents matching the subquery, and IDF value is calculated using the number of documents including the extents matching the subquery. While we have defined a relevance of the structured query as a vector, we need to sort the documents according to the relevance vectors. In this paper, we first map a vector into a scalar value, and then sort the documents 1 In this query, '[x]' is a syntax sugar of ' x Q /x '. according to this scalar measure. Three methods are introduced for the mapping from the relevance vector to the scalar measure. The first one simply works out the sum of the elements of the relevance vector. Definition 3 (Simple Sum) ρ sum (q, d) = m i=1 σ(q i , d) The second represents the rareness of the structures. When the query is A £ B or A ¡ B, if the number of extents matching the query is close to the number of extents matching A, matching the query does not seem to be very important because it means that the extents that match A mostly match A £ B or A ¡ B. The case of the other operators is the same as with £ and ¡. Definition 4 (Structure Coefficient) When the operator op is , or Q, the structure coefficient of the query A op B is: sc AopB = C(A) + C(B) − C(A op B) C(A) + C(B) and when the operator op is £ or ¡, the structure coefficient of the query A op B is: sc AopB = C(A) − C(A op B) C(A) where A and B are the queries and C(A) is the number of extents that match A in the document collection. The scalar measure ρ sc (q i , d) is then defined as ρ sc (q, d) = m i=1 sc qi · σ(q i , d) The third is a combination of the measure of the query itself and the measure of the subqueries. Although we calculate the score of extents by subqueries instead of using only the whole query, the score of subqueries can not be compared with the score of other subqueries. We assume normalized weight of each subquery and interpolate the weight of parent node and children nodes. Definition 5 (Interpolated Coefficient) The interpolated coefficient of the query q i is recursively defined as follows: ρ ic (q i , d) = λ · σ(q i , d) + (1 − λ) c i ρ ic (q ci , d) l where c i is the child of node n i , l is the number of children of node n i , and 0 ≤ λ ≤ 1. This formula means that the weight of each node is defined by a weighted average of the weight of the query and its subqueries. When λ = 1, the weight of each query is normalized weight of the query. When λ = 0, the weight of each query is calculated from the weight of the subqueries, i.e. the weight is calculated by only the weight of the words used in the query. Experiments In this section, we show the results of our preliminary experiments of text retrieval using our model. Because there is no test collection of the structured query and tagannotated text, we used the GENIA corpus (Ohta et al., 2002) as a structured text, which was an XML document composed of paper abstracts in the field of biomedical science. The corpus consisted of 1,990 articles, 873,087 words (including tags), and 16,391 sentences. We compared three retrieval models, i) our model, ii) exact matching of the region algebra (exact), and iii) not-structured flat model. In the flat model, the query was submitted as a query composed of the words in the queries in Table 1 connected by the "and" operator ( ). The queries submitted to our system are shown in Table 1, and the document was "sentence" represented by " sentence " tags. Query 1, 2, and 3 are real queries made by an expert in the field of biomedicine. Query 4 is a toy query made by us to see the robustness compared with the exact model easily. The system output the ten results that had the highest relevance for each model 2 . Table 2 shows the number of the results that were judged relevant in the top ten results when the ranking was done using ρ sum . The results show that our model was superior to the exact and flat models for Query 1, 2, and 3. Compared to the exact model, our model output more relevant documents, since our model allows the partial matching of the query, which shows the robustness of our model. In addition, our model outperforms the flat model, which means that the structural specification of the query was effective for finding the relevant documents. For Query 4, our model succeeded in finding the relevant results although the exact model failed to find results because Query 4 includes the tag not contained in the text (" dummy " tag). This result shows the robustness of our model. Although we omit the results of using ρ sc and ρ ic because of the limit of the space, here we summarize the results of them. The number of relevant results using ρ sc was the same as that of ρ sum , but the rank of irrelevant Query our model exact flat 1 10/10 9/10 9/10 2 6/10 5/ 5 3/10 3 10/10 9/ 9 8/10 4 7/10 0/ 0 9/10 results using ρ sc was lower than that of ρ sum . The results using ρ ic varied between the results of the flat model and the results of ρ sum depending on the value of λ. Table 1 : 1Queries submitted in the experiments Table 2 : 2(The number of relevant results) / (the number of all results) in top 10 results. '([cons]£([sem]£"G#DNA domain or region")) ("in"Q([cons]£([sem]£("G#tissue" "G#body part"))))' 2 '([event]£([obj]£"gene")) ("in"Q([cons]£([sem]£("G#tissue" "G#body part"))))' 3 '([event]£([obj]Q([sem]£"G#DNA domain or region"))) ("in"Q([cons]£([sem]£("G#tissue" "G#body part"))))' 4 '([event]£([dummy]£"G#DNA domain or region")) ("in"Q([cons]£([sem]£("G#tissue" "G#body part"))))' For the exact model, ten results were selected randomly from the exactly matched results if the total number of results was more than ten. After we had the results for each model, we shuffled these results randomly for each query, and the shuffled results were judged by an expert in the field of biomedicine whether they were relevant or not. ConclusionsWe proposed a ranked retrieval model for structured queries and texts by extending the region algebra to be ranked. Our model achieved robustness by extending the concept of words to extents and by matching with subqueries decomposed from a given query instead of matching the entire query or words. Expressive and efficient ranked querying of XML data. T Chinenyanga, N Kushmerick, Proceedings of WebDB. WebDBT. Chinenyanga and N. Kushmerick. 2001. Expressive and efficient ranked querying of XML data. In Pro- ceedings of WebDB-2001. An algebra for structured text search and a framework for its implementation. C L A Clarke, G V Cormack, F J Burkowski, The computer Journal. 38C. L. A. Clarke, G. V. Cormack, and F. J. Burkowski. 1995. An algebra for structured text search and a framework for its implementation. The computer Jour- nal, 38(1):43-56. GE-NIA corpus: an annotated research abstract corpus in molecular biology domain. T Ohta, Y Tateisi, H Mima, J Tsujii, Proceedings of HLT. HLTT. Ohta, Y. Tateisi, H. Mima, and J. Tsujii. 2002. GE- NIA corpus: an annotated research abstract corpus in molecular biology domain. In Proceedings of HLT 2002. Okapi/Keenbow at TREC-8. S E Robertson, S Walker, TREC-8. S. E. Robertson and S. Walker. 2000. Okapi/Keenbow at TREC-8. In TREC-8, pages 151-161. Adding relevance to XML. A Theobald, G Weilkum, Proceedings of WebDB'00. WebDB'00A. Theobald and G. Weilkum. 2000. Adding relevance to XML. In Proceedings of WebDB'00. XPRES: a Ranking Approach to Retrieval on Structured Documents. J Wolff, H Flörke, A Cremers, IAI-TR-99-12University of BonnTechnical ReportJ. Wolff, H. Flörke, and A. Cremers. 1999. XPRES: a Ranking Approach to Retrieval on Structured Docu- ments. Technical Report IAI-TR-99-12, University of Bonn.

Bilingually Motivated Domain-Adapted Word Segmentation for Statistical Machine Translation

We introduce a word segmentation approach to languages where word boundaries are not orthographically marked, with application to Phrase-Based Statistical Machine Translation (PB-SMT). Instead of using manually segmented monolingual domain-specific corpora to train segmenters, we make use of bilingual corpora and statistical word alignment techniques. First of all, our approach is adapted for the specific translation task at hand by taking the corresponding source (target) language into account. Secondly, this approach does not rely on manually segmented training data so that it can be automatically adapted for different domains. We evaluate the performance of our segmentation approach on PB-SMT tasks from two domains and demonstrate that our approach scores consistently among the best results across different data conditions.

Bilingually Motivated Domain-Adapted Word Segmentation for Statistical Machine Translation Yanjun Ma National Centre for Language Technology School of Computing Dublin City University Dublin 9Ireland Andy Way away@computing.dcu.ie National Centre for Language Technology School of Computing Dublin City University Dublin 9Ireland Bilingually Motivated Domain-Adapted Word Segmentation for Statistical Machine Translation March -3 April 2009. c 2009 Association for Computational Linguistics We introduce a word segmentation approach to languages where word boundaries are not orthographically marked, with application to Phrase-Based Statistical Machine Translation (PB-SMT). Instead of using manually segmented monolingual domain-specific corpora to train segmenters, we make use of bilingual corpora and statistical word alignment techniques. First of all, our approach is adapted for the specific translation task at hand by taking the corresponding source (target) language into account. Secondly, this approach does not rely on manually segmented training data so that it can be automatically adapted for different domains. We evaluate the performance of our segmentation approach on PB-SMT tasks from two domains and demonstrate that our approach scores consistently among the best results across different data conditions. Introduction State-of-the-art Statistical Machine Translation (SMT) requires a certain amount of bilingual corpora as training data in order to achieve competitive results. The only assumption of most current statistical models (Brown et al., 1993;Vogel et al., 1996;Deng and Byrne, 2005) is that the aligned sentences in such corpora should be segmented into sequences of tokens that are meant to be words. Therefore, for languages where word boundaries are not orthographically marked, tools which segment a sentence into words are required. However, this segmentation is normally performed as a preprocessing step using various word segmenters. Moreover, most of these segmenters are usually trained on a manually segmented domain-specific corpus, which is not adapted for the specific translation task at hand given that the manual segmentation is performed in a monolingual context. Consequently, such segmenters cannot produce consistently good results when used across different domains. A substantial amount of research has been carried out to address the problems of word segmentation. However, most research focuses on combining various segmenters either in SMT training or decoding (Dyer et al., 2008;Zhang et al., 2008). One important yet often neglected fact is that the optimal segmentation of the source (target) language is dependent on the target (source) language itself, its domain and its genre. Segmentation considered to be "good" from a monolingual point of view may be unadapted for training alignment models or PB-SMT decoding (Ma et al., 2007). The resulting segmentation will consequently influence the performance of an SMT system. In this paper, we propose a bilingually motivated automatically domain-adapted approach for SMT. We utilise a small bilingual corpus with the relevant language segmented into basic writing units (e.g. characters for Chinese or kana for Japanese). Our approach consists of using the output from an existing statistical word aligner to obtain a set of candidate "words". We evaluate the reliability of these candidates using simple metrics based on co-occurrence frequencies, similar to those used in associative approaches to word alignment (Melamed, 2000). We then modify the segmentation of the respective sentences in the parallel corpus according to these candidate words; these modified sentences are then given back to the word aligner, which produces new alignments. We evaluate the validity of our approach by measuring the influence of the segmentation process on Chinese-to-English Machine Translation (MT) tasks in two different domains. The remainder of this paper is organised as fol-lows. In section 2, we study the influence of word segmentation on PB-SMT across different domains. Section 3 describes the working mechanism of our bilingually motivated word segmentation approach. In section 4, we illustrate the adaptation of our decoder to this segmentation scheme. The experiments conducted in two different domains are reported in Section 5 and 6. We discuss related work in section 7. Section 8 concludes and gives avenues for future work. The Influence of Word Segmentation on SMT: A Pilot Investigation The monolingual word segmentation step in traditional SMT systems has a substantial impact on the performance of such systems. A considerable amount of recent research has focused on the influence of word segmentation on SMT (Ma et al., 2007;Chang et al., 2008;Zhang et al., 2008); however, most explorations focused on the impact of various segmentation guidelines and the mechanisms of the segmenters themselves. A current research interest concerns consistency of performance across different domains. From our experiments, we show that monolingual segmenters cannot produce consistently good results when applied to a new domain. Our pilot investigation into the influence of word segmentation on SMT involves three offthe-shelf Chinese word segmenters including ICTCLAS (ICT) Olympic version 1 , LDC segmenter 2 and Stanford segmenter version 2006-05-11 3 . Both ICTCLAS and Stanford segmenters utilise machine learning techniques, with Hidden Markov Models for ICT (Zhang et al., 2003) and conditional random fields for the Stanford segmenter (Tseng et al., 2005). Both segmentation models were trained on news domain data with named entity recognition functionality. The LDC segmenter is dictionary-based with word frequency information to help disambiguation, both of which are collected from data in the news domain. We used Chinese character-based and manual segmentations as contrastive segmentations. The experiments were carried out on a range of data sizes from news and dialogue domains using a state-of-the-art Phrase-Based SMT (PB-SMT) system-Moses (Koehn et al., 2007). The performance of PB-SMT system is measured with BLEU score (Papineni et al., 2002). We firstly measure the influence of word segmentation on in-domain data with respect to the three above mentioned segmenters, namely UN data from the NIST 2006 evaluation campaign. As can be seen from Table 1, using monolingual segmenters achieves consistently better SMT performance than character-based segmentation (CS) on different data sizes, which means character-based segmentation is not good enough for this domain where the vocabulary tends to be large. We can also observe that the ICT and Stanford segmenter consistently outperform the LDC segmenter. Even using 3M sentence pairs for training, the differences between them are still statistically significant (p < 0.05) using approximate randomisation (Noreen, 1989) However, when tested on out-of-domain data, i.e. IWSLT data in the dialogue domain, the results seem to be more difficult to predict. We trained the system on different sizes of data and evaluated the system on two test sets: IWSLT 2006 and 2007. From Table 2, we can see that on the IWSLT 2006 test sets, LDC achieves consistently good results and the Stanford segmenter is the worst. 4 Furthermore, the character-based segmentation also achieves competitive results. On IWSLT 2007, all monolingual segmenters outperform character-based segmentation and the LDC segmenter is only slightly better than the other segmenters. From the experiments reported above, we can reach the following conclusions. First of all, character-based segmentation cannot achieve state-of-the-art results in most experimental SMT settings. This also motivates the necessity to work on better segmentation strategies. Second, monolingual segmenters cannot achieve consis- Table 2: Word segmentation on IWSLT data sets tently good results when used in another domain. In the following sections, we propose a bilingually motivated segmentation approach which can be automatically derived from a small representative data set and the experiments show that we can consistently obtain state-of-the-art results in different domains. Bilingually Motivated Word Segmentation Notation While in this paper, we focus on Chinese-English, the method proposed is applicable to other language pairs. The notation, however, assumes Chinese-English MT. Given a Chinese sentence c J 1 consisting of J characters {c 1 , . . . , c J } and an English sentence e I 1 consisting of I words {e 1 , . . . , e I }, A C→E will denote a Chinese-to-English word alignment between c J 1 and e I 1 . Since we are primarily interested in 1-to-n alignments, A C→E can be represented as a set of pairs a i = C i , e i denoting a link between one single English word e i and a few Chinese characters C i .The set C i is empty if the word e i is not aligned to any character in c J 1 . Candidate Extraction In the following, we assume the availability of an automatic word aligner that can output alignments A C→E for any sentence pair (c J 1 , e I 1 ) in a parallel corpus. We also assume that A C→E contain 1-to-n alignments. Our method for Chinese word segmentation is as follows: whenever a single English word is aligned with several consecutive Chinese characters, they are considered candidates for grouping. Formally, given an alignment A C→E between c J 1 and e I 1 , if a i = C i , e i ∈ A C→E , with C i = {c i 1 , . . . , c im } and ∀k ∈ 1, m − 1 , i k+1 − i k = 1, then the alignment a i between e i and the sequence of words C i is considered a candidate word. Some examples of such 1-to-n alignments between Chinese and English we can derive automatically are displayed in Figure 1. 5 Figure 1: Example of 1-to-n word alignments between English words and Chinese characters Candidate Reliability Estimation Of course, the process described above is errorprone, especially on a small amount of training data. If we want to change the input segmentation to give to the word aligner, we need to make sure that we are not making harmful modifications. We thus additionally evaluate the reliability of the candidates we extract and filter them before inclusion in our bilingual dictionary. To perform this filtering, we use two simple statistical measures. In the following, a i = C i , e i denotes a candidate. The first measure we consider is co-occurrence frequency (COOC(C i , e i )), i.e. the number of times C i and e i co-occur in the bilingual corpus. This very simple measure is frequently used in associative approaches (Melamed, 2000). The second measure is the alignment confidence (Ma et al., 2007), defined as AC(a i ) = C(a i ) COOC(C i , e i ) , where C(a i ) denotes the number of alignments proposed by the word aligner that are identical to a i . In other words, AC(a i ) measures how often the aligner aligns C i and e i when they co-occur. We also impose that | C i | ≤ k, where k is a fixed integer that may depend on the language pair (between 3 and 5 in practice). The rationale behind this is that it is very rare to get reliable alignments between one word and k consecutive words when k is high. The candidates are included in our bilingual dictionary if and only if their measures are above some fixed thresholds t COOC and t AC , which allow for the control of the size of the dictionary and the quality of its contents. Some other measures (including the Dice coefficient) could be considered; however, it has to be noted that we are more interested here in the filtering than in the discovery of alignments per se, since our method builds upon an existing aligner. Moreover, we will see that even these simple measures can lead to an improvement in the alignment process in an MT context. Bootstrapped word segmentation Once the candidates are extracted, we perform word segmentation using the bilingual dictionaries constructed using the method described above; this provides us with an updated training corpus, in which some character sequences have been replaced by a single token. This update is totally naive: if an entry a i = C i , e i is present in the dictionary and matches one sentence pair (c J 1 , e I 1 ) (i.e. C i and e i are respectively contained in c J 1 and e I 1 ), then we replace the sequence of characters C i with a single token which becomes a new lexical unit. 6 Note that this replacement occurs even if no alignment was found between C i and e i for the pair (c J 1 , e I 1 ). This is motivated by the fact that the filtering described above is quite conservative; we trust the entry a i to be correct. This process can be applied several times: once we have grouped some characters together, they become the new basic unit to consider, and we can re-run the same method to get additional groupings. However, we have not seen in practice much benefit from running it more than twice (few new candidates are extracted after two iterations). Word Lattice Decoding Word Lattices In the decoding stage, the various segmentation alternatives can be encoded into a compact representation of word lattices. A word lattice G = V, E is a directed acyclic graph that formally is a weighted finite state automaton. In the case of word segmentation, each edge is a candidate word associated with its weights. A straightforward es-timation of the weights is to distribute the probability mass for each node uniformly to each outgoing edge. The single node having no outgoing edges is designated the "end node". An example of word lattices for a Chinese sentence is shown in Figure 2. Word Lattice Generation Previous research on generating word lattices relies on multiple monolingual segmenters (Xu et al., 2005;Dyer et al., 2008). One advantage of our approach is that the bilingually motivated segmentation process facilitates word lattice generation without relying on other segmenters. As described in section 3.4, the update of the training corpus based on the constructed bilingual dictionary requires that the sentence pair meets the bilingual constraints. Such a segmentation process in the training stage facilitates the utilisation of word lattice decoding. Phrase-Based Word Lattice Decoding Given a Chinese input sentence c J 1 consisting of J characters, the traditional approach is to determine the best word segmentation and perform decoding afterwards. In such a case, we first seek a single best segmentation: f K 1 = arg max f K 1 ,K {P r(f K 1 |c J 1 )} Then in the decoding stage, we seek: e I 1 = arg max e I 1 ,I {P r(e I 1 |f K 1 )} In such a scenario, some segmentations which are potentially optimal for the translation may be lost. This motivates the need for word lattice decoding. The search process can be rewritten as: e I 1 = arg max e I 1 ,I {max f K 1 ,K P r(e I 1 , f K 1 |c J 1 )} = arg max e I 1 ,I {max f K 1 ,K P r(e I 1 )P r(f K 1 |e I 1 , c J 1 )} = arg max e I 1 ,I {max f K 1 ,K P r(e I 1 )P r(f K 1 |e I 1 )P r(f K 1 |c J 1 )} Given the fact that the number of segmentations f K 1 grows exponentially with respect to the number of characters K, it is impractical to firstly enumerate all possible f K 1 and then to decode. However, it is possible to enumerate all the alternative segmentations for a substring of c J 1 , making the utilisation of word lattices tractable in PB-SMT. Evaluation The intrinsic quality of word segmentation is normally evaluated against a manually segmented gold-standard corpus using F-score. While this approach can give a direct evaluation of the quality of the word segmentation, it is faced with several limitations. First of all, it is really difficult to build a reliable and objective gold-standard given the fact that there is only 70% agreement between native speakers on this task (Sproat et al., 1996). Second, an increase in F-score does not necessarily imply an improvement in translation quality. It has been shown that F-score has a very weak correlation with SMT translation quality in terms of BLEU score (Zhang et al., 2008). Consequently, we chose to extrinsically evaluate the performance of our approach via the Chinese-English translation task, i.e. we measure the influence of the segmentation process on the final translation output. The quality of the translation output is mainly evaluated using BLEU, with NIST (Doddington, 2002) and METEOR (Banerjee and Lavie, 2005) as complementary metrics. Data The data we used in our experiments are from two different domains, namely news and travel dialogues. For the news domain, we trained our system using a portion of UN data for NIST 2006 evaluation campaign. The system was developed on LDC Multiple-Translation Chinese (MTC) Corpus and tested on MTC part 2, which was also used as a test set for NIST 2002 evaluation campaign. For the dialogue data, we used the Chinese-English datasets provided within the IWSLT 2007 evaluation campaign. Specifically, we used the standard training data, to which we added devset1 and devset2. Devset4 was used to tune the parameters and the performance of the system was tested on both IWSLT 2006 and 2007 test sets. We used both test sets because they are quite different in terms of sentence length and vocabulary size. To test the scalability of our approach, we used HIT corpus provided within IWSLT 2008 evaluation campaign. The various statistics for the corpora are shown in Table 3. Baseline System We conducted experiments using different segmenters with a standard log-linear PB-SMT model: GIZA++ implementation of IBM word alignment model 4 (Och and Ney, 2003), the refinement and phrase-extraction heuristics described in (Koehn et al., 2003), minimum-errorrate training (Och, 2003), a 5-gram language model with Kneser-Ney smoothing trained with SRILM (Stolcke, 2002) on the English side of the training data, and Moses (Koehn et al., 2007;Dyer et al., 2008) to translate both single best segmentation and word lattices. Experiments Results The initial word alignments are obtained using the baseline configuration described above by segmenting the Chinese sentences into characters. From these we build a bilingual 1-to-n dictionary, and the training corpus is updated by grouping the characters in the dictionaries into a single word, using the method presented in section 3.4. As previously mentioned, this process can be repeated several times. We then extract aligned phrases using the same procedure as for the baseline system; the only difference is the basic unit we are considering. Once the phrases are extracted, we perform the estimation of weights for the features of the log-linear model. We then use a simple dictionary-based maximum matching algorithm to obtain a single-best segmentation for the Chinese sentences in the development set so that Finally, in the decoding stage, we use the same segmentation algorithm to obtain the single-best segmentation on the test set, and word lattices can also be generated using the bilingual dictionary. The various parameters of the method (k, t COOC , t AC , cf. section 3) were optimised on the development set. One iteration of character grouping on the NIST task was found to be enough; the optimal set of values was found to be k = 3, t AC = 0.0 and t COOC = 0, meaning that all the entries in the bilingually dictionary are kept. On IWSLT data, we found that two iterations of character grouping were needed: the optimal set of values was found to be k = 3, t AC = 0.3, t COOC = 8 for the first iteration, and t AC = 0.2, t COOC = 15 for the second. As can be seen from Table 4, our bilingually motivated segmenter (BS) achieved statistically significantly better results than character-based segmentation when enhanced with word lattice decoding. 8 Compared to the best in-domain segmenter, namely the Stanford segmenter on this particular task, our approach is inferior according to BLEU and NIST. We firstly attribute this to the small amount of training data, from which a high quality bilingual dictionary cannot be obtained due to data sparseness problems. We also attribute this to the vast amount of named entity terms in the test sets, which is extremely difficult for our approach. 9 We expect to see better results when a larger amount of data is used and the segmenter is enhanced with a named entity recogniser. On IWSLT data (cf . Tables 5 and 6 Parameter Search Graph The reliability estimation process is computationally intensive. However, this can be easily parallelised. From our experiments, we observed that the translation results are very sensitive to the parameters and this search process is essential to achieve good results. Figure 3 is the search graph on the IWSLT data set in the first iteration step. From this graph, we can see that filtering of the bilingual dictionary is essential in order to achieve better performance. Figure 3: The search graph on development set of IWSLT task Vocabulary Size Our bilingually motivated segmentation approach has to overcome another challenge in order to produce competitive results, i.e. data sparseness. Given that our segmentation is based on bilingual dictionaries, the segmentation process can significantly increase the size of the vocabulary, which could potentially lead to a data sparseness problem when the size of the training data is small. Tables 7 and 8 list the statistics of the Chinese side of the training data, including the total vocabulary (Voc), number of character vocabulary (Char.voc) in Voc, and the running words (Run.words) when different word segmentations were used. From Table 7, we can see that our approach suffered from data sparseness on the NIST task, i.e. a large vocabulary was generated, of which a considerable amount of characters still remain as separate words. On the IWSLT task, since the dictionary generation process is more conservative, we maintained a reasonable vocabulary size, which contributed to the final good performance. Voc Scalability The experimental results reported above are based on a small training corpus containing roughly 40,000 sentence pairs. We are particularly interested in the performance of our segmentation ap- proach when it is scaled up to larger amounts of data. Given that the optimisation of the bilingual dictionary is computationally intensive, it is impractical to directly extract candidate words and estimate their reliability. As an alternative, we can use the obtained bilingual dictionary optimised on the small corpus to perform segmentation on the larger corpus. We expect competitive results when the small corpus is a representative sample of the larger corpus and large enough to produce reliable bilingual dictionaries without suffering severely from data sparseness. As we can see from Using different word aligners The above experiments rely on GIZA++ to perform word alignment. We next show that our approach is not dependent on the word aligner given that we have a conservative reliability estimation procedure. Table 11 shows the results obtained on the IWSLT data set using the MTTK alignment tool (Deng and Byrne, 2005;Deng and Byrne, 2006 (Xu et al., 2004) were the first to question the use of word segmentation in SMT and showed that the segmentation proposed by word alignments can be used in SMT to achieve competitive results compared to using monolingual segmenters. Our approach differs from theirs in two aspects. Firstly, (Xu et al., 2004) use word aligners to reconstruct a (monolingual) Chinese dictionary and reuse this dictionary to segment Chinese sentences as other monolingual segmenters. Our approach features the use of a bilingual dictionary and conducts a different segmentation. In addition, we add a process which optimises the bilingual dictionary according to translation quality. (Ma et al., 2007) proposed an approach to improve word alignment by optimising the segmentation of both source and target languages. However, the reported experiments still rely on some monolingual segmenters and the issue of scalability is not addressed. Our research focuses on avoiding the use of monolingual segmenters in order to improve the robustness of segmenters across different domains. (Xu et al., 2005) were the first to propose the use of word lattice decoding in PB-SMT, in order to address the problems of segmentation. (Dyer et al., 2008) extended this approach to hierarchical SMT systems and other language pairs. However, both of these methods require some monolingual segmentation in order to generate word lattices. Our approach facilitates word lattice gener-ation given that our segmentation is driven by the bilingual dictionary. Conclusions and Future Work In this paper, we introduced a bilingually motivated word segmentation approach for SMT. The assumption behind this motivation is that the language to be segmented can be tokenised into basic writing units. Firstly, we extract 1-to-n word alignments using statistical word aligners to construct a bilingual dictionary in which each entry indicates a correspondence between one English word and n Chinese characters. This dictionary is then filtered using a few simple association measures and the final bilingual dictionary is deployed for word segmentation. To overcome the segmentation problem in the decoding stage, we deployed word lattice decoding. We evaluated our approach on translation tasks from two different domains and demonstrate that our approach is (i) not as sensitive as monolingual segmenters, and (ii) that the SMT system using our word segmentation can achieve state-of-the-art performance. Moreover, our approach can easily be scaled up to larger data sets and achieves competitive results if the small data used is a representative sample. As for future work, firstly we plan to integrate some named entity recognisers into our approach. We also plan to try our approach in more domains and on other language pairs (e.g. Japanese-English). Finally, we intend to explore the correlation between vocabulary size and the amount of training data needed in order to achieve good results using our approach. Figure 2 : 2Example for significance testing.Table 1: Word segmentation on NIST data sets40K 160K 640K 3M CS 8.33 12.47 14.40 17.80 ICT 10.17 14.85 17.20 20.50 LDC 9.37 13.88 15.86 19.59 Stanford 10.45 15.26 16.94 20.64 Table 3 : 3Corpus statistics for Chinese (Zh) character segmentation and English (En) minimum-error-rate training can be performed. 7 Table 4 : 4BS on NIST taskBLEU NIST METEOR CS 0.1931 6.1816 0.4998 LDC 0.2037 6.2089 0.4984 BS-SingleBest 0.1865 5.7816 0.4602 BS-WordLattice 0.2041 6.2874 0.5124 Table 5 : 5BS on IWSLT 2006 taskBLEU NIST METEOR CS 0.2959 6.1216 0.5216 LDC 0.3174 6.2464 0.5403 BS-SingleBest 0.3023 6.0476 0.5125 BS-WordLattice 0.3171 6.3518 0.5603 Table 6 : 6BS on IWSLT 2007 task Table 7 : 7Vocabulary size of NIST task (40K) Table 8 : 8Vocabulary size of IWSLT task (40K) Table 9 , 9our segmentation approach achieved consistent results on both IWSLT 2006 and 2007 test sets. On the NIST task (cf. Table 10), our approach outperforms the basic character-based segmentation; however, it is still inferior compared to the other in-domain monolingual segmenters due to the low quality of the bilingual dictionary induced (cf. section 6.1).IWSLT06 IWSLT07 CS 23.06 30.25 ICT 23.36 33.38 LDC 24.34 33.44 Stanford 21.40 33.41 BS-SingleBest 22.45 30.76 BS-WordLattice 24.18 32.99 Table 9 : 9Scale-up to 160K on IWSLT data sets WordLattice 13.74 15.33Table 10: Scalability of BS on NIST task160K 640K CS 12.47 14.40 ICT 14.85 17.20 LDC 13.88 15.86 Stanford 15.26 16.94 BS-SingleBest 12.58 14.11 BS- ).IWSLT06 IWSLT07 CS 21.04 31.41 ICT 20.48 31.11 LDC 20.79 30.51 Stanford 17.84 29.35 BS-SingleBest 19.22 29.75 BS-WordLattice 21.76 31.75 Table 11 : 11BS on IWSLT data sets using MTTK7 Related Work http://ictclas.org/index.html 2 http://www.ldc.upenn.edu/Projects/ Chinese 3 http://nlp.stanford.edu/software/ segmenter.shtml Interestingly, the developers themselves also note the sensitivity of the Stanford segmenter and incorporate external lexical information to address such problems(Chang et al., 2008). While in this paper we are primarily concerned with languages where the word boundaries are not orthographically marked, this approach, however, can also be applied to languages marked with word boundaries to construct bilingually motivated "words". In case of overlap between several groups of words to replace, we select the one with the highest confidence (according to tAC). In order to save computational time, we used the same set of parameters obtained above to decode both the singlebest segmentation and the word lattice. 8 Note the BLEU scores are particularly low due to the number of references used (4 references), in addition to the small amount of training data available.9 As we previously point out, both ICT and Stanford segmenters are equipped with named entity recognition functionality. This may risk causing data sparseness problems on small training data. 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Constructing a Corpus of Japanese Predicate Phrases for Synonym/Antonym Relations

We construct a large corpus of Japanese predicate phrases for synonym-antonym relations. The corpus consists of 7,278 pairs of predicates such as "receive-permission (ACC)" vs. "obtain-permission (ACC)", in which each predicate pair is accompanied by a noun phrase and case information. The relations are categorized as synonyms, entailment, antonyms, or unrelated. Antonyms are further categorized into three different classes depending on their aspect of oppositeness. Using the data as a training corpus, we conduct the supervised binary classification of synonymous predicates based on linguistically-motivated features. Combining features that are characteristic of synonymous predicates with those that are characteristic of antonymous predicates, we succeed in automatically identifying synonymous predicates at the high F-score of 0.92, a 0.4 improvement over the baseline method of using the Japanese WordNet. The results of an experiment confirm that the quality of the corpus is high enough to achieve automatic classification. To the best of our knowledge, this is the first and the largest publicly available corpus of Japanese predicate phrases for synonym-antonym relations.

Constructing a Corpus of Japanese Predicate Phrases for Synonym/Antonym Relations Tomoko Izumi izumi.tomoko@lab.ntt.co.jp Tomohide Shibata shibata@i.kyoto-u.ac.jp Graduate School of Informatics Kyoto University Yoshida-honmachi Sakyo-ku KyotoJapan Hisako Asano asano.hisako@lab.ntt.co.jp Yoshihiro Matsuo matsuo.yoshihiro@lab.ntt.co.jp Sadao Kurohashi Graduate School of Informatics Kyoto University Yoshida-honmachi Sakyo-ku KyotoJapan NTT Media Intelligence Laboratories Nippon Telegraph and Telephone Corporation 1-1 Hikarinooka YokosukaKanagawaJapan Constructing a Corpus of Japanese Predicate Phrases for Synonym/Antonym Relations predicatessynonymantonym We construct a large corpus of Japanese predicate phrases for synonym-antonym relations. The corpus consists of 7,278 pairs of predicates such as "receive-permission (ACC)" vs. "obtain-permission (ACC)", in which each predicate pair is accompanied by a noun phrase and case information. The relations are categorized as synonyms, entailment, antonyms, or unrelated. Antonyms are further categorized into three different classes depending on their aspect of oppositeness. Using the data as a training corpus, we conduct the supervised binary classification of synonymous predicates based on linguistically-motivated features. Combining features that are characteristic of synonymous predicates with those that are characteristic of antonymous predicates, we succeed in automatically identifying synonymous predicates at the high F-score of 0.92, a 0.4 improvement over the baseline method of using the Japanese WordNet. The results of an experiment confirm that the quality of the corpus is high enough to achieve automatic classification. To the best of our knowledge, this is the first and the largest publicly available corpus of Japanese predicate phrases for synonym-antonym relations. Introduction Identifying synonymy and antonymy relations between words and phrases is one of the fundamental tasks in Natural Language Processing (NLP). Understanding these semantic relations is crucial for realizing NLP applications including QA systems, information retrieval, text mining etc. Among various words and phrases, identifying the semantic relations between predicates is especially important because predicates convey the propositional meaning of a sentence. For example, identifying synonymous predicates such as "can't repair X" and "unable to fix X" is crucial for text mining systems. Recognizing semantic opposites (antonyms) and their aspect of oppositeness are also important for NLP tasks. For example, predicate pairs expressing opposite attributive meaning such as "beautiful" and "ugly" can be used to detect contradiction in texts while antonym pairs expressing perspective differences of an event such as "sell" and "buy" are important to identify paraphrasing ("sell" and "buy" become a paraphrase if they share the same participants.). However, it is hard to obtain a rich language resource that can completely cover synonymy/antonymy relations of predicates. This is because the meaning of a predicate varies depending on the context. For example, "ignore" and "break" can express the same meaning if they are combined with the argument "rule" (break the rule vs. ignore the rule). In this paper, we introduce a large human annotated set of predicates for synonym-antonym relations. Synonym relations in this paper denote a mutual entailment relation from Chierchia and McConnel-Ginet, (2000), which can also be defined as "content synonyms" and "paraphrasing". We also annotate an entailment relation, whose synonymy is unidirectional. Antonym relations include semantic opposites, in which the events expressed by these opposite predicates result in contradiction. Accompanied by a noun phrase and case information, our data consists of 7,278 pairs of predicates such as "receive-permission (ACC)" vs. "obtain-permission (ACC)"; the relations are categorized as synonyms (mutual entailment and entailment), antonyms, or unrelated. Antonyms are further categorized into three different classes depending on their aspect of oppositeness. Using the data, we further propose a supervised classification task of synonymous predicates based on linguistically-motivated features. We believe that this is the first and the largest publicly available corpus to specify detailed synonym-antonym relations between different predicates in Japanese. For the sake of simplicity, we use the term synonyms/antonyms to refer to semantic relations of predicate phrases throughout this paper. Related Work WordNet (Miller, 1995), one of the largest thesauruses, provides word-level semantic relations including synonyms (synsets), hyponyms, and antonyms. WordNet is also available in different languages including Japanese (Bond et al., 2009), but the Japanese WordNet only provides synonyms (synsets) and hyponyms. Aizawa (2008) automatically constructs semantically similar word pairs (synonyms and hyponyms) using the pattern "C such as A and B (e.g., "publications such as magazines and books)". Here, A and B (i.e., magazines and books) can be synonyms while C (i.e., publications) can be a hyponym of A and B. This pattern, however, can only be used for extracting noun phrases; for synonymous predicates with an argument, we need a different pattern set. Mitchell and Lapata (2010) construct adjective-noun, noun-noun, and verb-object phrases with human judged similarity ratings. However, the data only shows relatedness scores, so one cannot distinguish whether the relation of a pair is synonymous or antonymous. As shown, existing resources cannot directly used to measure the semantic relations of predicates or predicate-argument structures, such as "break the rule" vs. "ignore the rule". Constructing Predicate Phrases in Synonym-Antonym Relations in Japanese To express the relations of synonyms and antonyms, we formed predicate pairs that are accompanied by a noun and a case marker. Synonymous predicates are further categorized into mutually synonymous or not (i.e., entailment) 1 . Antonyms are also subcategorized into three classes depending on their aspects of oppositeness. The following is an example. (1) ugly vs. beautiful (2) matriculate vs. graduate (3) buy vs. sell (1) expresses the semantic opposite in quality, having ugly in one extreme and beautiful in the other. (2) expresses events which cannot coincide. Interestingly, these two events are also have a past-future event relation such that one enters school, and then graduates (sequential event). (3) also expresses an opposite event, but the oppositeness is rather related to the difference in perspective. That is, (3) involves the same event but focuses on the different roles of the two participants, the buyer and the seller. We also included predicates whose meanings are unrelated because these pairs can be used by supervised methods as negative examples. Table 1 summarizes our data. 1 In this paper, we treat both synonyms and entailment as synonymous predicates because synonyms can also be called mutual entailment. Semantic Relation # of Pairs Examples Synonyms 3,188 denwa-o-tsukau vs. denwa-o-riyou "use a phone" "utilize a phone" kyoka-o-eru vs. kyoka-o-syutoku "receive permission" "obtain permission" Entailment 1,557 eigo-ga-tannou vs. eigo-o-hanasu "fluent in English" "speak English" Antonyms Incompatible Attribute/Event Relation 426 meirei-o-ukeru vs. meirei-o-kyohi "follow the order" "reject the order" Sequential Event Relation 131 denwa-o-kakeru vs. denwa-o-kiru "make a phone call" "hang up a phone" Counterpart Perspective Relation 159 sain-o-motomeru vs. sain-ni-oujiru "ask for an autograph" "give an autograph" Unrelated 1,817 denwa-ga-hairu vs. denwa-de-tsutaeru "receive a phone call" "announce by phone" TOTAL 7,278 Extracting Predicate Phrases We extracted predicates from Japanese Wikipedia. 2 Based on the frequencies of nouns in Wikipedia, we selected every fifth noun starting from the most frequent noun until the total number of nouns selected reached 100. We used these 100 nouns as an argument, and extracted the top 20 predicates based on mutual information between the noun and the predicate. The following is an example; (4) Predicates with the argument denwa "phone"  denwa-o-tsukau phone-ACC-use "use a phone"  denwa-ni-deru phone-DAT-answer "answer the phone" If a predicate appears with an auxiliary verb expressing negation, passive, causative, and potentials, we retain the negation and modality information by setting semantic labels, such as "negation" and "passive." Annotation based on Linguistic Tests Annotation was done by three annotators, all with a solid background in linguistics. We divided the data into three based on argument noun phrases, and each annotator annotated two to three predicate-argument pairs for each semantic category of an assigned noun phrase. 2 http://ja.wikipedia.org/wiki In order to make the data as consistent as possible, we also created several linguistic tests based on Chierchia and McConnel-Ginet (2000 We further categorized antonyms into the following three categories based on their oppositeness.  Incompatible Attribute/Event Relation The two predicates in this relation always denote a contradictory fact. (e.g., ugly vs. beautiful)  Sequential Event Relation The events expressed by the predicates cannot coincide but can be in the relation of past-future event. (e.g., matriculate vs. graduate)  Counterpart Perspective Relation The two predicates express different perspectives of a single event (e.g., buy vs. sell). We evaluated the quality of the corpus by randomly selecting predicate-argument pairs of 100 different noun arguments and asked an evaluator, not one of the annotators, whether the assigned semantic relation was correct or not. 93.4% of the predicate-argument pairs were evaluated as being assigned the correct semantic relation. Automatic Recognition of Synonymous Predicates Using the corpus as a training set, we conducted the supervised binary classification of synonymous predicates. The purpose of automatic classification is to examine the quality of the data as well as to investigate the possibility of automatically constructing a thesaurus of synonymous and antonymous predicates. Because the number of predicates in antonym relations is relatively small (only 10 % of the corpus), we conducted a binary classification of synonymous predicates, making predicates in synonym and entailment relations as positive examples and those in antonym relations and others as negative examples. As features for recognizing semantically similar predicates, we used two different kinds of linguistically-motivated features; one for recognizing synonyms and the other for recognizing antonyms. These features are summarized in Table 2. Linguistic Features for Recognizing Synonyms Definition sentences in a dictionary If two predicates (e.g., buy vs. purchase) express the same meaning, one (especially one with broader meaning) tends to occur in the definition sentence of the other (e.g., "to buy something, especially big of expensive" is a definition of purchase). We call this feature as "complementarity in definition sentences" because one predicate complements the meaning of the other synonymous predicate. We use the binary feature of existence of complementarity in definition sentences. We also observed that if two predicates are synonymous, their definition sentences are also similar. The following is an example of definition sentences of "high-priced" and "expensive". (5) "high-priced": Costing a lot of money (6) "expensive": Costing a lot of money We also used this characteristic, and the following are features extracted from definition sentences. 3 ・ Complemantarity in definition sentences ・ Commonality in the content words of two definition sentences (Frequencies of overlapped content words are used.) Abstract Predicate Categories If two predicates are synonyms, their abstract semantic categories must be the same. Therefore, we used the semantic categories that the predicates share as features. For example, the following two synonymous predicates share the same predicate attribute in Goi-Taikei (Ikehara et al., 1999). (7) Predicate Attributes of kau "buy" and kounyuu-suru "purchase" Both share the same predicate attributes of Transfer in possessions and Action. We use yougen zousei "predicate attributes" in Goi-Taikei (Ikehara et al, 1999) as features. The predicate attributes in Goi-Taikei are hierarchically organized; the deeper the shared attribute is, the more similar the two predicates are, so we use a weighted overlap ratio in predicate attributes, in which the deeper attributes are weighted more heavily. The weights are decided heuristically. Level x indicates the level at Goi-Taikei's Predicate Attribute Hierarchy (the highest being 1 and the lowest 4). PAtr is for Predicate Attributes. Weighted ratio of overlap in PAtr = (|PAtr for Pred1 at Level 1 ∩ PAtr for Pred2 at Level1| * 1 + (|PAtr for Pred1 at Level2 ∩ PAtr for Pred2 at Level2|) * 1.5 + (|PAtr for Pred1 at Level3 ∩ PAtr for Pred2 at Level3|) * 2 +(|PAtr for Pred1 at Level4 ∩ PAtr for Pred2 at Level4|) * 2.5 (|PAtr for Pred1 ∪ PAtr for Pred2|) ・ Predicate attributes that two predicates share ・ Weighted ratio of overlap in predicate attributes Distributional Similarities We used distributional similarities between predicates and predicate-argument pairs calculated from a vector model constructed from 6.9 billion sentences on the Web, the methods proposed in Shibata and Kurohashi (2010). They use words in the dependency relations with the predicate-argument or predicate as features for vector models. The following are distributional similarity based features we used. ・ Distributional similarity between predicates (e.g., buy vs. purchase) ・ Distributional similarity between predicate-argument structures (e.g., break-rule vs. ignore-rule) Modality and Negation Because we target predicates, we also used information of modality and negations such as "cannot" if a predicate has one. Because an auxiliary verb in a predicate phrase is transformed into a semantic label, we simply use the label as features. ・ Overlapped semantic labels (Semantic labels that both predicates have) ・ Asymmetric Occurrence of Negation, and Passive ・ Overlap rate of Semantic labels Linguistic Features for Recognizing Antonyms Measures such as distributional similarities often mistakenly assign a high score to antonym pairs. We, therefore, add several linguistic features that are peculiar to antonyms. Compounding and the tari contrastive construction In Japanese, Antonymous phrases tend to make a compound such as uri-kai (buy and sell) in which the conjunctive form of uru "sell" is combined with the conjunctive form of kau "buy". Similarly, antonymous phrases have a tendency to appear in the tari construction, in which the contrast of two different events/actions is described. (8) hon-o ut-tari kat-tari dekimasu. book-ACC sell-tari buy-tari can "You can sell and/or buy books here." We used the likelihood of making a compound and of appearing in the tari contrastive construction as features for distinguishing semantically similar phrases from antonymous phrases. By automatically generating a compound and a word string in the tari contrastive construction (Predicate A-tari-Predicate B-tari) for each predicate pair, we use the following two features as compounding and tari contrastive features. ・ Document frequency (df) of the compound calculated from the web ・ Ngram score calculated based on Japanese google ngram. ・ Ngram score of the string with the tari The higher frequency/score of the two compounds is used. Suffix combination Additionally, we used the information of Kanji character in each predicate pair. (9) 入院 vs. 退院 "enter the hospital" "leave the hospital" The kanji "入" expresses the action of entering, while the kanji "退" expresses leaving, which themselves are antonyms. In order to represent these properties, the following prefix combination features are used. The prefix combination is constructed by combining the first character of each predicate. The higher ngram score/document frequency is selected. ・ Prefix combination of predicate pairs ・ Document frequency of prefix combination ・ Ngram score of prefix combination ・ Overlap Flag in prefix combination (indicating whether prefixes extracted from each predicate are the same) Experiment and Discussion Experiment and Result We conducted an experiment of automatically classifying synonymous predicates using the features discussed in Section 4. For training, we used LIBSVM (Chang & Lin, 2011) and conducted a five-fold cross validation for evaluation. As a baseline, we used the Japanese WordNet (Bond et al., 2009), one of the largest thesauruses. If the synonymous predicate pairs are listed in the synsets in WordNet, they are counted as correct. The results are evaluated based on Precision (Prec), Recall (Rec) and F-score (F). Prec = # ∩ # # ec = # ∩ # # core = 2 * * ( + ) As shown in Table 3, using the data as a training set, our supervised classification of synonymous predicates achieved the high F-score of 0.915, compared to the baseline (0.514). Discussion Although the use of WordNet yielded the highest precision, it suffered from low recall. The following are examples that are not listed in synsets of WordNet but were correctly categorized as synonymous predicates by our method. (10) meisho-o-annai vs. meisho-o-shoukai landmark-ACC-guide landmark-ACC-introduce "guide a landmark" "introduce a landmark" (11) shien-ni-ataru vs. shien-o-zisshi support-DAT-take part in support-ACC-carry out "take part in supporting s/th" "carry out support for s/th" Predicates such as "ataru (take part in/ do)" and "zisshi (carry out/ conduct)" become synonymous with the argument "shien (support)." WordNet tends not to include predicates in synsets that become synonyms in a certain context, which degrades recall. Combining the features for recognizing synonyms and those for recognizing antonyms was very effective as the overall F-score drastically increased (Table 3). An error analysis revealed that the proposed method failed to classify synonymous predicates when their meanings are idiomatic. (12) ki-ni-yamu vs. ki-ga-yowai eart-DAT-suffer heart-NOM-weak worry" "be anxious" "(lit., my heart suffers)" "(lit., my heart is weak)" These idiomatic expressions need more sophisticated rules of inference. One possible solution would be to use how these expressions are translated into a foreign language because these idiomatic expressions might be translated into the same phrase as direct word-to-word translation is avoided for idiomatic expressions. The analysis of idiomatic expressions and their translations is for future study. Conclusion In conclusion, we constructed a large corpus of Japanese predicate phrases for synonym-antonym relations. The antonym relation was further categorized into detailed subclasses depending on their aspect of oppositeness. The corpus consists of a wide variety of expressions including idiomatic expressions. Using the data as training set, we proposed the supervised classification of synonymous predicates and achieved a promising result, indicating that the quality of the corpus is high enough to achieve automatic classification. To the best of our knowledge, this is the first and the largest publicly available corpus of Japanese predicate phrases for synonym-antonym relations. 4 We hope that the corpus will accelerate research into the automatic acquisition of language resources. Compounding and the tari contrastive construction-The Frequency and Ngram scores of the compounding word of predicate pairs -The ngram score of the string in which two predicates are combined by the tari conjunct. Suffix combination -The combination of the first character of antonym pair and its Ngram score and frequency.POS-Part-of-Speech of each predicate ・ Kau "buy" : [Transfer in possession], [Action] ・ Kounyu-suru "purchase": [Transfer in possession], [Action] Table 1 : 1Corpus of Japanese Predicate Phrases for Synonym-Antonym Relations. ). For simplicity, we use the term Predicate A and Predicate B to refer to predicate pairs. (# indicates a semantically wrong sentence.)  Synonym (Mutual Entailment) Definition: Predicate A and Predicate B denote the same event. (If the event expressed by Predicate A is true, the event expressed by Predicate B is also true and vice versa.) (e.g., repair vs. fix) Test: Negating only one of the predicates results in a contradictory fact (i.e., does not make sense). Example: # I repaired my PC, but I didn't fix it. Definition: If the event denoted by Predicate A is true, the event denoted by Predicate B is also true, but not vice versa. (e.g., snore vs. sleep) Test: Negating only Predicate B (i.e., sleep) does not make sense. However, the opposite is possible. Example: # I snored last night, but I didn't sleep. I slept last night, but I didn't snore. Definition: If the event denoted by Predicate A is true, the event denoted by Predicate B must be false. (e.g., long vs. short) Test: Predicate A and Predicate B cannot be combined by the conjunction "but" in a sentence. Example: # His legs are long, but they are short. -Binary features indicating whether a predicate appears in the definition sentences of the other predicate -Word overlap among definition sentences between predicate pairs Abstract predicate categories -Predicate categories that the two predicates share -Ratio of overlap in predicate categories Distributional Similarities -Distributional similarities between predicates -Distributional similarities between predicate-argument pairs Modality and Negation -Modality and Negations that each predicate has -Ratio of overlap in Modality and Negations between two predicates Entailment  Antonym Features Description Recognizing Synonyms Definition sentences in a dictionary Recognizing Antonyms Table 2 : 2Summary of Features. //nlp.ist.i.kyoto-u.ac.jp/index.php?PredicateEvalSet4 The corpus can be downloaded from http:Precision Recall F-Score Baseline-WordNet 0.977 0.349 0.514 Proposed 0.899 0.932 0.915 Only Using Features for Recognizing Synonyms 0.730 0.917 0.812 Only Using Features for Recognizing Antonyms 0.721 0.972 0.828 Table 3 : 3Results of Experiment. We use Gakken Japanese Dictionary(Kindaichi and Ikeda, 1988). On calculating word similarity using large text corpora. A Aizawa, Journal of Information Processing (IPSJ). 49Aizawa, A. (2008).On calculating word similarity using large text corpora, Journal of Information Processing (IPSJ), vol. 49, 3, 1426-1436. Enhancing the Japanese WordNet. F Bond, H Isahara, S Fujita, K Uchimoto, T Kuribayashi, K Kanzaki, The 7th Workshop on Asian Language Resources, in conjunction with ACL-IJCNLP 2009. SingaporeBond F, Isahara H, Fujita S, Uchimoto K, Kuribayashi T and Kanzaki K (2009). Enhancing the Japanese WordNet, The 7th Workshop on Asian Language Resources, in conjunction with ACL-IJCNLP 2009, Singapore. Meaning and grammar: An introduction to semantics. G Chierchia, S Mcconnell-Ginet, The MIT pressCambridge, MA2nd ed.Chierchia, G., and McConnell-Ginet, S. (2000). Meaning and grammar: An introduction to semantics (2nd ed.). Cambridge, MA: The MIT press. Lexical Semantics. D A Cruse, Cambridge University PressNew YorkCruse, D. A., (1986). Lexical Semantics. New York: Cambridge University Press. LIBSVM: A Library for Support Vector Machines. C-C Chang, C-J Lin, ACM Transactions on Intelligent Systems and Technology. 23TISTChang, C-C., and Lin, C-J. (2011). "LIBSVM: A Library for Support Vector Machines" ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), No.27. S Ikehara, M Miyazaki, S Shirai, A Yokoo, H Nakaiwa, K Ogura, Y Ooyama, Y Hayashi, Goi-Taikei. TokykoIwanamiIkehara, S., Miyazaki, M., Shirai, S., Yokoo, A., Nakaiwa, H., Ogura K., Ooyama, Y., and Hayashi Y. (1999). Goi-Taikei, Tokyko:Iwanami. H Kindaichi, Y Ikeda, Gakken Japanese Dictionary. TokyoGakusyuu KenkyuushandKindaichi, H., and Ikeda, Y. (1988). Gakken Japanese Dictionary (2 nd Ed.), Tokyo: Gakusyuu Kenkyuusha. Identifying synonyms among distributionally similar words. 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A Visually-Aware Conversational Robot Receptionist

Socially Assistive Robots (SARs) have the potential to play an increasingly important role in a variety of contexts including healthcare, but most existing systems have very limited interactive capabilities. We will demonstrate a robot receptionist that not only supports task-based and social dialogue via natural spoken conversation but is also capable of visually grounded dialogue; able to perceive and discuss the shared physical environment (e.g. helping users to locate personal belongings or objects of interest). Task-based dialogues include check-in, navigation and FAQs about facilities, alongside social features such as chit-chat, access to the latest news and a quiz game to play while waiting. We also show how visual context (objects and their spatial relations) can be combined with linguistic representations of dialogue context, to support visual dialogue and question answering. We will demonstrate the system on a humanoid ARI robot, which is being deployed in a hospital reception area.

A Visually-Aware Conversational Robot Receptionist September, 2022 Nancie Gunson n.gunson@hw.ac.uk School of Mathematical and Computer Sciences Interaction Lab Heriot-Watt University EdinburghScotland, UK Daniel Hernandez Garcia d.hernandez_garcia@hw.ac.uk School of Mathematical and Computer Sciences Interaction Lab Heriot-Watt University EdinburghScotland, UK Weronika Sieińska w.sieinska@hw.ac.uk School of Mathematical and Computer Sciences Interaction Lab Heriot-Watt University EdinburghScotland, UK Angus Addlesee a.addlesee@hw.ac.uk School of Mathematical and Computer Sciences Interaction Lab Heriot-Watt University EdinburghScotland, UK Christian Dondrup c.dondrup@hw.ac.uk School of Mathematical and Computer Sciences Interaction Lab Heriot-Watt University EdinburghScotland, UK Oliver Lemon o.lemon@hw.ac.uk School of Mathematical and Computer Sciences Interaction Lab Heriot-Watt University EdinburghScotland, UK Jose L Part School of Computing Edinburgh Alana AI Edinburgh UK Yanchao Yu y.yu@napier.ac.uk Napier University ScotlandUK A Visually-Aware Conversational Robot Receptionist Proceedings of the SIGdial 2022 Conference the SIGdial 2022 ConferenceEdinburgh, UKSeptember, 2022645 Socially Assistive Robots (SARs) have the potential to play an increasingly important role in a variety of contexts including healthcare, but most existing systems have very limited interactive capabilities. We will demonstrate a robot receptionist that not only supports task-based and social dialogue via natural spoken conversation but is also capable of visually grounded dialogue; able to perceive and discuss the shared physical environment (e.g. helping users to locate personal belongings or objects of interest). Task-based dialogues include check-in, navigation and FAQs about facilities, alongside social features such as chit-chat, access to the latest news and a quiz game to play while waiting. We also show how visual context (objects and their spatial relations) can be combined with linguistic representations of dialogue context, to support visual dialogue and question answering. We will demonstrate the system on a humanoid ARI robot, which is being deployed in a hospital reception area. Introduction Socially Assistive Robots (SARs) are increasingly being explored in contexts ranging from education (Papadopoulos et al., 2020) to healthcare (González-González et al., 2021). It has been noted, however, that despite the success of SARs and spoken dialogue systems in their respective research fields, integration of the two is still rare (Lima et al.,Figure 1: Interacting with SPRING-ARI 2021) and social robots in general still lack interaction capabilities (Cooper et al., 2020). In a similar fashion, even recent research on combining vision and language has tended to centre around the use of still images (Mostafazadeh et al., 2017;Zhou et al., 2020), with few systems able to support visual dialogue as part of a natural, situated conversation. The SPRING project aims to develop such a system in the form of a robot receptionist for visitors to an eldercare outpatient hospital. In this context the robot must be able to communicate naturally with users on a variety of both functional and social topics, including but not limited to those concerning the shared physical environment. We demonstrate our progress towards this goal, with a multi-modal conversational AI system that is integrated on an ARI robot 1 (Fig. 1) and task-based conversation with visual dialogue regarding navigation and object detection in the shared space. Our system greets visitors, supports them to check-in, answers FAQs and helps users to locate key facilities and objects. It also offers social support/entertainment in the form of chit-chat, a quiz and access to the latest news. System Architecture The system architecture ( Fig. 2), is composed of three main modules; a visual perception system, a dialogue system, and a social interaction planner. Visual Perception System The visual perception system is implemented as a ROS action server and is based on scene segmentation (Wu et al., 2019) from Facebook's Detectron2 framework 2 . From the segmented scene, the goal is to build a scene graph to capture relationships between objects such as location, adjacency, etc. Dialogue System The Dialogue System (Fig. 3) is based on the Alana system (Curry et al., 2018), an ensemble of different bots that compete in parallel to produce a response to user input. There are two types of bot: rule-based bots that can, for example, drive the conversation if it stalls, and express the identity of a virtual 'persona' (e.g. answering questions about the robot's age etc.); and data-driven bots 2 https://github.com/facebookresearch/ detectron2 that can retrieve replies from various information sources, e.g., News feeds. The SPRING system retains the rule-based and News bots, supplemented with a number of new, domain-specific bots. Visual Task Bot handles visual dialogue within the conversation, converting the user's inferred intent and any entities associated with it to a goal message that is forwarded to the visual action server (Part et al., 2021). Reception Bot welcomes visitors, helps them check-in and answers FAQs on, e.g., catering facilities and schedules. Directions Bot helps users find key facilities such as the bathrooms and elevator, while Quiz Bot is a simple trueor-false game designed to keep users entertained while they wait. The Dialogue Manager decides which response the robot verbalises based on a bot priority list. Automatic Speech Recognition (ASR) on the robot is currently implemented (in English) via Google Cloud 3 . Natural Language Understanding (NLU) is based on the original Alana pipeline, augmented using the RASA framework 4 for the parsing of domain-specific enquiries. Quiz bot employs regex-based intent recognition. Natural Language Generation (NLG) for the majority of bots consists of templates, with only News bot retrieving content from selected online sites. The utterances are voiced on the robot by Acapela's UK English Text-To-Speech voice 'Rachel' 5 . Social Interaction Planner The Social Interaction Planner interfaces the dialogue, vision systems, and the physical actions of the robot. It creates and executes plan(s) containing dialogue, physical, and perception actions based on the current dialogue context, and is based on the ideas of Lemon et al., 2002), enabling multi-threaded task execution and dialogue that is flexible and pausable. As shown in Fig. 2 it comprises several components, with the Arbiter managing communication between the dialogue system, the robot, and the planner The Planner is a key component and uses the principle of recipes and resources as developed by (Lemon et al., 2002) to eliminate the problems from (re-)planning and concurrent interactive planning and execution. A domain file lists all the possible actions and specifies their types and parameters including their preconditions and effects. Recipes then describe the sequence of actions (i.e. dialogue, physical, or perception) involved in achieving a desired goal. When requested by the Arbiter, these recipes are transformed into Petri-Net Plans (PNP) (Dondrup et al., 2019) and are concurrently executed together with any other plans that may already be running. At run-time, redundant actions whose effects have already been achieved are skipped, or repair actions are executed in cases where an action was unsuccessful. At any time, each action has the ability to communicate with the dialogue system via the Arbiter to allow for clarification or to communicate perception results. The SPRING ARI Robot ARI is a humanoid robot, designed for use as a socially assistive companion (Cooper et al., 2020). It is 1.65m tall, has a mobile base, a touch-screen on the torso, movable arms and head with LCD eyes that enable gaze behaviour. The version of the robot used here is equipped with several cameras creating a 360º field of view. For audio capture and processing a ReSpeaker Mic v2.0 array, with 4 microphones is mounted on the front of the belly. Example Dialogues The example dialogue shown in Table 1 Conclusions and Future Work The proposed system demonstrates how to successfully weave together a wide range of task-based, social, and visually grounded dialogue and physical actions on an SAR in a receptionist environment. Next steps are to generate the scene graphs automatically by combining data-driven approaches (Zellers et al., 2018;Yang et al., 2018;Zhang et al., 2019;Tang et al., 2020) with prudent use of refining rules. Crucially also, we are working on extending the system to handle multi-party interactions, an active area of research and highly likely to occur in this context. For the demonstration, we will showcase our system on the ARI robot, inviting attendees to interact with it and experience all the capabilities of the system described in this paper. Figure 3 : 3Dialogue System Architecture and which combines socialASR Robot's Camera Feed Server Result Visual Action Server Panoptic Segmentation of the Scene TTS Chat/ Command/ Query result Alana Interface NLU Query user/ Query result ASR result/ KB query Arbiter ROS Action Servers Execution status PNP Server Plan User command/ Execution status Planner Query/ Update KB DB Dialogue Manager Task Stack Post- Processor Task Bots Social Bots Text Audio Stream on table n e x t to on on magazine cup plant Simplified Scene Graph Panoptic Segmentation Scene Graph Generation Goal Message Goal Parsing Scene Graph Query Visual Perception System Social Interaction Planner Dialogue System Figure 2: System Architecture. The Social Interaction Planner (blue blocks) interacts with the Dialogue System (green blocks) through an Arbiter module. The Vision module (yellow blocks) is implemented as a ROS Action Server within the planning framework. Based on the user's intent, the Planner can recruit the vision module to respond to questions about the visual scene. Pleased to meet you, Jeanne. Is this your first visit? U: No, I've been before. R: [reception_bot] OK great, you'll be an expert then! That's you checked in. Please take a seat and a nurse will call you when they're ready. I'm here if you need anything else while you're waiting. U: Is there something to read while I wait? R: [visual_task_bot] I can see a magazine on the table. U: Aha thanks. R: No problem.illustrates the system's visual dialogue capabilities (in bold), occurring as part of the welcome conversation. U: Hello, I've got an appointment at 10am. R: [reception_bot] I can help you with that. May I take the name please? U: It's Jeanne X. R: [reception_bot] Table 1 : 1Task-based Dialogue with Visual Dialogue. The example dialogue in Table 2 further illustrates the robot's natural language social and taskbased capabilities. U: Excuse me, do you know how much longer I will have to wait? R: [reception_bot] I expect it won't be long now. A nurse will come and get you. Do you want to play a game to pass the time? U: OK then, yes please. R: [quiz_bot] Great, let's play! Please remember, whenever you want to leave the game, just say 'end game'. First question, is Covid-19 on all 7 continents? U: Nope. R: Correct. Next question ...... R: [quiz_bot] Congratulations! You won. That was fun. Now, please let me know if you need anything else. U: I need the bathroom, can you tell me where it is? R: [directions_bot]To find the nearest bathroom, leave the waiting room and go right towards the door with two round windows. The bathroom will then be on your left. 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PROCESSING OF SYNTAX AND SEMANTICS OF NATURAL LANGUAGE BY PREDICATE LOGIC

The syntax and semantic analyses of natural language are described from the standpoint of manmachine communication. The knowledge based system KAUS(Knowledge Acquisition and Utilization System) which has capabilities of deductive inference and automatic program generation of database access is utilized for that purpose. We try to perform syntax and semantic analyses of English sentences more or less conccurently by defining the correspondence between the basic patterns of English and the extended atomic formula in the framework of KAUS. Knowledge representation based on sets and logic, the sentence analysis utilizing this knowledge are given with some examples.

PROCESSING OF SYNTAX AND SEMANTICS OF NATURAL LANGUAGE BY PREDICATE LOGIC Hiroyuki Yamauchi Institute of Space and Aeronautical Science University of Tokyo 4-6-1 Komaba, Meguro-ku153TokyoJapan PROCESSING OF SYNTAX AND SEMANTICS OF NATURAL LANGUAGE BY PREDICATE LOGIC The syntax and semantic analyses of natural language are described from the standpoint of manmachine communication. The knowledge based system KAUS(Knowledge Acquisition and Utilization System) which has capabilities of deductive inference and automatic program generation of database access is utilized for that purpose. We try to perform syntax and semantic analyses of English sentences more or less conccurently by defining the correspondence between the basic patterns of English and the extended atomic formula in the framework of KAUS. Knowledge representation based on sets and logic, the sentence analysis utilizing this knowledge are given with some examples. i. Introduction This paper presents natural language understanding in man-machine invironments. The syntax and semantic analyses program is given almost in logical forms of the knowledge based system KAUS (knowledge Acquisition and Utilization System). KAUS is a logic machine based on the axiomatic set theory and it has capabilities of deductive inference and automatic program generation of database access. In natural language understanding, it should be required that the syntax analysis is performed with association of word semantics. The descriptions of word semantics are fundamental as well as syntax features in language analysis. When using natural language to communicate with a machine, the understanding of meanings of sentences is presupposed to the machine. We think of words as representing concept sets or property sets, and formalize them into a structure called SKELETON STRUCTURE using their element-set relationships. In this, we can represent semantic categories of words in hierarchical order. The correspondence between natural language expressions and system's logical formulas is given in the straightforward manner such as "X give Y to Z" ---(GIVE X Z Y P) where the variables X, Y and I are usually bounded by their semantic categories. We call this set of representations ATOM FORMAT DEFINITION SET. Furthermore, causality relations of general facts and paraphrases of sentences are given as general axioms. Individual facts are also given as axioms. We call these sets of representations AXIOM SET. Conceptual schemas of databases can also be given in the axiom set. The KAUS's knowledge base comprises the above three components. Utilizing this knowledge base, we try to perform the syntax and semantic analyses of natural language more or less conccurently in the sense that all these processes will be carried out through the deductive process in KAUS. At the time, the logic program of the analyses written in KAUS language is executed by the deductive process. In the chapter 2, some considerations oh natural language(NL) processing are given as preliminaries. In the chapter 3, the outline of KAUS is described, where the knowledge representation, the system language and the deductive inference rule are presented. In the chapter 4, a rather intuitive and straightforward approach to the analyses of English sentences is described with some examples. A part of the logic program of the analyses is also given there. Some Considerations on NL Processing When we are going to construct a natural language understanding system, the following four points must be clarified: i). the main motivation and goal for NL processing 2). knowledge representation suitable for NL processing 3). the aspect of programming language and methodology by which the modification, updating and extension of the program of NL processing can be easily made. 4). the efficiency of processing In the sequel, we will clarify our standpoint about the above matters. Motivation and Goal At present, the details of the mechanism of human language comprehension and knowledge memorization have not yet been clarified. These are still left uncertain. Now, let us consider the human process of natural language understanding. When we read a sentence, we make an image (meaning) of each word and match the pattern of the sentence predicting what sorts of words will be coming to the next. The imagination, prediction and pattern matching are usually made involuntarily. Furthermore, if several sentences are following, we settle the scene of the discourse and extract the meaning of the current sentence with relation to the discourse. That is, the word meanings (semantics), the sentence pattern (syntax) and the world knowledge are fundamental components for the sentence comprehension. Our main motivation and goal are to deal with the NL processing from the standpoint of manmachine communication, and with the above consideration, to write a syntax and semantics analysis program under the knowledge-based system ~US which has capabilities of deductive inference and automatic program generation of database access. Knowledge Representation In natural language analysis, knowledge representation to parse the sentences and extract their meanings is most crucial. The various kinds of parsers have been revised by experts; among them, the ATN parser by W. Woods is widespread and used elsewhere. It is essentially a context-free grammar based one. Further, to represent the semantic structure of the sentences, the frame theory revised by M. Minsky is widespread. The system using the ATN or ATN-like parser and the frame or frame-like structure, present them in semantic networks. On the other hand, predicate calculus oriented knowledge representation is also available to language processing. We will adopt predicate calculus oriented representation of knowledge for NL processing and database definitions. In database applications, predicate logic is of greate advantage to define intensional and extensional data to which the deductive inference rule can be applied. Moreover, it has been pointed out that there exist similarities between natural language and predicate logic. But as the expressive power of first order logic is rather restricted, we will extend the formalism of first order logic. This formalism will be given in the next chapter. Programming Langua$e and Methodology To make a computer understand natural language, we must program "the understanding mechanism" and give "the state of the world" to the machine. It should be noticed here that natural language contains ambiguous expressions in itself. It is usually very difficult how to process ambiguities of sentences. In general, "ambiguity" may be a property of subjects but should not be a property of the processing mechan&sm; the solution of the subject and the subject itself may be given plausibly or uncertainly but not the solution mechanism itself. With this consideration, we adopt the logic programming method which can be involved in KAUS's system language. By this method, we can design a sentence analysis program in the top-down style. The logic programming with use of the KAUS's formal logic is perspicuous, by which the modification, updating and extension can be made easily. In particular, the correspondences between the form of natural language expressions and the system's own formulas can be defined fairly in the straightforward manner, which are referred to in turn by the deductive inference and retrieval mechanism to translate the former into the latter. Efficiency of Processing With respect to the efficiency of processing, the program written in KAUS's logic programming style may be slightly less comfortable than the program written altogether in the procedural form becuase of the necessity of the execution of deductive retrieval program. However, the efficiency would be slightly sacrificed at the moment for the sake of clarity of the program. Outline of KAUS In this chapter, we describe briefly about an knowledge based system called KAUS(Knowledge Acquisition and Utilization System) which was realized in accordance with the design philosophy presented in the paper [6] and [7]. KAUS is constructed on the basis of the axiomatic set theory which has capabilities of deductive inference and automatic program generation for database access. It can be also applied to the logic programming of the semantic processing of natural language. In the following, we focus our discussions to the characteristics of knowledge representation formalism, the deductive inference rule and the system language features. Knowledge Representation Formalism We think of words as representing concept or property sets of things, and organize them into a structure on the basis of their set-theoretic implications. We will call such a structure SKELETON STRUCTURE. Using this formalism, we can represent semantic categories of words in hierarchical order. For example, the following set relations are structurized into Hereupon, let us consider the ordinary first order predicate logic at a moment. In the first order logic, domains of variables are described by using predicates. For example, "every man walks" is represented by (Vx)[MAN(x) ...... > WALK(x)](3) where MAN(x) is used for the domain restriction of the variable X. Thus, this restriction can be interpreted as "x is an element of MAN". Moreover, if it were required to answer the question "does every boy walks?", the following axiom would have to be given. (Vx)[BOY(x) ..... -> MAN(x)] (4) Then, using (3) and (4), the above question can be evaluated by Resolution Principle. But such descriptions as (3) and (4) are rather cumbersome. In place of them, we give the following (5) and (6) which have the same interpretation as (3) and (4) respectively. (Vx/MAN)[WALK(x)] (5) MAN ~ BOY (6) where the prefix (Vx/MAN) in (5) which can be included in our axiom set described later denotes "for every x~ MAN" The set relation (6) can be also given in our skeleton structure. Using both (5) and (6), we can derive the answer of the above question faster than the ordinary first order logic where such representations as (3) and (4) are used. This is the merit of the skeleton structure and will be much more clarified in the section of Deductive Inference Rule. Atom Format Definition Set In the first order logic, constants, variables and functions are recognized as terms. Then, an atom is defined as P(t~, t2,..., t~) where P is a n-place predicate symbol and t~ (i = l,...,n) is a term. A formula is defined by using such atoms, logical connectives and quantifiers. However, when we wish to translate a natural laguage expression into a predicate form in the above formalism; we cannot directly handle phrases and clauses both of which are used as verb objects or verb complements. Therefore, we extend the atom definition as follows: i). a formula can be permitted as a term besides constants, variables and functions 2). a function can be used as an atom --we call this type of an atom PROCEDURAL TYPE ATOM (PTA), while the other type atom is called NONPROCEDURAL TYPE ATOM (NTA). (note: because of permitting a PTA as an atom, we can perceive that our logical formulas afford a sort of logic programming facilities.) The atom format definition set provide us with a definition of a correspondence between some syntactic features bf natural language and our logical formulas. In addition, PTA definitions are also given in the set. In the figure 2, some examples are illustrated , where all of the used atom formats conform to the following standard format definitions: NTAI : (V S X, X2 P) (7) NTA2 : (A 0 V) (8) PTA ,: '(F Y,---Y~; X~---X~)(9) NTAI is usually used for representing a simple sentence, while NTA2 is used for representing an Adj.× Noun phrase. In the NTA] definition, V denotes a predicate symbol. S, XI, X~ and P are terms, of which S is usually used as the subject of V, and P is a formula which denotes the modifiers concerning time, locus, goal, reason and manner of V. Some of the terms may be omitted according to the syntactic feature of a sentence. In the NTA2 definition, (A 0 V) denotes "the Attribute of Object is Value". Finally, in the PTA definition, F denotes a function name which takes input variables Xt ,.. .., X~ and output variables Yi ,..., Y~ • It must be remarked here that some of the variables in (7), (8) and (9) may be bounded by their domains or semantic categories, but they were omitted for the sake of simplicity. "X give Y to Z ..... (GIVE X Z Y P) "red X ..... (COLOR X RED) "two X' .... (CARDINAL X 2) "X on Y" --- (ON X Y P) "X be father of Y" --- (FATHER Y X) "X + Y = Z" --- '(SUM Z; X Y) "X / Y : Z ..... '(DIV Z; X Y) "sin(X) = Y ..... '(SIN Y; X) Figure 2. Examples of Atom Definition Axiom Set The axiom set comprises descriptions of the world which specify general facts such as causality relations of things and sentence paraphrasing, and individual facts such as "John gave a red car to Mary". These facts are represented by formulas in the standard form Q (~Fv G ) (I0) Where Q represents quantification of variables, taking the form (q, V~/d, )(qzvz/d~ )... (qKv,/dK), in which q~ denotes a quantifier symbol V or 3, vz is a variable name and dz indicates the domain of the variable VZ. F and G represent premise and conclusion respectively in the formula; namely, F --~ G. More clearly, F is constituted from NTAs and PTAs by using logical connectives A , V ands, and also the same is G except that no PTAs are permitted. The internal representation of a formula in KAUS is an AND-OR tree to whose root node the quantification part Q is attached (see Figure 3). Now, we have here several examples* • Example I. We can represent a causality relation of "give" as follows: (Vx/PS)(Vy/PS)(Vz/PO)[~(GIVE x y z (TIME past))v((HAVE y z (TIME present))A ~(HAVE x z (TIME present)))] This says that "if X ~ PerSon) gave Z ~ Physical-Object) to y (~PerSon), then y has Z and X has not Z at the moment". Example 2. We can paraphrase "x give z to y" to "y receive z from x" • (Vx/PS)(Vy/PS)(Vz/PO)(Vp/VMOD) [--~(GIVE x y z p)v (RECEIVE y z x p)] where p/VMOD specifies that p is any formula which qualifies the predicates. Example 3. We can express the meaning of "float" as follows : (Vx/PO)(Vy/LQ)(Vu/RNUM)(Vv/RNUM)(Vp/VMOD) [~((SPEC-GR x U)A (SPEC-GR y v)A'(LT u v)) v (FLOAT x y p)] This says that "if the specific gravity of × is U, the specific gravity of y is V and U is less than V , then X float on y". Example 4. The fact, "John gave a red car to Mary" is represented as follows : [(GIVE #JOHN #MARY CAR (TIME PAST)) A (COLOR CAR RED)^ (QF CAR A)] Example 5. The fact, "John drinks coffee after John plays tennis" is represented as follows : (DRINK #JOHN COFFEE (TIME (AFTER (PLAY #JOHN TENNIS (TIME PRESENT))))) All of these general/individual facts can be put into the axiom set and they will be referred to through the deductive retrieval mechanism in KAUS. *). The real notation implemented by KAUS is partly modified (see 3.3 System Language). In the previous section, we have given the formalism of knowledge representation, where the concepts and properties of words can be partially ordered according to their set-theoretic implications. Moreover, we have defined the correspondence between the syntax features of natural language and logical expressions somewhat in the straightforward manner. We have also described how to represent universal/individual facts in our axiom set. However, it must be stressed here that these three types of knowledge are not independently presented in the knowledge base but interrelated each other with respect to domains of variables of atoms; that is, the bidirected access paths to each constituent of the knowledge base are mutually defined (see Figure 3). By making much use of these access paths, the knowledge only necessary for deduction can be retrieved efficiently. The deduction is performed by the inference rule comprising the following four components: i). S R: Selection Rule for a literal resolved upon 2). TIC: Test for Implicative Condition 3). R R: Replacement Rule 4). T T: Test for Termination of deduction SR(Selection Rule) A literal resolved upon is selected from the query tree. The selection criterions are as follows. First, the left-toright search for a non-evaluated NTA(non-procedural type atom) is made in the tree. When it is found, it is checked whether a constant or 3-variable is contained in the NTA. If it does the case, this NTA is took as a candidate resolved upon. If no such a literal is presented in the tree, certain counterplans are executed. The details would be omitted here. TIC(Test for Implicative Condition) After the candidate literal to be resolved upon is decided, axioms which contain the literals with the same predicate symbol as the candidate literal's but with the opposite sign are retrieved in the knowledge base. At that occasion, the search domain is narrowed to the subset of the axiom set by indexing the variable domains of the " 392-literal resolved upon. The skeleton structure is referred to for this purpose. Now, let us denote P as such a literal searched in the knowledge base and C as a candidate literal resolved upon. TIC checks the implicative conditions of P ---> C. One of the conditions to be checked is the set relation of the P's variable domains to the corresponding C's variable domains. The Table 1 shows this condition. Let us consider an example: P says that "a certain particular girl is loved by every man". On the other hand, C says that "every boy loves some woman" in the ordinary sense. In this case, P ---> C can be established because all the corresponding variable pairs (X, U), (y, V) and (p, q) satisfy the condition in Table i then, P ---> C can never established in spite of satisfaction of Table i, because the most general significant unifier does not exist, TIC tests this condition, too. RR(Replacement Rule) Let F be an axiom containing P and let G be the query clause containing C. RR generates a new query clause R after substitution o has been done. That is, Co is replaced with (Fo -Po), resulting in R: QR (G~ -C~) .(Fo -P~) where • denotes that (Fo -Po) should be attached to the place where C was resided. Substitution o is defined in Table I. TT(Test for Termination) When all the nodes in the query tree have been evaluated, deduction is terminated. Though we have said that all NTA nodes in the query tree are evaluated by means of the above SR, TIC and RR, we have not said almost anything about PTAs. We must now mention about them. PTAs in the query tree are in general, ready to be evaluated just at the time when all its input variables have been filled up with their values. Then, the evaluation is performed by calling the subroutines attached to PTAs. 3,3 System Language The system language provide us with fucalties of constructing knowledge base and relational database. Besides that, it can be used as substitute for logic programming and query repre- The syntax of the system language has already been supposed in the section 3.1. At this section, we present the really implemented features of the language briefly. Details are excluded because this is not the purpose of this paper. The syntax of the system language is based on the tuple (V0, Vi,... , V n) where V 0 is either a system command name, a predicate symbol or a variable whose domain is a PREDICATE; Vi(i ¥0 ) is a term as which a string and a numerical constant and a formula are permitted. The system language has the following characteristics that are not included in the first order logic: i where the symbol $ attached to X denotes that X is a variable and the symbol ? denotes that the expression is a query. 3). A recursive expression can be permitted. 4). A procedural type atom PTA ---a function ---can be permitted as an atom. For this reason, an aspect of logic programming is obtained. Syntax and Semantics Analyses In syntax and semantics analyses, word meanings, sentence patterns and world knowledge are fundamental. Characteristics of the sentence analysis partly depend on knowledge representation used. As described in the chapter 3, we represent knowledge in the framework of sets and logic in KAUS. The characteristics of our sentence analysis program are that, during the analysis, we use the rather direct correspondence between the basic sentence patterns (syntax) of natural language and the extended atomic formulas in KAUS, and that the pattern matching method can be used together with the deductive inference rule. A more characteristic is that words in a clause are put into four groups preserving the word order, each of which contains subjects, the main verb, direct objects/complements and indirect objects/complements respectively. In this chapter we present the English sentence analysis program using the above method. Descriptive Presentation of the analysis The analysis takes the following steps: i. The parts of speech of each word in the input sentence and atom format definitions attached to each predicative/attributive word such as verbs, adjectives, prepositions and the others are fetched from the knowledge base. 2. The input sentence is partitioned into a set of clauses by considering the positions of conjunctions, relative pronouns and punctuation marks. 3. Words in a clause are put into four groups preserving the word order in the clause, each of which may contain subjects, the main verb, direct objects/complements respectively. For this purpose, the correspondence relation between the NL syntax and the atomic formula syntax is utilized. 4. Each phrase in the four groups is decided whether it indicates qualification of nouns or of predicates. 5. If there are AND/OR conjunctions in a clause it is decided whether they are in the scope of a preposition or not. 6. After all of the words in a clause have been interrelated each other, the remaining clauses are processed by repeating 3 to 5. 7. After all of the clauses have been processed, substantiation of each personal and demonstrative pronoun is established. 8. In consequence, the extended formula deduced from the input sentence is obtained. To get comprehension of the above description, let us consider the next example: IN HIBIYA-PARK, JOHN MET JACK AND MARY. (16) This is the case that the sentence comprises only one clause. On this sentence, the basic sentence pattern of "meet", that is, "PERSON1 meet PERSON2", is fetched from the atom format definition set. The atom format definition of the preposition, "in" is also fetched; that is, "THING in PLACE". But this will not be used in this case. Then, grouping of words in the sentence is made according to the pattern "PERSON1 meet PERSON2", resulting in ; an empty group where the words marked with the star symbol * denote the instances of PERSON1 and PERSON2. This can be established by using the skeleton structure in which JOHN, JACK and MARY are defined as elements of PERSON. In the next place, the phrase "IN HIBIYA-PARK" is decided as indicating qualification of the verb "MET". The conjunction "AND" is then determined as to be in the scope of the conjunction of direct objects of "MET". As the final result, we obtain the following formula: Let us consider a more complex sentence which contains a relative clause and a personal pronoun in it: JOHN GAVE A RED CAR TO MARY WHO HE LOVES. (18a) JOHN GAVE A RED CAR TO MARY WHO LOVES HIM.(18b) In this case, both of the sentence (18a) and (18b) are split into the two clauses respectively; among which, each nucleus clause of (18a) and (18b) is the same, that is, "John gave a red car to Mary"; but the relative clause in (18a) means that "John loves Mary" while that in (18b) means that "Mary loves John". On the nucleus clause, the basic sentence pattern "PERSON1 gave THING to PERSON2" for "give" and "red THING" for the attributive adjective "red" are fetched from the knowledge base. Then, grouping of words in the clause is made according to the basic sentence pattern, resulting in group 1 : [ JOHN* ] group 2 : [ GAVE ] group 3 : [ A RED CAR* ] group 4 : [ TO MARY* ] In the next place, the pattern "red THING" is applied to "RED CAR" in the group 3, and in consequence, the formula (COLOR CAR RED) is derived, We translate the indefinite article "a" into the formula (QF CAR A) to denote that "car" is qualified by "a" in the clause. Thus, all the semantic relations among words in the nucleus clause "John gave a red car to Mary" have been derived: (GIVE #JOHN #MARY CAR (TIME PAST)) A(COLOR CAR RED)A(QF CAR A)(19) The relative clause in (18a), "who he loves", is transformed to (LOVE HE MARY (TIME PRESENT)), and "who loves him" in (18b) is transformed to (LOVE MARY HE (TIME PRESENT)). This can be attained by making use of the basic sentence pattern, "PERSON1 love PERSON2". In case of "who he loves", the following four word groups are initially generated by using this pattern. ---(WHO) HE LOVES --group 1 : [ (WHO) HE* ] group 2 : [ LOVES ] group 3 : [ # ] ; an empty group group 4 : [ ] ; not to be used Then, taking account of the state of the group 1 and the group 2, the antecedent of "who" is decided to be the direct object of "love". In the last place, the personal pronoun "he" is substantiated by"John". The similar discussion can be given to the case, "who loves him", and therefore, it is omitted here. The final results of the analysis of (18a) and (18b) So far, we have been concerned with declarative sentences. With regard to interrogative and imperative sentences, we transform them to declarative sentences prior to the successive analysis of them. Further, passive sentences have not been taken into account of hitherto. The passive voice is especially used in sentences in which it is unnecessary or undesirable to mention the agent, though the agent may be expressed by means of an adjunct with "by,'. The verbal meaning of the passive voice may also be brought out by adjuncts expressing other adverbial relations, such as time, manner, cause or instrument. A passive sentence may be analyzed with adaptation of the special treatment of the verb "be" followed by a past participle. where the [ ] is used to denote special attention to readings. The special treatment of the verb "be" is not only introduced to passive sentences but also to the other fragments shown in Table 2. For example, a sentence pattern with the formal subject Of "be" is treated as It should be denoted here that both of (23) and (24) have been transformed to a similar form in terms of (A 0 V) atoms. To-infinitive P.P. : Past Participle Logic Program By using a logic programming method, we can clearly show what should be done in the program depending on an approach taken for the sentence analysis, in which modification, updating and extension can easily be made. In the previous section, we have shown our approach by which a sentence may be analyzed rather intuitively and straightforwardly in the framework of KAUS. According to this method, we present, in this section, a part of the program by which an input clause is analyzed yielding an extended formula in KAUS. The execution of the program is undertaken by the deductive retrieval process with use of the merits of our knowledge representation. In the following representation of the program, we made convention that the top formula is concluded if all of the successive formulas indented are evaluated (proofed). The first program denotes that an input clause S is translated into an extended formula EP by evaluating the successive thirteen indented formulas, of which the ARGUMENT formulas are in turn to be replaced with the premises of the second or the third ARGUMENT program, or the other ARGUMENT programs that are not exhibited here, by the deductive inference rule. Then, these premises are in turn to be evaluated. The third ARGUMENT program exhibited above is concerned with a relative clause except where-and prep.-relative-noun clauses. The other NTAs in the first program may be evaluated in the similar way. The figure 4 shows a sample process of the program, where the main terms used in the program are also shown by categorizing them hierarchically in the skeleton structure. Conclusion We have described the syntax and semantics analyses of natural language (English) within the framework of the intelligent man-machine system KAUS. The more the volume of data related to the sentence analysis is enlarged in the knowledge base and the sentence analysis program itself is extended, the more the class of acceptable sentences will be broadened. This may be ensured by the method described hitherto. Fiqure I. An Example of Skeleton Structure PERSON D MAN , PERSON ~ WOMAN MAN ~BOY , WOMAN ~GIRL , MANn WOMAN = MAN ~ #JOHN , WOMAN ~ #MARY (I) In the figure, the relation of set inclusion is uniformly represented as the element-set relations by using the concept of power sets. Then the power set of PERSON (we denote it by @PERSON) comprises all the subsets of PERSON: @PERSON = [ MAN, WOMAN, BOY, GIRL .... .... #JOHN, #MARY .... ] HIBIYA-PARK, JOHN* ] [ MET ] [ JACK* AND MARY* ] [ ¢ ] [ (MEET JOHN JACK (TIME PAST)~(PLACE (IN HIBIYA-PARK)))A(MEET JOHN MARY (TIME PAST) A(PLACE (IN HIBIYA-PARK)))] are (GIVE #JOHN #MARY CAR (TIME PAST)) A(COLOR CAR RED)^ (QF CAR A) ^(LOVE #JOHN #MARY (TIME PRESENT)) (20a) (GIVE #JOHN #MARY CAR (TIME PAST)) A (COLOR CAR RED) A (QF CAR A) A(LOVE #MARY #JOHN (TIME PRESENT)) (20b) For example, JOHN IS LOVED BY MARY. ---> [JOHN] IS [LOVED] [BY MARY]. ---> MARY LOVES JOHN. ---> (LOVE #MARY #JOHN (TIME PRESENT)) (21) THE BOOK IS WRITTEN IN ENGLISH. ---> [THE BOOK] IS [WRITTEN] [IN ENGLISH]. ---> [$X] WRITE THE BOOK IN ENGLISH. ---> (3x/PS)[(WRITE x BOOK (MANNER (IN ENGLISH))A(TIME PRESENT)) A(QF BOOK THE)] IT IS EASY TO PLEASE JOHN, ---> [TO PLEASE JOHN] IS [EASY]. ---> (GRADE-DIFFI (TOINF (TO PLEASE #JOHN)) EASY) (23) IT IS SNOOPY THAT HAS STOLEN THE FISH. ---> [THAT HAS STOLEN THE FISH] IS [SNOOPY]. ---> (STRESS-VAL (STEAL #SNOOPY FISH (TIME PRES.PERFECT)) #SNOOPY) A(QF FISH THE) -PARK, JOHN MET JACK AND MARY. VGP = [MET] SGP = [IN HIBIYA-PARK, JOHN] XGP = [JACK AND MARY] YGP = [@] ; not used VP = [VPI] cf. VPI: Verb × Direct Object SDOM = [PERSON] XDOM = [PERSON] YDOM = not used MVERB = [MEET] SUBJ = [JOHN] XOBJ = [JACK, MARY] YOBJ = not used EF = [(MEET #JOHN #JACK (TIME PAST) A(PLACE (IN HIBIYA-PARK))) A (MEET #JOHN #MARY ....... )))] Figure 4. A Sample Process by the Program . However, if P and C areP: (Vx/BOY)(~y/WOMAN)(vp/VMOD) [(LOVE x y p)] (13) C: (Iv/WOMAN) (Vu/BOY) (Vq/VMOD) [(LOVE u v q)] Table i . iImplicative ConditionQF~ Q~ QA~ XR~ CONDITION V V V Xp~ n X~ X~ n X~ :~ V 3 3 X~,. X~: ~ Xz; 3 V 3 X~ XP,. ~ XZ~ --- V V X~ ......... --- 3 3 X~ ......... V --- V X~ ......... 3 --- 3 Xr~ ......... 3 3 (non-implicative) P: Qp(Xp, Xr, ~ " -CT: negated form of C: Q~ (X~, X~, X~ query C R: QR(X~, ~= ~ R: replaced form of sentations. ). Variables can be explicitly specified. For example, [AX/MAN][EY*/WOMAN] where A and E denote the universal andexistential quantifier respectively, and the symbol * attached to Y denotes that Y is a query variable. 2). A predicate symbol itself can be a variable. For example, [EX*/PRED]($X, #JOHN, #MARY)? Table 2 . 2Treatment of the Fragment of "X be Y " THERE be N, Prep. N~ (PREP Nf N~) IT be Adj. TO-INF (ATTR TO-INF Adj.) IT be Z THAT-CLAUSE (ATTR THAT-CLAUSE Z) N be P.P. Adjunct (PRED S U V P)X be Y Atomic Formula N be Adj. (ATTR N Adj.) N~ be Prep. N~ (PREP N, N2) N, be ReI.N of N~ (REL.N N~ N,) ReI.N of N, be N~ (REL.N N, N~) N, be N~ (ATTR N, N~) note. N : Noun Adj. : Adjective Prep. : Preposition ReI.N : Attributive noun TO-INF: AcknowledgementThe author is indebted to associate professor Setsuo Ohsuga of University of Tokyo, who gave the author helpful suggestions in the presentation. . ARGUMENT EARG EDOM EGP) '(MARK EGP. ARGUMENT EARG EDOM EGP) '(MARK EGP ; EGP) '(MAKE-SET EARG ; EGP) (ARGUMENT EARG EDOM empty) '(SEARCH REL ; INPUT) '(NON-EQ REL WHERE) '(ANTEC X ; REL INPUT) '(PRE-W Z ; REL) '(SYN-CAT K ; Z) '(NON-ELM K PREP. Edom Egp) &apos;(unmark-Prep-Obj Egp, PUT-ELM EARG ; X)EDOM EGP) '(UNMARK-PREP-OBJ EGP ; EGP) '(MAKE-SET EARG ; EGP) (ARGUMENT EARG EDOM empty) '(SEARCH REL ; INPUT) '(NON-EQ REL WHERE) '(ANTEC X ; REL INPUT) '(PRE-W Z ; REL) '(SYN-CAT K ; Z) '(NON-ELM K PREP) '(PUT-ELM EARG ; X) . Earg Egp) &apos;(adj-Mod, Adj, EARG EGP) '(ADJ-MOD ADJ ; . EARG EGP) '(NPREP-MOD NPREP. EARG EGP) '(NPREP-MOD NPREP ; . Earg Egp) &apos;(make-Ef Emod ; Det Adj Nprep, EARG EGP) '(MAKE-EF EMOD ; DET ADJ NPREP) TENSE-NOD TENSE ; VGP) (AUX-MOD AUX ; VGP) (VPREP-MOD VPREP. Vmod-Atom, Vmod, Sgp, Ygp, VGP SGP XGP YGP. ADV-MOD ADV(VMOD-ATOM VMOD VGP SGP XGP YGP) (TENSE-NOD TENSE ; VGP) (AUX-MOD AUX ; VGP) (VPREP-MOD VPREP ; VGP SGP XGP YGP) (ADV-MOD ADV ; . Sgp Xgp Ygp) (to-Inf-Mod Inf Vgp Sgp Xgp Ygp ; Make-Ef Vmod ; Tense Aux Vgp, Vprep, Inf, VGP SGP XGP YGP) (TO-INF-MOD INF VGP SGP XGP YGP) (MAKE-EF VMOD ; TENSE AUX VPREP ADV INF) . __=, __= . ********************************************* Reference, ********************************************* REFERENCE Case Systems for Natural Language. Bertram Bruce, Artificial Intelligence. 6Bertram Bruce, Case Systems for Natural Language, Artificial Intelligence 6, 1975. Symbolic Logic and Mechanical Theorem Proving. C L C Chang &amp; R, Lee, Academic PressC.L. Chang & R.C.To Lee, Symbolic Logic and Mechanical Theorem Proving, Academic Press, 1973. Developing a Natural Language Interface to Complex Data. G G Hendrix, Proc. 3rd VLDB, Part II. 3rd VLDB, Part IIHendrix G.G. et al., Developing a Natural Language Interface to Complex Data, Proc. 3rd VLDB, Part II, pp.37-58, 1977. Herve Gallaire, &amp; Jack Minker, Logic and Databases. New YorkPlenum PressHerve Gallaire & Jack Minker (edited), Logic and Databases, Plenum Press, New York, 1978. Some Relations between Predicate Calculus and Semantic Net Representations of Discourse. Robert F Simmons, 2Robert F. Simmons, Some Relations between Predicate Calculus and Semantic Net Repre- sentations of Discourse, 2nd IJCAI, 1971. Semantic Information Processing in Man-Machine Systems. S Ohsuga, Proc. IEEE. IEEES. Ohsuga, Semantic Information Processing in Man-Machine Systems, Proc. IEEE, 1977. Toward Intelligent Interactive Systems. S Ohsuga, Proc. The IFIP W.G. 5.2 Workshop Seilac II on Methodology of Interaction. The IFIP W.G. 5.2 Workshop Seilac II on Methodology of InteractionNorth HolandS. Ohsuga, Toward Intelligent Interactive Systems, Proc. The IFIP W.G. 5.2 Workshop Seilac II on Methodology of Interaction, North Holand, 1979.

Une approche fondée sur les lexiques d'analyse de sentiments du dialecte algérien

La plupart des outils d'analyse de sentiments traitent essentiellement l'arabe standard moderne (ASM), et peu d'entre eux ne prennent en considération les dialectes. À notre connaissance, aucun outil en libre accès n'est disponible concernant l'analyse de sentiments de textes écrits en dialecte algérien. Cet article présente un outil d'analyse de sentiments des messages écrits en dialecte algérien. Cet outil est fondé sur une approche combinant l'utilisation de lexiques ainsi qu'un traitement spécifique de l'agglutination. Nous avons évalué notre approche en utilisant deux lexiques annotés en sentiments et un corpus de test contenant 749 messages. Les résultats obtenus sont encourageants et montrent une amélioration continue après l'exécution de chaque étape de notre approche.ABSTRACT. Most of the sentiment analysis tools process only Modern Standard Arabic (MSA). Indeed, few dialects are considered by the actual tools, in particular Algerian dialect where we do not identify any free tool carrying texts of this dialect. In this article we present a tool for sentiment analysis of messages written in Algerian dialect. This tool is based on an approach which uses both lexicons and specific treatment of agglutination. This approach was experimented using two sentiment lexicons and a test corpus containing 749 messages. The obtained results were encouraging and showing continuous improvement after each step of the considered approach.MOTS-CLÉS : analyse de sentiments, dialecte algérien, lexique de sentiments, agglutination.

Une approche fondée sur les lexiques d'analyse de sentiments du dialecte algérien Imane Guellil i_guellil@esi.dz http://www.esi.dz * École Supérieure des Sciences Appliquées d'Alger Laboratoire des Méthodes de Conception des Systèmes. École nationale Supérieure d'Informatique Oued-Smar BP 68M, 16309AlgerAlgérie * -Faical Azouaou f_azouaou@esi.dz * -Houda Saâdane houda.saadane@geolsemantics.com -Nasredine Semmar nasredine.semmar@cea.fr GEOLSemantics ESSA-Alger * 12 Avenue Raspail94250GentillyFrance Laboratoire Vision et Ingénierie des Contenus CEA LIST 91191Gif-sur-YvetteFrance Une approche fondée sur les lexiques d'analyse de sentiments du dialecte algérien TAL. Volume 58 -n o 3/2017, pages 41 à 65 42 TAL. Volume 58 -n o 3/2017sentiment analysisalgerian dialectsentiment lexiconagglutination La plupart des outils d'analyse de sentiments traitent essentiellement l'arabe standard moderne (ASM), et peu d'entre eux ne prennent en considération les dialectes. À notre connaissance, aucun outil en libre accès n'est disponible concernant l'analyse de sentiments de textes écrits en dialecte algérien. Cet article présente un outil d'analyse de sentiments des messages écrits en dialecte algérien. Cet outil est fondé sur une approche combinant l'utilisation de lexiques ainsi qu'un traitement spécifique de l'agglutination. Nous avons évalué notre approche en utilisant deux lexiques annotés en sentiments et un corpus de test contenant 749 messages. Les résultats obtenus sont encourageants et montrent une amélioration continue après l'exécution de chaque étape de notre approche.ABSTRACT. Most of the sentiment analysis tools process only Modern Standard Arabic (MSA). Indeed, few dialects are considered by the actual tools, in particular Algerian dialect where we do not identify any free tool carrying texts of this dialect. In this article we present a tool for sentiment analysis of messages written in Algerian dialect. This tool is based on an approach which uses both lexicons and specific treatment of agglutination. This approach was experimented using two sentiment lexicons and a test corpus containing 749 messages. The obtained results were encouraging and showing continuous improvement after each step of the considered approach.MOTS-CLÉS : analyse de sentiments, dialecte algérien, lexique de sentiments, agglutination. Introduction L'arabe est une langue riche et complexe utilisée par plus de 400 millions de locuteurs dans le monde (Siddiqui et al., 2016). Cette langue est cependant dans un état de diglossie 1 dans les pays où elle est utilisée car elle coexiste avec vingts-deux dialectes (Sadat et al., 2014). L'intérêt porté à l'arabe et ses dialectes a largement augmenté au cours de ces dernières années. Un intérêt principalement dû à la proportion des locuteurs arabes au sein des médias sociaux. Au cours de la dernière décennie, plusieurs travaux ont été menés sur l'arabe et ses dialectes. Une revue des méthodes et résultats pour le traitement de l'arabe dialectal a été réalisée par Shoufan et Alameri (2015). Quatre types de tâches y sont présentés : 1) l'analyse basique, 2) la construction de ressources, 3) l'identification du dialecte utilisé, et 4) l'analyse sémantique. La richesse des médias sociaux en termes d'opinions, d'émotions et de sentiments a suscité l'intérêt de la communauté de recherche à se pencher beaucoup plus sur les problématiques reliées à l'analyse sémantique et plus particulièrement sur l'analyse de sentiments de l'arabe et ses dialectes. L'analyse de sentiments consiste à déterminer la valence (positive, négative ou neutre) d'un message donné. Plusieurs approches d'analyse ou de classification de sentiments ont vu le jour : 1) l'approche supervisée : utilisant les techniques d'apprentissage, elle est fondée sur les corpus annotés, 2) l'approche non supervisée : fondée sur l'existence d'un lexique de sentiments contenant un ensemble de termes, leur valence et leur intensité pouvant aller de − 1 à + 1 ou encore de − 5 à + 5, et 3) l'approche hybride : combinant les deux approches précédentes. Toutes ces approches ont été adoptées dans le cas de l'arabe et de ses dialectes. Cette adoption, ou adaptation, véhicule des problèmes liés à l'approche à laquelle sont ajoutés les problématiques de l'arabe et ses dialectes. Une présentation de ces problèmes est donnée dans (Guellil et Boukhalfa, 2015). Ces problèmes sont principalement liés au manque de corpus annotés et aux problèmes quantitatif et qualitatif des lexiques de sentiments. Ces problématiques sont recensées dans la littérature comme suit : 1) les problématiques orthographiques liées à la diacritisation ainsi qu'aux différentes manières d'écrire chaque lettre arabe, et 2) les problématiques morphologiques liées à la dérivation, la flexion et l'agglutination. En plus de ces problématiques, chaque dialecte rajoute des problématiques supplémentaires. Prenons par exemple le dialecte algérien (qui est l'objet de ce travail), il s'agit d'un dialecte maghrébin utilisé par plus de 40 millions de locuteurs (ce qui représente 10 % de la population s'exprimant en arabe). Il souffre cependant d'un manque considérable de travaux, d'outils et de ressources. En plus des problématiques de ce dialecte partagées avec l'arabe standard, il dispose d'une très forte richesse du vocabulaire provenant de plusieurs autres langues. Dans (Meftouh et al., 2012), les auteurs illustrent le fait que le dialecte algérien est composé de 65 % d'arabe, de 19 % de français et de 16 % de turque et de berbère. Ce dialecte dispose également d'une très grande richesse morphologique, par exemple, l'agglutination aux mots des pronoms personnels et les pronoms de compléments d'objet direct (COD), que nous trouvons aussi dans l'arabe standard, est étendue aux pronoms de compléments d'objets indirect (COI) ainsi que la négation. Dans ce travail, nous présentons et implémentons une approche d'analyse de sentiments (AS) du dialecte algérien (DALG) combinant l'utilisation de lexiques de sentiments et le traitement de l'agglutination. Cette dernière se compose de deux principales étapes : 1) construction d'un lexique de sentiments, et 2) calcul de la valence et intensité du sentiment d'un message donné. La première étape consiste à construire un lexique de sentiments en dialecte algérien en disposant d'un lexique en anglais. Dans la seconde étape, nous procédons à tous les traitements morphologiques nécessaires dans le cadre de l'analyse de sentiments. Pour évaluer notre approche, nous utilisons deux lexiques de sentiments en anglais : SentiWordNet 2 et SOCAL 3 . Nous construisons par la suite SentiALG et SOCALALG (représentant la version algérienne de ces lexiques). Nous utilisons deux corpus de test : 1) une partie annotée du corpus multidialectal PADIC (Meftouh et al., 2015), et 2) une partie contenant des messages (posts et commentaires) extraits du média social Facebook. La suite de l'article est organisée comme suit : nous présentons d'abord dans la section 2 les principales caractéristiques de l'arabe et de ses dialectes, ensuite nous exposons dans la section 3 les principaux travaux connexes menés sur l'analyse de sentiments de cette langue et de ses dialectes. La section 4 décrit l'approche que nous proposons pour l'analyse. Nous consacrons la section 5 aux expériences menées ainsi qu'à la présentation des résultats obtenus. La section 6 conclut notre étude et présente nos travaux futurs. Spécificités de l'arabe et ses dialectes : zoom sur le dialecte algérien La langue arabe est une des langues les plus parlées et utilisées dans le monde. Elle est la langue officielle de plus de vingt-deux pays parlée par plus de 400 millions de locuteurs et elle est utilisée comme vecteur de transmission religieux pour tous les musulmans au nombre de un milliard et demi (Saâdane, 2015) à travers le monde. Elle constitue ainsi un élément principal dans la culture et la pensée d'une partie importante de l'humanité et du patrimoine mondial (Saâdane et al., 2013). Elle est également la quatrième langue la plus utilisée d'Internet (Siddiqui et al., 2016). L'arabe dispose de trois principales variétés qui coexistent côte à côte à savoir : 1) l'arabe classique utilisé dans le Coran, livre sacré des musulmans, 2) l'arabe standard moderne (ASM) utilisé par les locuteurs arabes instruits dans leurs écrits et dans les conversations formelles à savoir dans le système éducatif et littéraire. La plupart des travaux de recherche s'appuient sur cette variante, 3) l'arabe dialectal qui constitue le moyen de communication de la vie quotidienne, employé dans les conversations informelles, interviews et la littérature orale. Spécificités de l'ASM L'ASM est classé dans le groupe des langues sémitiques contemporaines qui s'écrit de droite à gauche. L'alphabet arabe contient vingt-huit lettres dont vingtcinq consonnes et trois voyelles. En plus des voyelles, l'arabe utilise également des marques diacritiques correspondant à des voyelles courtes. Prenons par exemple la lettre qui se prononce (b). Si nous mettons au-dessus de cette lettre la diacritique fatha, la lettre devient : se prononçant : 'ba'. Si nous mettons la même diacritique mais au-dessous de la lettre (correspondant à la kasra), la lettre devient et se prononce 'bi'. Mise à part la diacritisation, les lettres arabes ont en général quatre manières de s'écrire : 1) au début du mot, elle s'écrit , prenons l'exemple du mot biŷr 4 'un puits', 2) au milieu du mot, par exemple ( Haqiyba 'une valise'), 3) à la fin du mot en étant attachée à lettres précédente, par exemple ( qalb 'un coeur'), et 4) à la fin du mot sans être attachée à la lettre précédente, par exemple ( baAb 'une porte'). Un mot en ASM peut également présenter plusieurs aspects morphologiques dont la dérivation, la flexion et l'agglutination. La dérivation consiste à représenter chaque mot sous la forme de « lemme-schéma ». Par exemple, les trois lettres « ktb » est un lemme lié à « l'écriture ». Dans les schémas que nous utilisons, les lettres du lemme sont remplacées par les chiffres 1, 2 et 3 dans l'ordre. Si nous appliquons par exemple le schéma « 1a2a3a » au lemme, nous obtenons le mot kataba 'Il a écrit'. La flexion représente les différentes variations grammaticales d'un mot pouvant être reliées à sa conjugaison, son passage au féminin, pluriel, etc. Le lemme « ktb » est donc conjugué au présent de la sorte : Âktubu 'j'écris', taktubiyn 'tu écris'/ féminin, etc. Ce verbe est conjugué différemment au passé. Par exemple la traduction de 'j'ai écrit' en arabe est katabtu. L'agglutination consiste à rassembler un ensemble de mots, de pronoms ( Spécificités du dialecte algérien Le DALG est utilisé principalement pour la communication orale de tous les jours (dans la vie quotidienne, les séries télévisées en Algérie, etc.). Il n'est pas enseigné dans les écoles, et reste absent des communications écrites officielles. Néanmoins ces dernières années, ce dialecte prend une place plus importante à l'écrit avec les médias sociaux (Harrat et al., 2017). Comme nous avons exposé les principales caractéristiques orthographiques et morphologiques du DALG partagées avec l'ASM au sein de la section 2.1, nous nous concentrons dans cette partie sur les caractéristiques propres au DALG. Spécificités orthographiques du dialecte algérien Le DALG fait appel à toutes les voyelles et consonnes utilisées par l'ASM. En plus de ces dernières, il fait appel aux trois lettres , se prononçant respectivement p, g, v (Meftouh et al., 2015). Le DALG est enrichi par les langues des groupes ayant colonisé ou géré la population algérienne au cours de l'histoire du pays. Parmi les langues de ces groupes, citons le turc, l'espagnol, l'italien et plus récemment le français (Saâdane et Habash, 2015 ;Saâdane, 2015 ;Meftouh et al., 2012). De ce fait, au sein du DALG, nous trouvons des mots tels que sallam 'saluer' et ayant comme origine l'ASM (Harrat et al., 2017). Nous pouvons également trouver un mot comme Farmliy 'infirmier' étant originaire du français, ou encore le mot baAbuwr 'bateau', issu du turc, šlaAγam 'moustaches' emprunté du berbère, zabla 'faute', issu de la langue italienne et siymaAna 'une semaine' emprunté de l'espagnol. Spécificités morphologiques du dialecte algérien Nous abordons au cours de cette partie quatre aspects importants reliés à la morphologie (agglutination) du DALG, à savoir : 1) la conjugaison, 2) la négation, Notons qu'en arabe il existe deux formes pour la deuxième personne du singulier, selon le sexe de la personne à qui l'on s'adresse. À partir du tableau 1, nous pouvons déjà conclure que les lettres représentent des préfixes et les lettres représentent des suffixes pour le DALG. Il est également à noter que la conjugaison au futur ne figure pas sur ce tableau car cette dernière est la même que celle au présent associé à des mots du futur tels que Awmbςd, raAH, γadwa , etc, voulant respectivement dire (demain, aller, après) (Harrat et al., 2016 Les COD et COI sont également agglutinés aux verbes conjugués en DALG, au même titre que les pronoms personnels et la négation. Les pronoms COD et COI représentent des suffixes du verbe conjugué. Ces pronoms ont déjà été étudiés (Guellil et Azouaou, 2017 ;Saâdane et Habash, 2015 ;Harrat et al., 2016). Il existe cependant un nombre plus important de pronoms que ceux cités dans ces travaux car l'agglutination des pronoms de base entre eux donne naissance à de nouveaux suffixes. Nous récapitulons dans le tableau 2, l'ensemble des COD et COI de base. 3. Analyse de sentiments de l'arabe et ses dialectes : état de l'art L'analyse de sentiments (AS) est un domaine interdisciplinaire se trouvant entre les domaines de traitement du langage naturel, de l'intelligence artificielle et la fouille de texte (Medhat et al., 2014). L'AS s'effectue sur trois niveaux : documents, phrases et aspects. La richesse des médias sociaux en termes d'opinion et de sentiment a suscité l'intérêt de la communauté de recherche (Guellil et Boukhalfa, 2015). Cet intérêt est aussi important pour la langue arabe compte tenu du nombre massif des utilisateurs s'exprimant en arabe et ses dialectes sur Internet : 156 millions d'utilisateurs selon Siddiqui et al. (2016), soit 18,8 % de la population globale d'Internet (Korayem et al., 2012). En nous fondons sur les caractéristiques de l'arabe et de ses dialectes présentées au sein de la section 2, nous concluons que les approches dédiées aux autres langues ne pourraient être appliquées dans notre cas (sauf avec modifications majeures). Au sein du présent travail, nous nous concentrons donc sur les travaux menés sur l'arabe et ses dialectes où nous nous fondons sur six états de l'art regroupant et analysant les travaux menés sur cette langue ainsi que ses dialectes (Kaseb et Ahmed, 2016 ;Biltawi et al., 2016a ;Korayem et al., 2012 ;Harrag, 2014 ;Assiri et al., 2015 ;Alhumoud et al., 2015). Après une analyse approfondie de ces études, nous concluons cependant que l'AS de l'arabe et de ses dialectes peut s'effectuer en suivant trois approches (comme pour toutes les autres langues) : supervisées, non supervisées et hybrides. Nous présentons, l'ensemble des travaux menés sur l'AS de l'arabe et ses dialectes tout en les regroupant par le type d'approche utilisé. Approches supervisées L'approche supervisée dépend de l'existence des données (documents, phrases, etc.) annotées comme positives, négatives ou neutres (Biltawi et al., 2016b). L'approche supervisée, reconnue également par la classification supervisée, peut se faire en faisant appel à plusieurs algorithmes de classification tels les machines à vecteurs support (MVS), les classifieurs bayésiens naïfs (BN), les arbres de décision (AD), etc. De nombreux travaux ont été réalisés pour analyser et classer les sentiments de l'arabe et ses dialectes en utilisant des approches supervisées. Citons notamment le travail de Cherif et al. (2015a) qui a fait appel à la technique MVS pour classer les sentiments de message écrit en arabe (ASM) en cinq classes allant de très bien à pas bien du tout. Pour réaliser cette tâche, les auteurs ont commencé par le prétraitement des phrases. Ils font également appel à un extracteur de lemmes présenté dans un travail précédent (Cherif et al., 2015b) afin de supprimer les préfixes et suffixes des mots pour obtenir leurs radicaux. Il faut cependant noter que ces auteurs suppriment les préfixes et suffixes reliés à la conjugaison, au pluriel et aux pronoms. Il ne supprime cependant pas les affixes reliés à la négation qui pourraient affecter la qualité de l'analyse de sentiments. Dans (Hadi, 2015) les auteurs ont utilisé les deux méthodes MVS et BN pour classifier un ensemble de messages en positif, négatif ou neutre. Pour ce faire, ils construisent un corpus arabe (ASM) contenant 3 700 messages extraits de Twitter. Chaque message a été annoté par trois locuteurs arabes natifs en positif, négatif et neutre. Nous enchaînons avec le système « SAMAR » analysant en parallèle la subjectivité d'un texte ainsi que ses sentiments (Abdul-Mageed et al., 2014). Ce travail se focalise principalement sur le ASM ainsi que le dialecte égyptien. Les auteurs utilisent plusieurs corpus dont certains extraits des médias sociaux et d'autres ayant été utilisés dans d'autres travaux tels que (Diab et al., 2010). Les auteurs de ce travail font appel à une variante de la MVS « light » proposée dans (Joachims, 2002). Ils se fondent cependant sur beaucoup de caractéristiques (features) dont l'analyse morphologique, la recherche des parties du discours, le lexique annoté, etc. Dans (Itani et al., 2012), les auteurs ont exploité un modèle BN pour classifier automatiquement les sentiments des posts Facebook écrits en plusieurs dialectes arabes. Ils se concentrent sur les dialectes syriens, égyptiens, irakiens et libanais. Nous finissons cette partie avec le travail de Mdhaffar et al. (2017) qui combinent plusieurs classificateurs pour analyser le sentiment de messages écrits en dialecte tunisien. Parmi les principaux classificateurs utilisés la MVS et le BN. Dans ce travail, les auteurs présentent également la construction du corpus TSAC qui est un corpus tunisien dédié à l'analyse de sentiments. Tous les travaux présentés au sein de cette catégorie se fondent sur un corpus annoté pour pouvoir effectuer la classification des sentiments. La construction de ce corpus est, dans la majorité des cas, manuelle, ce qui est très consommateur de temps et d'effort, et amène les auteurs à construire souvent des corpus réduits influant négativement les résultats. Du fait que notre approche s'appuie sur un lexique de sentiments, nous avons fondé les travaux présentés dans notre cas sur les différents prétraitements proposés. Approches non supervisées L'approche non supervisée est une approche qui se fond sur un lexique de sentiments. Plusieurs travaux ont également été menés en faisant appel à cette approche. Nous commençons par le travail de Al-Ayyoub et al. (2015) qui a permis de construire un lexique de 120 000 termes arabes (ASM). Pour aboutir à ce dernier, les auteurs ont commencé par collecter des lemmes en arabe. Ils les ont traduits en anglais en utilisant Google traduction. Ils ont supprimé ensuite les mots répétés. Ces auteurs ne prennent pas en considération le contexte du lemme dans le processus de traduction. Ils utilisent ensuite un lexique de sentiments anglais pour déterminer leur valence et intensité. Dans la même perspective, un autre lexique contenant 157 969 synonymes et 28 760 lemmes a été construit dans (Badaro et al., 2014). Pour aboutir à ce dernier, les auteurs ont dû combiner plusieurs ressources de l'arabe. Dans (Mohammad et Turney, 2013), les auteurs ont développé un lexique de sentiments contenant 14 182 unigrammes anglais classés en positif ou négatif à l'aide du Amazon Mechanical Turk 5 . Ce lexique a ensuite été traduit en quarante langues dont l'ASM. L'auteur dans (AL-Khawaldeh, 2015) a construit également un lexique de sentiments, mais en traitant aussi de la négation. Ces auteurs se consacrent sur l'arabe (ASM) et ont également défini un ensemble de règles pour capturer la morphologie de la négation. Abdulla et al. (2014a) ont commencé par la construction d'un corpus contenant 4 000 commentaires textuels collectés à partir de Twitter et Yahoo Maktoob 6 . Un lexique a alors été construit, il ne contient que 300 mots. Nous enchaînons avec les travaux de Abdulla et al. (2014b) qui se focalisent sur trois techniques de construction de lexiques avec une manuelle et deux autres automatiques. Pour la partie automatique, les auteurs se focalisent sur la traduction du lexique de sentiments anglais SentiStrength 7 en utilisant Google traduction. Nous terminons avec le travail de Mataoui et al. (2016) qui est le seul à étudier l'AS du DALG. Au sein de ce travail, les auteurs ont construit manuellement un lexique de sentiments en commençant par un lexique arabe et égyptien existant. Pour répondre aux caractéristiques morphologiques de cette langue et de ce dialecte, les auteurs utilisent l'outil de lemmatisation nommé « Khoja » 8 (Khoja et Garside, 1999). Nous constatons donc que l'approche non supervisée est essentiellement fondée sur la construction de lexiques. Pour aboutir à cette construction, les auteurs tendent vers trois techniques : 1) construction manuelle (dans ce cas-là ce n'est pas purement supervisé car les mots sont manuellement annotés), 2) combinaison entre plusieurs ressources existantes, 3) traduction d'une ressource existante. Pour notre cas et vu que l'approche de construction manuelle est très consommatrice de temps (en plus, elle a déjà été présentée dans (Mataoui et al., 2016)), que la combinaison de plusieurs ressources est impossible dans le cas du dialecte algérien, souffrant d'un manque considérable de ressources, nous optons pour une construction à base de traduction. Pour ce faire, nous nous appuyons sur les différents travaux de Abdulla et al. (2014a) 5. https ://www.mturk.com/mturk/welcome 6. L'édition arabe de yahoo. 7. http ://sentistrength.wlv.ac.uk/ 8. https ://github.com/motazsaad/khoja-stemmer-command-line et de Abdulla et al. (2014b), qui se focalisent sur des lexiques de sentiments existants pour faire la traduction vers l'ASM. En ce qui concerne les travaux sur le dialecte algérien, l'unique travail recensé est celui de Mataoui et al. (2016). Néanmoins, ce travail fait appel à l'outil « Khoja » pour la phase de lemmatisation. Cependant, cet outil est dédié à l'ASM et ne peut donc pas être utilisé pour le DALG. L'une des problématiques majeures reliées aux dialectes arabes est que les outils dédiés à l'ASM ne donnent pas de bons résultats pour ses dialectes (Harrat et al., 2014). En plus, les auteurs utilisent cet outil sans y apporter aucune modification. Pour illustrer l'incapacité de cet outil à traiter le DALG, nous avons lemmatisé un ensemble de phrases en DALG et nous avons constaté que cet outil ne traitait en aucun cas les mots agglutinés, tels que mAnnsAhAš 'je ne l'oublierai pas'. Il change aussi le sens de certains mots, comme le mot mliyH 'bien' qu'il lemmatise en mlH 'sel'. Ceci est principalement dû aux spécificités et suffixes du DALG qui ne sont pas partagés avec l'ASM. Un tel outil ne peut donner de bons résultats que pour des messages pouvant être classés comme des messages partagés entre l'ASM et le DALG, par exemple kraht HyaAtiy 'j'en ai marre de ma vie'. Approches hybrides L'approche hybride consiste à combiner les méthodes utilisées dans l'approche supervisée et non supervisée. Par exemple, dans le travail de Hedar et Doss (2013), les auteurs ont utilisé un classificateur MVS. Pour faire cette classification, les auteurs utilisent un lexique contenant 1 300 mots dont 600 sont positifs et 700 négatifs. Ce travail s'appuie sur l'argot égyptien. Les résultats expérimentaux ont montré que l'utilisation du lexique améliore considérablement les résultats. Dans (Khalifa et Omar, 2014), les auteurs ont proposé une méthode hybride fondée sur un lexique et un classificateur BN en même temps. La méthode proposée est précédée d'une phase de prétraitement (normalisation, segmentation, etc.). Le lexique intervient pour remplacer les mots avec leurs synonymes. Ces auteurs se focalisent sur l'arabe standard (ASM). L'approche hybride est une approche qui donne de très bons résultats, mais nous ne pouvons l'appliquer à ce stade puisque nous ne disposons pas de données annotées. Dans le but de synthétiser tous les travaux présentés, nous les classifions dans le tableau 3, par rapport à la langue étudiée (ASM, dialecte arabe en donnant le type du dialecte), ainsi que l'approche de classification et la méthode utilisée pour le travail présenté. En ce focalisant sur le tableau 3, nous constatons qu'un seul travail a été mené sur l'AS du DALG. Nous précisons cependant que le DALG est un dialecte peu étudié en général. Peu de travaux se sont focalisés sur ce dernier (Meftouh et al., 2015 ;Harrat et al., 2017 ;Guellil et al., 2017b ;Saâdane et Habash, 2015 ;Guellil et al., 2017a ;Harrat et al., 2016) Approches Analyse de sentiments de textes écrits en dialecte algérien Dans cette partie, nous définissons et implémentons une approche non supervisée, fondée sur un lexique de sentiments, pour déterminer la valence (positive, négative) et l'intensité (1,54, − 2, 87, etc.) d'un message donné écrit en DALG. Notre approche reçoit en entrée un message écrit en DALG (en caractères arabes) ainsi qu'un lexique de sentiments en DALG préalablement construit en traduisant un lexique de sentiments anglais existant. Elle retourne en sortie la valence du message ainsi que son intensité. Pour ce faire, notre approche est constituée de deux étapes principales 1) construction d'un lexique de sentiments en DALG, 2) calcul de la valence et de l'intensité d'un message (en appelant le lexique créée à l'étape 1). La figure 1 illustre l'architecture générale de notre approche. Étape 1 : construction d'un lexique de sentiments en dialecte algérien Cette étape reçoit en entrée un lexique de sentiments en anglais (nous choisissons l'anglais car c'est la langue qui a bénéficié de plus de travaux sur l'AS (Guellil et Traduction des mots du lexique anglais en dialecte algérien Pour faire cette traduction, nous faisons appel à l'API glosbe. Cette dernière prend en entrée un mot en anglais et retourne un ensemble de mots en DALG. La spécificité de cette API est que la traduction est faite par des utilisateurs ordinaires natifs du DALG. Nous traduisons chacun des mots de notre lexique de sentiments anglais en faisant appel à cette API. Nous affectons à tous les mots récoltés le même score que le mot en anglais. Prenons par exemple le mot anglais 'excellent' 9 voulant dire 'excellent' et ayant un score égal à + 5. Sa traduction en DALG donne les mots : Extraction des mots en dialecte algérien et calcul de leurs scores Après la réalisation de la première phase (section 4.1.1), nous nous sommes rendu compte qu'un mot en DALG est associé à plusieurs mots en anglais et peut donc avoir plusieurs scores. Prenons par exemple le mot mliyH 'bien'. Ce mot peut être associé aux mots anglais : excellent, best, generous, etc. Nous observons donc que les mots anglais auxquels est associé le mot mliyH peuvent avoir différents scores ('excellent' (+ 5), 'generous' (+ 2), etc). Nous extrayons donc, au sein de cette partie, tous les mots en DALG et sans répétition et calculons leurs scores. Pour le calcul du score, nous prenons la moyenne des scores de tous les mots anglais auxquels notre mot en DALG est associé. Nous obtenons ainsi notre lexique en DALG contenant par exemple le mot mliyH avec un score égal à 1,90, le mot krah avec un score égal à − 3, 16, etc. Étape 2 : calcul de la valence d'un message écrit en dialecte algérien Cette étape reçoit en entrée un message écrit en DALG et retourne en sortie sa valence et son intensité. Si nous prenons, par exemple, la phrase mliyHa AlqrrrrraAya # Âmiyra traduite en 'C'est bien d'étudier Amira'. Si nous nous appuyons sur le lexique construit à l'étape 1, cette phrase est reconnue positive avec une intensité globale égale à 1,69. Nous constatons cependant que pour arriver à un tel résultat, un ensemble de traitements doivent être appliqués à ce message dont 1) différents prétraitements allant de la suppression des lettres répétées à la recherche des émoticônes et expression fortes, 2) recherche des n-gramme du message présents dans le lexique, 3) extraction du lemme de chaque mot du message non identifié comme n-gramme, 4) traitement du passé, et enfin 5) analyse de la négation. − suppression des exagérations, par exemple AlqrrrrraAya est transformé en AlqraAya ; − suppression de certaines ponctuations telles que le # et espacements de certains points ('., !, ?') attachés au mots ; − remplacement des caractères arabes par leurs Unicodes pour traiter le phénomène relié à la présence de différentes lettres selon leurs emplacements (traités au sein de la section 2.1) ; − recherche d'émoticônes et d'expressions fortes tels , nmuwt ςlaý 'j'adore' ou encore yanςal Allah 'Que Dieu maudisse', etc. Le but étant d'attribuer directement au message la valence de l'émoticône ou de l'expression trouvée. Dans le cas où il y a plusieurs émoticônes ou expressions, nous ne prenons en considération que la première ; − prétraitements reliés à l'opposition exprimée en dialecte algérien à l'aide du mot baSaH 'mais', notre système ne prend en considération que la partie du message qui se trouve après . Nous procédons ainsi, car nous avons constaté que le mot annulait le sentiment de la partie qui le précède. Prenons l'exemple du message : Habiyt nuxruj nalςb nafraH baSaH raAniy mriyĎa traduit en 'je voulais sortir jouer être heureuse mais je suis malade'. Ce message à beau contenir plus de mots positifs que négatifs, il reste négatif parce que c'est la partie qui est après l'opposition qui détermine véritablement le sentiment. Prétraitement d'un message en dialecte algérien Recherche des expressions du message dans le lexique de sentiments Notre lexique de sentiments peut contenir des séquences d'un mot tel que 'bien', de deux mots telles que bSawt ςaAliy 'à haute voix', de trois mots tels que ςlaŷ maraA maraA 'des fois' ou encore de quatre mots tels que yastaςmal mara waHda bark 'il utilise une seule fois seulement'. Pour rechercher la présence d'une séquence de mots du message dans le lexique, nous formons d'abord l'ensemble de ces séquences, par exemple Âmiyra mliyHa AlqraAya , ce dernier contient trois mots individuels ( ), deux séquences de deux mots ( ) et une seule séquence de trois mots ( ). Une fois les séquences de mots du message construites, nous commençons leur recherche dans le lexique. Dans le cas de l'exemple présenté, un seul unigramme mliyHa a pu être trouvé dans le lexique. Un traitement de la négation est ensuite indispensable pour déterminer le score des séquences retrouvées (que nous présenterons dans la section 4.2.5). Extraction du lemme des mots en dialecte algérien Dans cette étape, nous extrayons de chaque mot (non reconnu par l'étape 4.2.2) son lemme (à l'aide du lexique de sentiments). Nous procédons comme suit : 1) nous recherchons tous les mots du lexique inclus dans notre mot, 2) nous sélectionnons les mots ayant le plus grand nombre Traitement du passé Certains verbes conjugués au passé ne peuvent pas être traités comme la plupart des mots cités dans la section 4.2.1. Nous ne pouvons donc pas directement extraire les affixes de ces verbes car nous devons d'abord faire des transformations sur leurs lemmes. Nous citons par exemple, les deux verbes nsaý 'oublier' et bkaý 'pleurer'. La conjugaison de ces deux verbes au passé est : nsiyt et bkiyt (pour la première personne du singulier). La lettre ý est donc supprimée afin de rajouter les suffixes nécessaires. Pour pouvoir former le lemme de ces verbes, il faut supprimer les suffixes et ajouter la lettre ý. Pour réaliser cette étape, nous procédons comme suit : 1) identification de la liste des suffixes passés (par exemple ,etc.) et suppression de ces derniers dans les mots analysés (à cette étape nsyt est transformé en ns, puisque le suffixe est enlevé), 2) ajout de la lettre à la fin des mots récoltés (à cette étape est transformé en nsý) et 3) recherche du lemme obtenu dans le lexique de sentiments, si le lemme obtenu est trouvé, le mot est validé et la négation est ensuite traitée (voir la section 4.2.5). Analyse de la négation L'analyse de la négation représente un défi de recherche important concernant l'AS et pas seulement pour l'arabe, mais pour toutes les langues. Néanmoins ce défi est accentué dans le cas de l'arabe et ses dialectes où la négation s'agglutine le plus souvent au mot, au même titre que les différents pronoms. Pour plus de détails sur la négation, nous vous invitons à vous référer à la section 2.2.2.2. Les utilisateurs peuvent faire appel à la négation de différentes manières, par exemple le mot mAnHabkumš 'je ne vous aime pas' peut s'écrire mA nHabkumš, mAnHabkum š ou encore mA nHabkum š. Nous constatons que la négation peut être agglutinée aux termes comme elle peut être séparée de ces derniers. Nous traitons dans ce travail deux sortes de négations : 1) la négation agglutinée au mot, et 2) la négation séparée du mot. Pour les deux cas, nous définissons une liste de préfixes et de suffixes reliés à la négation. Nous avons cependant constaté que, dans la plupart des cas, la négation n'influe pas seulement sur le mot qu'elle précède, mais sur le reste de la phrase également. Une fois qu'un préfixe ou un suffixe de négation est détecté, nous inversons le score des mots succédant à cette négation (multipliant le score par (− 1)). Étude expérimentale Pour développer notre solution nous nous sommes inspirés du programme élaboré par Taboada et al. (2011). Le programme et les différents lexiques de ses auteurs sont librement téléchargeables 10 . La différence entre cette solution et la nôtre réside au niveau de l'identification des parties du discours faite par ces auteurs et non réalisée par notre système. Ces auteurs utilisent un outil très répandu pour l'identification des parties du discours 11 , qui ne peut pas être utilisé pour le DALG. Nous définissons donc un seul lexique regroupant les quatre parties grammaticales : adjectifs, verbes, noms et adverbes. Pour illustrer les résultats de l'AS du DALG, nous présentons les quatre parties : 1) l'environnement expérimental, 2) les résultats expérimentaux et leurs analyse, 3) l'analyse des cas d'erreurs, et 4) perspective d'extension de notre approche à l'ASM. Environnement expérimental Nous présentons dans cette section l'ensemble des données et des paramètres utilisés dans nos expérimentations : 1) les lexiques, 2) les corpus de test, et 3) les différents types d'expérimentations effectuées. Lexiques utilisés Pour la construction des lexiques, nous avons fait appel à deux lexiques anglais. D'une part, le SOCAL, qui est utilisé dans (Taboada et al., 2011), et d'autre part, le SentiWordNet qui est utilisé dans (Baccianella et al., 2010). Concernant SOCAL, nous Corpus de test utilisés Nous avons utilisé dans ce travail deux corpus de test : 1) le premier corpus contient 323 messages (phrases) pris du corpus PADIC, dont 157 sont positifs et 166 négatifs, PADIC étant le seul corpus parallèle multidialectal, contenant également le DALG (Meftouh et al., 2015), 2) le deuxième corpus contient 426 messages extraits du média social Facebook 13 , dont 220 sont positifs et 206 sont négatifs. Les statistiques relatives à ces corpus sont présentées dans le tableau 5.1.2. Ces corpus ont été annotés par deux annotateurs natifs du dialecte algérien (un des auteurs de ce travail étant l'un des annotateurs. L'accord entre annotateurs (Kappa) est de 0,954 (0,956 pour le corpus PADIC et 0,952 pour le corpus Facebook). Les annotateurs ont reçu les instructions suivantes : − les messages doivent être annotés en deux classes (positive ou négative). Les messages objectives ou neutres ne doivent pas être pris en considération ; − les annotateurs ne doivent en aucun cas se référer à leur opinion personnelle mais plutôt au sentiment général de la phrase ; − prendre en considération les exagérations et les émoticônes qui pourraient accentuer le sentiment ; − dans le cas où un message contiendrait du texte et des émoticônes de valences différentes, il faudrait privilégier le sentiment porté par le texte pour l'annotation. Tableau 5. Résultats expérimentaux avec les deux lexiques utilisés pour chaque corpus avec chaque lexique cinq expérimentations : 1) n-gramme, 2) n-gramme + prétraitement, 3) n-gramme + prétraitement + lemme, 4) n-gramme + prétraitement + lemme + passé, et 5) n-gramme + prétraitement + lemme + passé + négation. Nous souhaitons montrer à travers ces expérimentations l'impact de chaque étape de notre approche sur les résultats obtenus. Résultats expérimentaux Nous illustrons nos résultats à l'aide de trois métriques : la précision (P), le rappel (R) et la F-mesure (F1). Nous présentons dans le tableau 5, les résultats obtenus (P, R et F1) sur les deux corpus de test utilisés (PADIC et Facebook) avec les deux lexiques SOCALALG et SentiALG. D'après le tableau 5, nous constatons que les résultats évoluent positivement après l'exécution de chaque étape de notre approche et ce pour nos deux lexiques (sauf pour le traitement de la négation où il y a une légère régression des résultats). L'évolution la plus importante en termes de F1 a été observée au sein du corpus PADIC où les résultats initiaux étaient de 0,56 pour finir avec un score de 0,78. Analyse des cas d'erreurs Après une analyse approfondie des messages qui sont mal classés par notre système, nous présentons dans le tableau 6 les principales erreurs de classification du sentiment du DALG. D'après le tableau 6, nous constatons qu'il existe sept principales erreurs 1) les pluriels irréguliers, 2) les mots non existants dans nos lexiques, 3) les mots ayant une valence différente dans nos lexiques, 4) les mots en ASM, non utilisés en DALG, 5) les mots ayant une intensité trop élevée, 6) le non-traitement des intensificateurs, 7) les lemmes retrouvés dans le lexique par erreur. Les pluriels irréguliers signifient que la formation du pluriel de certains mots ne suit pas les règles que nous avons présentées dans la section 2. Leurs traitements amélioraient le score final. Enfin, les mots comme bkiynaA 'Nous avons pleuré' dont un lemme a été reconnu par erreur, avant même de faire appel au traitement du passé. Afin de résoudre ces problématiques, nous proposons les améliorations suivantes de notre système : − proposition d'un lemmatiseur propre au dialecte algérien dédié à la segmentation et à l'analyse des pluriels irréguliers ; − extension de notre lexique pour qu'il ait un champ plus étendu. Nous prévoyons de faire cet extension à l'aide des techniques du « word Embedding » ; − intégration du contexte dans l'annotation du lexique ; − enrichissement de notre lexique en DALG avec un autre lexique en ASM. Tableau 6. Les principales erreurs de classification de notre système 5.4. Perspective d'extension de notre approche à l'ASM Afin de comparer notre approche avec les travaux existants, nous proposons l'extension de cette dernière à l'ASM. Pour ce faire, nous construisons en premier lieu nos lexiques : SOCAL_ASM et Senti_ASM en suivant la même méthode que celle présentée dans la section 4.1. Nous obtenons ainsi 5 190 termes pour SOCAL_ASM et 15 838 pour Senti_ASM. Afin de pouvoir appliquer notre approche sur les deux lexiques obtenus, nous utilisons le corpus ASM utilisé dans (Altowayan et Tao, 2016). Ce corpus contient 4 294 messages, dont 2 147 positifs et 2 147 négatifs. Il a été construit en combinant plusieurs autres corpus présentés au sein de la littérature. En ajoutant certains affixes à notre approche, dédiés à l'ASM, nous obtenons un F1-score égal à 0,58 pour Senti_ASM et 0,62 pour SOCAL_ASM. Nous constatons que la mise à l'échelle de notre approche sur l'ASM donne des résultats compétitifs dans le cadre d'une approche fondée sur les lexiques. Les résultats fournis par SOCAL_ASM (F1 égale à 0,62) ne s'éloignent pas trop des résultats que nous avons présentés dans le tableau 5 pour le corpus Facebook. Ceci est tout à fait justifié car le corpus ASM que nous avons utilisé est essentiellement alimenté avec des données provenant des médias sociaux. Conclusion et perspectives Dans cet article, nous avons proposé et implémenté une approche d'AS de messages écrits en DALG. Cette approche s'appuie sur la construction et l'utilisation de lexiques de sentiments en DALG. Elle est fondée également sur l'agglutination qui est une problématique très importante dans le traitement de l'ASM et de ses dialectes. Nous avons évalué cette approche à l'aide des deux lexiques de sentiments construits, SOCALALG et SentiALG, ainsi qu'un corpus de test annoté manuellement contenant 747 messages. Les résultats expérimentaux indiquent une amélioration continue après l'exécution de chaque étape de notre approche atteignant une précision de 0,78, un rappel de 0,78 et une F-mesure de 0,78. Ces résultats pourraient cependant être améliorés en prenant en considération plusieurs facteurs étudiés dans la section 5.3. Nos travaux futurs s'orientent vers une proposition d'intégrer l'ensemble des critères suivants : − l'analyse des pluriels irréguliers et proposition d'une liste de changements et d'affixes qui pourraient traiter ces pluriels ; − la fusion des deux lexiques SOCAL_ALG et Senti_ALG ainsi que la fusion (ASM et DALG), car plusieurs utilisateurs utilisent l'alternance codique entre l'ASM et le DALG. Il faudrait également procéder à l'intégration d'autres lexiques de sentiments anglais tels que MPQA ou SentiStrenght ; − la définition d'une méthode combinant le traitement des lemmes et du passé en même temps ; − le traitement de l'intensification ; − la revue manuelle des lexiques utilisés pour pouvoir y intégrer la notion de contexte ainsi que l'enrichissement du lexique obtenu à l'aide des techniques de l'apprentissage profond (deep learning). Enfin, nous comptons également étendre cette approche aux corpus annotés pour pouvoir exploiter les techniques de classification usuelles. Néanmoins ces approches requièrent des corpus annotés. La construction de ces corpus est très coûteuse en termes de temps et d'efforts. Nous prévoyons donc de proposer une approche de construction automatique ou semi-supervisée de ces corpus. Remerciements Les premiers auteurs sont soutenus par l'École nationale supérieure d'informatique (ESI) à Alger ainsi que l'École supérieure des sciences appliquées d'Alger (ESSAA). Le troisième auteur est soutenu par la DGE (ministère de l'Industrie de France) et par la DGE (ministère de l'Économie de France) : projet « DRIRS », référencé par le numéro 172906108. Nous tenons à remercier Billel Gueni pour sa collaboration et ses précieux retours. Tableau 1 . 13) les noms et adjectifs, et 4) les compléments d'objet direct (COD) et les compléments d'objet indirect (COI). 2.2.2.1. Conjugaison en dialecte algérien Comme au sein de n'importe quel langage, la conjugaison inclut l'ajout d'un ensemble de préfixes et de suffixes à un lemme donné. Ces affixes varient selon le Conjugaison du verbe 'aimer' au présent, passé et impératif pronom utilisé. Ils sont généralement les mêmes pour tous les verbes. Nous faisons figurer dans le tableau 1 la conjugaison du verbe 'aimer' en DALG aux temps les plus utilisés, c'est-à-dire le présent et le passé composé de l'indicatif ainsi qu'à l'impératif. et adjectifs dans le dialecte algérien Comme pour toutes les langues, les noms et les adjectifs ont un genre et un nombre. Plusieurs auteurs (Harrat et al., 2016 ; Harrat et al., 2017 ; Guellil et Azouaou, 2016) attestent que pour former le féminin des noms et adjectifs en DALG la lettre doit être ajoutée comme suffixe. Par exemple le féminin de l'adjectif mliyH 'bien' est mliyHa . Concernant le pluriel, tous les travaux étudiés s'accordent sur le fait que le masculin pluriel et le féminin pluriel sont formés à partir du nom ou de l'adjectif auxquels sont respectivement ajoutés les suffixes yn et At. Par exemple, le pluriel de l'adjectif fanyaAn 'fainéant' est fanyaAniyn et le pluriel du nom šiyxa 'enseignante' est šiyxaAt. ( Al-Ayyoub et al., 2015) (Abdulla et al., 2014b) (Abdulla et al., 2014a) (Badaro et al., 2014) (Abdulla et al., 2014a) Jordanien (Mohammad et al., 2013) (AL-Khawaldeh, 2015) (Mataoui et al., 2016)(Mataoui et al., et al., 2013) L'argot égyptien NB + lexique(Khalifa et al., 2014) Tableau 3. Classification et synthèse des travaux étudiés Figure 1 . 1Architecture générale de notre approche Boukhalfa, 2015)). Chaque mot de ce lexique est traduit en appelant une API de traduction. Après la phase de traduction, un lexique de sentiments est construit en extrayant chaque terme en DALG et calculant son score. Cette étape contient donc deux sous-étapes : 1) traduction des mots du lexique anglais en DALG et 2) extraction des mots en DALG et calcul de leurs scores. 9. https ://glosbe.com/en/arq/excellent baAhiy, lTiyf, mliyH, etc. Tous ces mots ont donc un score égal à + 5, de même que le mot 'excellent'. Nous nous inspirons dans cette partie de plusieurs travaux : 1) le travail de Elarnaoty et al. (2012) qui fait la traduction de la version anglaise du lexique MPQA (Multi-Perspective Question Answering) en arabe, 2) le travail de El-Halees et al. (2011) qui fait la traduction de Sentistrength en arabe également, 3) le travail de Al-Ayyoub et al. (2015) qui traduit des lemmes arabes en anglais. Nous nous inspirons des différents travaux de Cherif et al. (2015a) pour proposer plusieurs prétraitements nécessaires au traitement de l'arabe et de ses dialectes. Nous nous inspirons également des différents travaux de Guellil et Azouaou (2017), Harrat et al. (2016) et Saâdane et Habash (2015) se focalisant sur les caractéristiques propres au dialecte algérien. Nous proposons donc l'ensemble des prétraitements suivants : − suppression des blancs, des lettres longues (reconnus par tatweel), par exemple devient mliyHa ; de lettres et 3) nous vérifions que le mot sélectionné peut être découpé en préfixe + lemme + suffixe et nous enlevons ainsi les préfixes et suffixes pour ne garder que le lemme et ce, en se fondant sur le travail de Cherif et al. (2015a). Pour cela nous définissons une liste globale des préfixes et suffixes du DALG. Nous illustrons cette étape en réutilisant l'exemple . Rappelons juste que l'unigramme mliyHa a été identifié au sein de l'étape 4.2.2. Nous aurons donc à traiter les deux mots restants : AlqraAya et Âmiyra . Aucune partie du mot n'a été retrouvée dans notre lexique de sentiments. Néanmoins le mot qraAy a été détecté comme faisant partie du mot . Ce mot se présente donc sous cette forme : Al+qraAy+ . Comme le mot (déterminant) est un préfixe reconnu du DALG et la lettre est un suffixe reconnu également, le lemme qraAy est donc validé. La négation doit ensuite être traitée (se référer à la section 4.2.5). 10. https ://github.com/sfu-discourse-lab/SO-CAL 11. Stanford CoreNLP : https ://stanfordnlp.github.io/CoreNLP/ avons d'abord fusionné les lexiques d'adjectifs, de verbes, de noms et d'adverbes. Nous avons ainsi obtenu 6 769 termes dont le sentiment est étiqueté entre − 1 et − 5 pour les termes négatifs et entre + 1 et + 5 pour les termes positifs. Après que l'intégralité des termes a été envoyée à l'API de traduction, 3 952 termes en anglais ont été reconnus et traduits. Notre lexique final, que nous nommons SOCALALG, contient 2 375 termes en DALG dont 1 363 termes négatifs, 948 termes positifs et 64 termes neutres (avec un sentiment égal à 0). Pour SentiWordNet, nous avons commencé par construire un lexique contenant l'ensemble des termes de SentiWordNet avec la moyenne du sentiment de chaque terme. Pour ce faire, nous avons fait appel à l'API JAVA de Petter Tönberg fourni dans le site officiel de SentiWordNet 12 . Comme les termes de SentiWordNet ont un sentiment étiqueté entre − 1 et + 1, nous avons multiplié tous les sentiments par 5 (pour l'aligner au lexique SOCAL). Le lexique obtenu contient 39 885 termes étiquetés entre + 0, 05 et + 5 pour les termes positifs et entre − 0, 05 et − 5 pour les termes négatifs. Une fois envoyés à l'API de traduction, 12 780 termes en anglais ont été reconnus et traduits. Notre lexique final que nous nommons SentiALG, contient 3 408 termes en DALG dont 1 856 négatifs, 1 539 positifs et 13 neutres. préfixes et suffixes) et de clitiques entre eux.Par exemple la forme agglutinée sayaktubuwnahaA 'ils l'écriront' peut être séparée de la sorte : . Le lemme étant (ktb). La lettre étant la marque du futur. Les deux lettres séparées par le lemme représentent le pronom personnel : « Ils ». Le pronom représente le complément d'objet direct (COD). Il est à signaler que les spécificités que nous venons de décrire pour l'ASM sont aussi présentes dans ses dialectes. Les différences entre l'ASM et ses dialectes résident principalement dans 1) la richesse du vocabulaire des dialectes par rapport à l'ASM et 2) le changement des affixes (préfixes et suffixes) utilisés dans les dialectes. Pour illustrer ces différences, nous nous penchons sur le DALG. Ce dialecte souffre d'un manque considérable de ressources, d'outils et de travaux de recherche le traitant en le comparant aux autres dialectes arabes. pas'. Prenons un autre exemple avec la phrase : « Cette fille est bien » qui). 2.2.2.2. Négation en dialecte algérien La négation du DALG peut se faire de deux manières principales, soit 1) à l'aide des lettres maA. . .š ou encore des lettres maA. . .š, 2) à l'aide du mot maAšiy. Prenons l'exemple de la phrase « Je l'aime » qui devient en DALG nHabuw pour le masculin et nHabhaA pour le féminin. La négation de cette phrase étant « Je ne l'aime pas » qui devient mAnHabuwš. Donc pour faire une analogie avec le français, le maA joue le rôle du 'ne' et le š joue le rôle de 'COD Exemple Traduction COI Exemple Traduction ‫ني‬ ‫تحبيني‬ Tu m'aimes ‫لي‬ ‫ت‬ ‫قولي‬ Tu me le dis ‫ك‬ ‫كرهتك‬ Je t'ai détesté ‫لك‬ ‫قولتلك‬ Je te l'ai dit ‫و‬ ‫هو‬ ‫ه‬ ‫كرهتو‬ ‫حبيته‬ Je l'ai détesté Je l'ai aimé ‫لو‬ ‫نقولو‬ Je lui dis ‫ها‬ ‫كرهتها‬ Je l'ai détesté ‫لها‬ ‫قولتولها‬ Je le lui ai dit ‫نا‬ ‫كرهتونا‬ Vous nous avez détestés ‫نا‬ ‫لنا‬ ‫قولتونا‬ Tu nous l'as dit ‫كم‬ ‫حبيناكم‬ Nous vous avons aimés ‫لكم‬ ‫قولتلكم‬ Je vous l'ai dit ‫هم‬ ‫كرهتوهم‬ Vous les avez détestés ‫لهم‬ ‫قوللهم‬ Dis-leur Tableau 2. Les pronoms COD et COI du dialecte algérien devient en DALG haAd AlTufla mliyHa . Sa négation étant « Cette fille n'est pas bien » qui devient en DALG haAd AlTufla maAšiy mliyHa . Nous constatons maintenant que pour exprimer la négation de mliyHa 'bien', nous employons le terme maAšiy. Donc, pour résumer, nous observons que la séquence est utilisée avec les verbes et le terme est utilisé avec les noms et les adjectifs. 2.2.2.3. COD et COI du dialecte algérien (clitiques pronominaux) ‫%ب,…‬ ‫ح‬ ‫ش%%%هى,‬ ‫شت%%%%%%ى,‬ Hate: ‫%%%%د,…‬ ‫كره,حس%‬ ‫زعاف,‬Traduction des mots ANG en DAALG Extraction des mots en DAALG et calcul de leurs scores Lexique ANG Excellent: 5 Like: 1 Hate: -4 API de traduction Excellent: …, ‫%%%%ح‬ ‫ملي%‬ ‫%%%اهي,‬ ‫ب%‬ ‫%%%%ف‬ ‫,لطي%‬ Like: 1,86 : ‫%%%اهي‬ ‫ب%‬ ‫%%%%ح:09,1‬ ‫ملي%‬ 1,94: ‫%%%%ف‬ ‫لطي%‬ 0,57: ‫شت%%%%%%ى‬ 0,30: ‫ش%%%هى‬ 1,56: ‫%ب‬ ‫ح‬ -2,33: ‫زعاف‬ -3,16: ‫%ره‬ ‫ك%‬ -2,44: ‫%%د‬ ‫حس%‬ Prétraitements généraux Recherche d'émoticônes Recherche d'expressions fortes Recherche d'expressions Extraction des lemmes Traitement passé Analyse de la négation Message en DAALG ‫أميرة‬ ‫مليحة‬ ‫القراية‬ ‫الفرحة‬ ‫هاذ‬ ‫نعيش‬ ‫,نحب‬ ‫الجزائر‬ ‫نحب‬ ‫يتربا.مشاهير‬ ‫محبش‬ ‫.ه‬ Liste d'émoticônes Liste d'expressions Valence + score Valence + score Valence + score Découpage en mots Émoticônes trouvés Émoticônes non trouvés Expressions trouvés Expressions non trouvés Message restant N-grammes trouvés Pour chaque mot du message Lemme Lemme non trouvé Passé trouvé Appel du mot suivant Passé non trouvé 5.1.3. Types d'expérimentations effectuéesNos expérimentations ont été effectuées sur 749 messages répartis en deux corpus et sur les deux lexiques SOCALALG et SentiALG. En plus de cela, nous avons menéCorpus PADIC Facebook Pos. Nég. Tout Pos. Nég. Tout Nbre messages 157 166 323 220 206 426 Nbre mots 849 952 1 802 1 711 1 735 3 446 Nbre mots/message 5,41 5,73 5,57 7,78 8,42 8,1 Nbre caractères/message 21,9 24,0 23,0 33,3 35,9 34,6 Nbre messages avec émoticônse 0 0 0 38 19 57 Tableau 4. Statistiques relatives aux corpus de test Lexique utilisé PADIC Facebook P R F1 P R F1 n-gramme (1) SOCALALG 0,71 0,45 0,55 0,68 0,36 0,47 SentiALG 0,73 0,45 0,56 0,67 0,38 0,48 n-gramme + prétraitement (2) SOCALALG 0,72 0,46 0,56 0,74 0 ,42 0,53 SentiALG 0,74 0,47 0,57 0,71 0,42 0,53 N-gramme + prétraitement + lemme (3) SOCALALG 0,70 0,69 0,70 0,70 0,63 0,67 SentiALG 0,75 0,74 0,74 0,69 0,63 0,66 n-gramme + prétraitement + lemme + passé (4) SOCALALG 0,70 0,70 0,70 0,72 0,64 0,67 SentiALG 0,75 0,74 0,74 0,69 0,64 0,66 n-gramme + prétraitement + lemme + passé + négation (5) SOCALALG 0,75 0,74 0,75 0,68 0,61 0,64 SentiALG 0,78 0,78 0,78 0,67 0,61 0,64 2.2.4. Prenons par exemple le mot mliyH signifiant 'bien', son pluriel n'est pas mliyHiyn selon la règle, mais plutôt le mot mlaAH. Nous avons également constaté que plusieurs mots importants ne sont pas présents dans l'un de nos lexiques ou parfois même dans les deux. Parmi ces derniers, nous citons : kaAduw 'un cadeau', zςaf 'Il est en colère', etc. D'autres mots tels que ςjiyb 'bizarre' ont une valence différente d'un lexique à un autre. Certains mots tels que 'imprimé' maTbuwςa ou encore AHsan 'le meilleur' sont des mots en ASM et donc non reconnus en DALG. Certains mots tels que waAš 'Comment ?' ont une intensité trop élevée dans les lexiques, ce qui peut fausser le calcul du score final. D'autres mots comme bzaAf 'très', représentant des intensificateurs, ne sont pas reconnus comme tels. ‫غي‬ ‫نعرف‬ ‫انا‬ ‫هلل‬ ‫الحمد‬AlHmd llh AnA nςrf γyr AlmlAH Dieu soit loué je ne connais que des gens bien Le mot ‫'المالح'‬ qui est le pluriel de ‫'مليح'‬ n'a pas été reconnu Celle qui m'a ramené un cadeau en fin d'année Le mot ‫'كادو'‬ n'existe pas dans nos lexiques Positif Négatif ‫ﻳواش‬ ‫تلق‬ ‫ا‬ ‫تقر‬ ‫قولك‬ Les études en notre temps sont devenues très difficiles Le non-traitement des intensificateurs tels que ‫اف'‬ ‫'بز‬ Le mot ' ‫ي‬ ‫'ك‬ est retrouvé ne donnant pas la chance au mot ‫'بىك'‬ d'être recherché Négatif Positif ‫احسن‬ ‫المطبوعة‬ ‫الف‬ ‫مرة‬ AlmTbwςħ AHsn Alf mrħ L'imprimerie est mieux mille fois. Contient des mots en ASM qui n'ont pas été reconnus Positif Négatif ‫!عجيب‬ Merveilleux Le mot ‫'عجيب'‬ ayant une valence positive dans un lexique et négative dans un autreMessages mal classés Traduction Cause principale de l'erreur Annotation _cible Annotation _système ‫المالح‬ Positif Négatif ‫ي‬ ‫جابتل‬ ‫ي‬ ‫ل‬ ‫هاديك‬ ‫كادو‬ ‫ي‬ ‫ف‬ ‫اللخر‬ ‫تاع‬ ‫العام‬ HAdyk ly jAbtly kAdw fy Allxr tAς AlςAm Qu'est-ce qu'on dit tu étudies tu trouveras Le mot ‫'واش'‬ ayant une intensité négative élevée Positif Négatif ‫عادت‬ ‫وقتنا‬ ‫ي‬ ‫ف‬ ‫اية‬ ‫القر‬ ‫اف‬ ‫بز‬ ‫صعيبة‬ AlqrAyħ fy wqtnA ςAdt bzAf Sςybħ Négatif Positif ‫!.بكينا‬ bkiynaA. ! On a pleuré ! ςjiyb Positif Négatif . Translittération arabe présentée dans schème Habash-Soudi-Buckwalter (HSB)(Habash et al., 2007). TAL. Volume 58 -n o 3/2017 . http ://sentiwordnet.isti.cnr.it/ 13. https ://fr-fr.facebook.com/policy.php Analyse de sentiments du dialecte algérien 59 TAL. 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On the Concept of Resource-Efficiency in NLP

Resource-efficiency is a growing concern in the NLP community. But what are the resources we care about and why? How do we measure efficiency in a way that is reliable and relevant? And how do we balance efficiency and other important concerns? Based on a review of the emerging literature on the subject, we discuss different ways of conceptualizing efficiency in terms of product and cost, using a simple case study on fine-tuning and knowledge distillation for illustration. We propose a novel metric of amortized efficiency that is better suited for life-cycle analysis than existing metrics.143

On the Concept of Resource-Efficiency in NLP Association for Computational LinguisticsCopyright Association for Computational LinguisticsMay 22-24, 2023 c 2023 Luise Dürlich luise.durlich@ri.se Department of Computer Science RISE Research Institutes of Sweden Department of Linguistics and Philology Uppsala University Evangelia Gogoulou evangelia.gogoulou@ri.se Department of Computer Science RISE Research Institutes of Sweden Division of Software and Computer Systems KTH Royal Institute of Technology Joakim Nivre joakim.nivre@ri.se Department of Computer Science RISE Research Institutes of Sweden Department of Linguistics and Philology Uppsala University On the Concept of Resource-Efficiency in NLP Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa) the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)Association for Computational LinguisticsMay 22-24, 2023 c 2023 Resource-efficiency is a growing concern in the NLP community. But what are the resources we care about and why? How do we measure efficiency in a way that is reliable and relevant? And how do we balance efficiency and other important concerns? Based on a review of the emerging literature on the subject, we discuss different ways of conceptualizing efficiency in terms of product and cost, using a simple case study on fine-tuning and knowledge distillation for illustration. We propose a novel metric of amortized efficiency that is better suited for life-cycle analysis than existing metrics.143 Introduction Resource-efficiency has recently become a more prominent concern in the NLP community. The Association for Computational Linguistics (ACL) has issued an Efficient NLP Policy Document 1 and most conferences now have a special track devoted to efficient methods in NLP. The major reason for this increased attention to efficiency can be found in the perceived negative effects of scaling NLP models (and AI models more generally) to unprecedented sizes, which increases energy consumption and carbon footprint as well as raises barriers to participation in NLP research for economic reasons (Strubell et al., 2019;Schwartz et al., 2020). These considerations are important and deserve serious attention, but they are not the only reasons to care about resource-efficiency. Traditional concerns like guaranteeing that models can be executed with sufficient speed to enable real-time processing, or with sufficiently low memory footprint to fit on small devices, will continue to be important as well. * Equal contribution to this work. 1 https://www.aclweb.org/portal/content/efficient-nlppolicy-document Resource-efficiency is however a complex and multifaceted problem. First, there are many relevant types of resources, which interact in complex (and sometimes antagonistic) ways. For example, adding more computational resources may improve time efficiency but increase energy consumption. For some of these resources, obtaining relevant and reliable measurements can also be a challenge, especially if the consumption depends on both software and hardware properties. Furthermore, the life-cycle of a typical NLP model can be divided into different phases, like pre-training, fine-tuning and (long-term) inference, which often have very different resource requirements but nevertheless need to be related to each other in order to obtain a holistic view of total resource consumption. Since one and the same (pre-trained) model can be finetuned and deployed in multiple instances, it may also be necessary to amortize the training cost in order to arrive at a fair overall assessment. To do justice to this complexity, we must resist the temptation to reduce the notion of resourceefficiency to a single metric or equation. Instead, we need to develop a conceptual framework that supports reasoning about the interaction of different resources while taking the different phases of the life-cycle into account. The emerging literature on the subject shows a growing awareness of this need, and there are a number of promising proposals that address parts of the problem. In this paper, we review some of these proposals and discuss issues that arise when trying to define and measure efficiency in relation to NLP models. 2 We specifically address the need for a holistic assessment of efficiency over the entire life-cycle of a model and propose a novel notion of amortized efficiency. All notions and metrics are illustrated in a small case study on fine-tuning and knowledge distillation. Strubell et al. (2019) were among the first to discuss the increasing resource requirements in NLP. They provide estimates of the energy needed to train a number of popular NLP models, including T2T (Vaswani et al., 2017), ELMo (Peters et al., 2018), BERT (Devlin et al., 2019) and GPT2 (Radford et al., 2019). Based on those estimates, they also estimate the cost in dollars and the CO 2 emission associated with model training. In addition to the cost of training a single model, they provide a case study of the additional (much larger) costs involved in hyperparameter tuning and model finetuning. Their final recommendations include: (a) Authors should report training time and sensitivity to hyperparameters. (b) Academic researchers need equitable access to computation resources. (c) Researchers should prioritize computationally efficient hardware and algorithms. Schwartz et al. (2020) note that training costs in AI increased 300,000 times from 2012 to 2017, with costs doubling every few months, and argue that focusing only on the attainment of state-of-theart accuracy ignores the economic, environmental, or social cost of reaching the reported accuracy. They advocate research on Green AI -AI research that is more environmentally friendly and inclusive than traditional research, which they call Red AI. Specifically, they propose making efficiency a more common evaluation criterion for AI papers alongside accuracy and related measures. Hershcovich et al. (2022) focus specifically on environmental impact and propose a climate performance model card that can be used with only limited information about experiments and underlying computer hardware. At a minimum authors are asked to report (a) whether the model is publicly available, (b) how much time it takes to train the final model, (c) how much time was spent on all experiments (including hyperparameter search), (d) what the total energy consumption was, and (e) at which location the computations were performed. In addition, authors are encouraged to report on the energy mix at the location and the CO 2 emission associated with different phases of model development and use. Liu et al. (2022) propose a new benchmark for efficient NLP models called ELUE (Efficient Language Understanding Evaluation) based on the concept of Pareto state of the art, which a model is said to achieve if it achieves the best performance at a given cost level. The cost measures used in ELUE are number of model parameters and number of floating point operations (FLOPs), while performance measures vary depending on the task (sentiment analysis, natural language inference, paraphrase and textual similarity). Treviso et al. (2022) provide a survey of current research on efficient methods for NLP, using a taxonomy based on different aspects or phases of the model life-cycle: data collection and preprocessing, model design, training (including pre-training and fine-tuning), inference, and model selection. Following Schwartz et al. (2020), they define efficiency as the cost of a model in relation to the results it produces. They observe that cost can be measured along multiple dimensions, such as computational, time-wise or environmental cost, and that using a single cost indicator can be misleading. They also emphasize the importance of separately characterizing different stages of the model lifecycle and acknowledge that properly measuring efficiency remains a challenge. Related Work Dehghani et al. (2022) elaborate on the theme of potentially misleading efficiency characterizations by showing that some of the most commonly used cost indicators -number of model parameters, FLOPs, and throughput (msec/example) -can easily contradict each other when used to compare models and are therefore insufficient as standalone metrics. They again stress the importance of distinguishing training cost from inference cost, and point out that their relative importance may vary depending on context and use case. For example, training efficiency is crucial if a model needs to be retrained often, while inference efficiency may be critical in embedded applications. The Concept of Efficiency in NLP Efficiency is commonly defined as the ratio of useful output to total input: 3 r = P C (1) where P is the amount of useful output or results, the product, and C is the total cost of producing the results, often defined as the amount of resources consumed. A process or system can then be said to reach maximum efficiency if a specific desired result is obtained with the minimal possible amount of resources, or if the maximum amount of results is obtained from a given resource. More generally, maximum efficiency holds when it is not possible to increase the product without increasing the cost, nor reduce the cost without reducing the product. In order to apply this concept of efficiency to NLP, we first have to decide what counts as useful output or results -the product P in Equation 1. We then need to figure out how to measure the cost C in terms of resources consumed. Finally, we need to come up with relevant ways of relating P to C in different contexts of research, development and deployment, as well as aggregating the results into a life-cycle analysis. We will begin by discussing the last question, because it has a bearing on how we approach the other two. The Life-Cycle of an NLP Model It is natural to divide the life-span of an NLP model into two phases: development and deployment. In the development phase, the model is created, optimized and validated for use. In the deployment phase, it is being used to process new text data in one or more applications. The development phase of an NLP model today typically includes several stages of training, some or all of which may be repeated multiple times in order to optimize various hyperparameters, as well as validation on held-out data to estimate model performance. The deployment phase is more homogeneous in that it mainly consists in using the model for inference on new data, although this may be interrupted by brief development phases to keep the model up to date. As researchers, we naturally tend to focus on the development of new models and many models developed in a research context may never enter the deployment phase at all. Since the development phase is typically also more computationally intensive than the deployment phase, it is therefore not surprising that early papers concerned with the increasing energy consumption of NLP research, such as Strubell et al. (2019) and Schwartz et al. (2020), mainly focused on the development phase. Nevertheless, for models that are actually put to use in large-scale applications, resources consumed during the deployment phase may in the long run be much more important, and efficiency in the deployment phase is therefore an equally valid concern. This is also the focus of the recently proposed eval-uation framework ELUE (Liu et al., 2022). As will be discussed in the following sections, some proposed efficiency metrics are better suited for one of the two phases, although they can often be adapted to the other phase as well. However, the question is whether there is also a need for metrics that capture the combined resource usage at development and deployment, and how such metrics can be constructed. One reason for being interested in combined metrics is that there may be trade-offs between resources spent during development and deployment, respectively, so that spending more resources in development may lead to more efficient deployment (or vice versa). To arrive at a more holistic assessment of efficiency, we need to define efficiency metrics for deployment that also incorporate development costs. Before we propose such a metric, we need to discuss how to conceptualize products and costs of NLP models. The Products of an NLP Model What is the output that we want to produce at the lowest possible cost in NLP? Is it simply a model capable of processing natural language (as input or output or both)? Is it the performance of such a model on one or more NLP tasks? Or is it the actual output of such a model when processing natural language at a certain performance level? All of these answers are potentially relevant, and have been considered in the literature, but they give rise to different notions of efficiency and require different metrics and measurement procedures. Regarding the model itself as the product is of limited interest in most circumstances, since it does not take performance into account and only makes sense for the development phase. It is therefore more common to take model performance, as measured on some standard benchmark, as a relevant product quantity, which can be plotted as a function of some relevant cost to obtain a so-called Pareto front (with corresponding concepts of Pareto improvement and Pareto state of the art), as illustrated in Figure 1, reproduced from Liu et al. (2022). One advantage of the product-as-performance model is that it can be applied to the deployment phase as well as the development phase, although the cost measurements are different in the two cases. For the development phase, we want to measure the total cost incurred to produce a model with a given performance, which depends on a multitude of factors, such as the size of the model, the num- Figure 1: Pareto front with model performance as the product and cost measured in FLOPs (Liu et al., 2022). ber of hyperparameters that need to be tuned, and the data efficiency of the learning algorithm. For the deployment phase, we instead focus on the average cost of processing a typical input instance, such as a natural language sentence or a text document, independently of the development cost of the model. Separating the two phases in this way is perfectly adequate in many circumstances, but the fact that we measure total cost in one case and average cost in the other makes it impossible to combine the measurements into a global life-cycle analysis. To overcome this limitation, we need a notion of product that is not defined (only) in terms of model performance but also considers the actual output produced by a model. If we take the product to be the amount of data processed by a model in the deployment phase, then we can integrate the development cost in the efficiency metric as a debt that is amortized during deployment. Under this model, the average cost of processing an input instance is not constant but decreases over the life-time of a model, which allows us to capture possible trade-offs between development and deployment costs. For example, it may sometimes be worth investing more resources into the development phase if this leads to a lower development cost in the long run. Moreover, this model allows us to reason about how long a model needs to be in use to "break even" in this respect. An important argument against the product-asoutput model is that it is trivial (but uninteresting) to produce a maximally efficient model that produces random output. It thus seems that a relevant life-cycle analysis requires us to incorporate both model performance and model output into the notion of product. There are two obvious ways to do this, each with its own advantages and drawbacks. The first is to stipulate a minimum performance level that a model must reach to be considered valid and to treat all models reaching this threshold as ceteris paribus equivalent. The second way is to use the performance level as a weighting function when calculating the product of a model. We will stick to the first and simpler approach in our case study later, but first we need to discuss the other quantity in the efficiency equation -the cost. Schwartz et al. (2020) propose the following formula for estimating the computational cost of producing a result R: The Costs of an NLP Model Cost(R) ∝ E · D · H(2) where E is the cost of executing the model on a single example, D is the size of the training set (which controls how many times the model is executed during a training run), and H is the number of hyperparameter experiments (which controls how many times the model is trained during model development). How can we understand this in the light of the previous discussion? First, it should be noted that this is not an exact equality. The claim is only that the cost is proportional to the product of factors on the right hand side, but the exact cost may depend on other factors that may be hard to control. Depending on what type of cost is considered -a question that we will return to below -the estimate may be more or less exact. Second, the notion of a result is not really specified, but seems to correspond to our notion of product and is therefore open to the same variable interpretations as discussed in the previous section. Third, as stated above, the formula applies only to the development phase, where the result/product is naturally understood as the performance of the final model. To clarify this, we replace R (for result) with P P (for product-as-performance) and add the subscript T (for training) to the factors E and D: DevCost(P P ) ∝ E T · D T · H(3) Schwartz et al. (2020) go on to observe that a formula appropriate for inference during the deployment phase can be obtained by simply removing the factors D and H (and, in our new notation, changing E T to E I since the cost of processing a single input instance is typically not the same at training and inference time): DepCost(P P ) ∝ E I(4) This corresponds to the product-as-performance model for the deployment phase discussed in the previous section, based on the average cost of processing a typical input instance, and has the same limitations. It ignores the quantity of data processed by a model, and it is insensitive to the initial investment in terms of development cost. To overcome the first limitation, we can add back the factor D, now representing the amount of data processed during deployment (instead of the amount of training data), and replace product-as-performance (P P ) by product-as-output (P O ): DepCost(P O ) ∝ E I · D I(5) To overcome the second limitation, we have to add the development cost to the equation: DepCost(P O ) ∝ E T · D T · H + E I · D I (6) This allows us to quantify the product and cost as they develop over the lifetime of a model, and this is what we propose to call amortized efficiency based on total deployment cost, treating development cost as a debt that is amortized during the deployment phase. Our notion of amortized efficiency is inspired by the notion of amortized analysis from complexity theory (Tarjan, 1985), which averages costs over a sequence of operations. Here we instead average costs over different life-cycle phases. As already noted, the product-as-output view is only meaningful if we also take model performance into account, either by stipulating a threshold of minimal acceptable performance or by using performance as a weight function when calculating the output produced by the model. Note, however, that we can also use the notion of total deployment cost to compare the Pareto efficiency of different models at different points of time (under a product-as-performance model) by computing average deployment cost in a way that is sensitive to development cost and lifetime usage of a model. The discussion so far has focused on how to understand the notion of efficiency in NLP by relating different notions of product to an abstract notion of cost incurred over the different phases of lifetime of a model. However, as noted in the introduction, this abstract notion of cost can be instantiated in many different ways, often in terms of a specific resource being consumed, and it may be more or less straightforward to obtain precise measures of the resource consumption. Before illustrating the different efficiency metrics with some real data, we will therefore discuss costs and resources that have been prominent in the recent literature and motivate the selection of costs included in our case study. Time and Space The classical notion of efficient computation from complexity theory is based on the resources of time and space. Measuring cost in terms of time and space (or memory) is important for time-critical applications and/or memoryconstrained settings, but in this context we are more interested in execution time and memory consumption than in asymptotic time and space complexity. For this reason, execution time remains one of the most often reported cost measures in the literature, even though it can be hard to compare across experimental settings because it is influenced by factors such as the underlying hardware, other jobs running on the same machine, and the number of cores used (Schwartz et al., 2020). We include execution time as one of the measured costs in our case study. Power and CO 2 Electrical power consumption and the ensuing CO 2 emission are costs that have been highlighted in the recent literature on resourceefficient NLP and AI. For example, Strubell et al. (2019) estimate the total power consumption for training NLP models based on available information about total training time, average power draw of different hardware components (GPUs, CPUs, main memory), and average power usage effectiveness (PUE) for data centers. They also discuss the corresponding CO 2 emission based on information about average CO 2 produced for power consumed in different countries and for different cloud services. Hershcovich et al. (2022) propose that climate performance model cards for NLP models should minimally include information about total energy consumption and location for the computation, ideally also information about the energy mix at the location and the CO 2 emission associated with different phases of model development and use. Against this, Schwartz et al. (2020) observe that, while both power consumption and carbon emission are highly relevant costs, they are difficult to compare across settings because they depend on hardware and local electricity infrastructure in a way that may vary over time even at the same location. In our case study, we include measurements of power consumption, but not carbon emission. Abstract Cost Measures Given the practical difficulties to obtain exact and comparable measure-ments of relevant costs like time, power consumption, and carbon emission, several researchers have advocated more abstract cost measures, which are easier to obtain and compare across settings while being sufficiently correlated with other costs that we care about. One such measure is model size, often expressed as number of parameters, which is independent of underlying hardware but correlates with memory consumption. However, as observed by Schwartz et al. (2020), since different models and algorithms make different use of their parameters, model size is not always strongly correlated with costs like execution time, power consumption, and carbon emission. They therefore advocate number of floating point operations (FLOPs) as the best abstract cost measure, arguing that it has the following advantages compared to other measures: (a) it directly computes the amount of work done by the running machine when executing a specific instance of a model and is thus tied to the amount of energy consumed; (b) it is agnostic to the hardware on which the model is run, which facilitates fair comparison between different approaches; (c) unlike asymptotic time complexity, it also considers the amount of work done at each time step. They acknowledge that it also has limitations, such as ignoring memory consumption and model implementation. Using FLOPs to measure computation cost has emerged as perhaps the most popular approach in the community, and it has been shown empirically to correlate well with energy consumption (Axberg, 2022); we therefore include it in our case study. Data The amount of data (labeled or unlabeled) needed to train a given model and/or reach a certain performance is a relevant cost measure for several reasons. In AI in general, if we can make models and algorithms more data-efficient, then they will ceteris paribus be more time-and energy-efficient. In NLP specifically, it will in addition benefit lowresource languages, for which both data and computation are scarce resources. In conclusion, no single cost metric captures all we care about, and any single metric can therefore be misleading on its own. In our case study, we show how different cost metrics can be combined with different notions of product to analyze resourceefficiency for NLP models. We include three of the most important metrics: execution time, power consumption, and FLOPs. Case Study To illustrate the different conceptualizations of resource-efficiency discussed in previous sections, we present a case study on developing and deploying a language model for a specific NLP task using different combinations of fine-tuning and knowledge distillation. The point of the study is not to advance the state of the art in resource-efficient NLP, but to show how different conceptualizations support the comparison of models of different sizes, at different performance levels, and with different development and deployment costs. Overall Experimental Design We apply the Swedish pre-trained language model KB-BERT (Malmsten et al., 2020) to Named Entity Recognition (NER), using data from SUCX 3.0 (Språkbanken, 2022) for fine-tuning and evaluation. We consider three scenarios: • Fine-tuning (FT): The standard fine-tuning approach is followed, with a linear layer added on top of KB-BERT. The model is trained on the SUCX 3.0 training set until the validation loss no longer decreases for up to 10 epochs. • Task-specific distillation (TS): We distill the fine-tuned KB-BERT model to a 6-layer BERT student model. The student model is initialized with the 6 lower layers of the teacher and then trained on the SUCX 3.0 training set using the teacher predictions on this set as ground truth. • Task-agnostic distillation (TA): We distill KB-BERT to a 6-layer BERT student model using the task-agnostic distillation objective proposed by Sanh et al. (2020). Following their approach, we initialize the student with every other layer of the teacher and train on deduplicated Swedish Wikipedia data by averaging three kinds of losses for masked language modelling, knowledge distillation and cosine-distance between student and teacher hidden states. The student model is subsequently fine-tuned on the SUCX 3.0 training set with the method used in the FT experiment. All three fine-tuned models are evaluated on the SUCX 3.0 test set. Model performance is measured using the F1 score, which is the standard evaluation metric for NER, and model output in number of Setup Details The TextBrewer framework (Yang et al., 2020) is used for the distillation experiments, while the Huggingface Transformers 4 library is used for finetuning and inference. More information on hyperparameters and data set sizes can be found in Appendix A. All experiments are executed on an Nvidia DGX-1 server with 8 Tesla V100 SXM2 32GB. In order to get measurements under realistic conditions, we run different stages in parallel on different GPUs, while blocking other processes from the system to avoid external interference. Each experimental stage is repeated 3 times and measurements of execution time and power consumption are averaged. 5 The different cost types are measured as follows: • Execution time: We average the duration of the individual Python jobs for each experimental stage. • Power consumption: We measure power consumption for all 4 PSUs of the server as well as individual GPU power consumption, following Gustafsson et al. (2018). Based on snapshots of measured effect at individual points in time, we calculate the area under the curve to get the power consumption in Wh. Since we run the task-agnostic distillation using distributed data parallelism on two GPUs, we sum the consumption of both GPUs for each TA run. • FLOPs: We estimate the number of FLOPs required for each stage using the estimation formulas proposed by Kaplan et al. (2020), for training (7) and inference (8): FLOP T = 6 · n · N · S · B (7) FLOP I = 2 · n · N · S · B(8) where n is the sequence length, N is the number of model parameters, S is the number of training/inference steps, and B is the batch size. The cost for fine-tuning a model is given by FLOP T , while the evaluation cost is FLOP I . For distillation, we need to sum FLOP T for the student model and FLOP I for the teacher model (whose predictions are used to train the student model). Table 1 shows basic measurements of performance and costs for different scenarios and stages. We see that the fine-tuned KB-BERT model (FT) reaches an F1 score of 87.3; task-specific distillation to a smaller model (TS) gives a score of 84.9, while fine-tuning after task-agnostic distillation (TA) only reaches 77.6 in this experiment. When comparing costs, we see that task-agnostic distillation is by far the most expensive stage. Compared to taskspecific distillation, the execution time is more than 40 times longer, the power consumption almost 100 times greater, and the number of FLOPs more than 20 times greater. Although the fine-tuning costs are smaller for the distilled TA model, the reduction is only about 50% for execution time and power consumption and about 30% for FLOPs. We also investigate whether power consumption can be predicted from the number of FLOPs, as this is a common argument in the literature for preferring the simpler FLOPs calculations over the more involved measurements of actual power consumption. We find an extremely strong and significant linear correlation between the two costs (Pearson r = 0.997, p ≈ 0). Our experiments thus corroborate earlier claims that FLOPs is a convenient cost measure that correlates well with power consumption (Schwartz et al., 2020;Axberg, 2022). However, it is worth noting that the GPU power consumption, which is reported in Table 1 and which can thus be estimated from the FLOPs count, is only 71.7% of the total power consumption of the server including all 4 PSUs. Basic Results Measuring and Comparing Efficiency So how do our three models compare with respect to resource-efficiency? The answer is that this depends on what concept of efficiency we apply and which part of the life-cycle we consider. Figure 2 plots product-as-performance as a function of cost separately for the development phase and the deployment phase, corresponding to Equations (3) and (4), which allows us to compare Pareto efficiency. Considering only the development phase, the FT model is clearly optimal, since it has both the highest performance and the lowest cost of all models. Considering instead the deployment phase, the FT model still has the best performance, but the other two models have lower (average) inference cost. The TA model is still suboptimal, since it gives lower performance at the same cost as the TS model. 6 However, FT and TS are both optimal with respect to Pareto efficiency, since they are both at the Pareto front given the data we have so far (meaning that neither is outperformed by a model at the same cost level nor has higher deployment cost than any model at the same performance level). In order to choose between them, we therefore have to judge whether a 2.4 point improvement in F1 score in the long run is worth the increase in execution time and power consumption, which in this case amounts to 0.077 nano-seconds and 0.607 micro-watts per token, respectively. For a more holistic perspective on life-time efficiency, we can switch to a product-as-output model and plot deployment efficiency as a function of both the initial development cost and the average inference cost for processing new data over lifetime, corresponding to Equation (6) and our newly proposed notion of amortized efficiency. This is depicted in Figure 3, which compares the FT and TS model (disregarding the suboptimal TA model). We see that, although the FT model has an initial advantage because it has not incurred the cost for distillation, the TS model eventually catches up and becomes more time-efficient after processing about 4B tokens and more energy-efficient after processing about 127M tokens. It is however important to keep in mind that this comparison does not take performance into account, so we again need to decide what increase in cost we are willing to pay for a given improvement in performance, although the increase in this case is sensitive to the expected lifetime of the models. Alternatively, as mentioned earlier, we could weight the output by performance level, which in this case would mean that the TS model would take longer to catch up with the FT model. Needless to say, it is often hard to estimate in advance how long a model will be in use after it has been deployed, and many models explored in a research context may never be deployed at all (over and above the evaluation phase). In this sense, the notion of life-time efficiency admittedly often remains hypothetical. However, with the increasing deployment of NLP models in real applications, we believe that this perspective on resource-efficiency will become more important. Conclusion In this paper, we have discussed the concept of resource-efficiency in NLP, arguing that it cannot be reduced to a single definition and that we need a richer conceptual framework to reason about different aspects of efficiency. As a complement to the established notion of Pareto efficiency, which separates development and deployment under a product-as-performance model, we have proposed the notion of amortized efficiency, which enables a life-cycle analysis including both development and deployment under a product-as-output model. We have illustrated both notions in a simple case study, which we hope can serve as inspiration for further discussions of resource-efficiency in NLP. Future work should investigate more sophisticated ways of incorporating performance level into the notion of amortized efficiency. Figure 2 : 2Pareto efficiency for the development phase (top) and the deployment phase (down) based on three different cost measures: execution time (left), power consumption (center), and FLOPs (right). Figure 3 : 3Amortized efficiency of the deployment phase over lifetime, based on three different cost measures: execution time (left), power consumption (center), and FLOPs (right). Most of the discussion is relevant also to other branches of AI, although some of the examples and metrics discussed are specific to NLP. Historically, the technical concept of efficiency arose in engineering in the nineteenth century, in the analysis of engine performance (thermodynamic efficiency); it was subsequently adopted in economy and social science by Vilfredo Pareto and others(Mitcham, 1994). https://huggingface.co/docs/transformers/index 5 Since we repeat stages 3 times for every model instance, task-specific distillation, fine-tuning of the distilled model, and evaluation of FT are repeated 9 times, while evaluation of TS and TA is repeated 27 times. It is worth noting, however, that the TA model can be fine-tuned for any number of specific tasks, which could make it competitive in a more complex scenario where we can distribute the initial distillation cost over a large number of finetuned models. AcknowledgmentsWe would like to thank Jonas Gustafsson and Stefan Alatalo from the ICE data center at RISE for their help with the experimental setup of the case study. Our sincere gratitude goes also to Petter Kyösti and Amaru Cuba Gyllensten for their insightful comments during the development of this work. Finally, we wish to thank the conference reviewers for their constructive feedback.ReferencesA Experimental DetailsA.1 Data SetsThe SUCX 3.0 dataset (simple_lower_mix version) 7 is used for fine-tuning, task-specific distillation and evaluation. The dataset splits are are the following: 43126 examples in the training set, 10772 in the validation set and 13504 examples in the test set.For task-agnostic distillation, we are using a deduplicated version of Swedish Wikipedia, with the following dataset split: 2, 552, 479 sentences in the training set and 25, 783 sentences in the validation set.A.2 Models and HyperparametersThe base model in our experiments is KB-BERTcased.8The hyperparameters used for fine-tuning and distillation are presented inTable 2. 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The Representation of Derivable Information in Memory: When What Might Have Been Left Unsaid Is Said

The Representation of Derivable Information in Memory: When What Might Have Been Left Unsaid Is Said Rand J Spiro Center for the Study of Reading University of Illinois at Urbana-Champaign Joseph Esposito Center for the Study of Reading University of Illinois at Urbana-Champaign Richard J Vondruska Center for the Study of Reading University of Illinois at Urbana-Champaign The Representation of Derivable Information in Memory: When What Might Have Been Left Unsaid Is Said However,a point often overlooked is that these same knowledge structures, with their information about the world's orderliness, may allow for more efficient processing and memorial representation of explicit information in discourse, in addition to their role in deriving implicit information. This paper will be concerned with the psychological processing of (imperfectly) Predictable or derivable information that is nevertheless explicit in discourse. Predictable Information in Discourse Despite the fact that most research on inferential processes in comprehension has been concerned with generation of implicit information, much inferentially related information is embodied explicitly in discourse. We are referring here primarily to pragmatic inferences, i.e., implications that are usually but not necessarily true. Language is infrequently characterized by absolute redundancy; semantic content is rarely "repeated," except for special purposes such as emphasis. However, pragmatic inferences are only imperfectly predictable. If you read that a karate champion hit a block, uncertainty is reduced by also reading that the block broke, despite the fact that that outcome is usually to be expected. Similarly, it would not be considered unusual when relating the events at a birthday party to mention that there was a cake with candles blown out by the celebrant. Many things go in stereotyped ways but require explicit mention because the stereotype does not describe all possible cases. Throughout this paper, "predictable" is used as a shorthand for "imperfectly predictable, or characterized by significantly less than perfect uncertainty." How is explicit but predictable information processed? As was mentioned above, attention has been primarily devoted to the processing of implicit predictable information, leaving little guidance on the present issue. However, in a variety of theoretical orientations, there is a common implication about how predictable information would be dealt with: simply put, explicit information, whether predictable or not, receives sufficient processing to be encoded in long-term memory. For example, Kintsch (1974) assumes "that subjects process and store [an inference] whether or not it is presented explicitly" (p. 154). It is difficult to imagine discourse representation theorists, who argue for the explicit representation in memory of implicit inferences (e.g., Frederiksen, 1975;Meyer, 1974), arguing that explicit inferences are not represented. In schema theories (e.g., Rumelhart & Ortony, 1977), explicit discourse information is used to bind schema variables, again suggesting that predictable information would receive explicit mental representation. If anything, one would expect existing theories to predict that explicit inferences would receive a stronger memorial representation than unpredictable information, given their greater contextual support. For example, in their associative network model, HAM, Anderson and Bower (1973) argued that the greater the number of interconnections between information, the greater the likelihood that information within the interconnected network would be recalled. This view will be referred to as the "storage of explicit inferences" (SEI) hypothesis. An alternative hypothesis is that predictable information, however central to a discourse, is taken for granted, processed only superficially and receives an attenuated cognitive representation or no enduring representation at all. f needed subsequently, it can be derived. This view will be referred to as the "superficial processing of explicit inferences" (SPEI) hypothesis. Processing explicit inferences in such a manner has the advantage of a cognitive economy of representation (besides a likely reduction in processing time). Most information that is acquired will never be used again. It would then seem to be more efficient to devote extra processing effort to the occasions when the information is needed (i.e., by deriving it when remembering) rather than exerting effort toward stable encoding at the time of comprehension. Experiments on the Representation of Explicit Inferences There are considerable problems in designing an empirical test of the hypothesis that explicit pragmatic inferences in discourse are not represented in long-term memory. If one merely tests memory for the inference, failure to remember could be attributed to not storing the information or to storing and then forgetting it; if the inference is remembered, it could be because it was stored and then retrieved, or it may have been generated at the time of test without having been stored. Spiro and Esposito (1977) developed a paradigm not subject to the ambiguities of interpretation of the more simple design discussed above. The primary manipulation of interest involved subsequently vitiating the force of an earlier explicit inference. If the inference is not stored, certain predictable errors in recalling it should be made. In the first experiment, subjects were presented stories which contained information A, B, and C such that B was strongly implied by A except in the presence of C. For example, the A, B, and C elements in one story (about a demonstration by a karate champion) could be paraphrased as follows: A: The karate champion hit the block. B: The block broke. C: He had had a fight with his wife earlier. It was impairing his concentration. C was either presented prior to A and 8 (C-Before), after A and B (C-After), or not at all (No-C). When C was not included in the story, if SPEI is correct, the B element should be taken for granted, processed only superficially, and not stably represented. It would be derivable if needed. However, if C is presented after A and B, memory for B should be impaired since B was not stored and C will block its derivation from A at the time of test. On the other hand, if C occurs in the text prior to A and B, then B is not strongly implied by A. B cannot be taken for granted with the assumption that it can be generated later if needed. Here B should be stably represented and memory for B should not be impaired. However, if SEI is correct, memory for B should not be affected by whether C is before or after A and B, since B is stored whether it is implied by A (C-After) or not implied by A (C-Before). Two objections to this argument can be made. The information might be stored, but remembering C might lead to a decision that the memory for B must be mistaken (a kind of output interference). However, C is present whether it occurs before or after A and B, so such an explanation would not account for differential effects of C-placement. The other possibility is that B is represented in C-After, but the representation is altered or corrected when the C information is encountered. This possibility was investigated in the second experiment. In the first experiment, the following predictions of the SPEI hypothesis were tested. More errors in response to questions about the presented predictable information (B) should be made in the C-After than in the C-Before conditions. Errors can be erroneous judgments that nothing about the implied information was presented, called B-Mention errors (e.g., the story did not mention whether the block was broken), or, when the subject believes that something about B was mentioned, remembering incorrectly what was specifically said in the direction of conforming with the C information, called B-Incorrect errors (e.g., it said in the story that the block did not break when he'hit it). Confidence in errors of the latter kind were also analyzed. If subjects are as confident about these errors as they are about their accurate responses, it would be even more difficult to maintain the hypothesis that the explicit inferences were represented. In the No-C condition, B-Mention errors may occur since B would not be represented according to the SPEI hypothesis. The more important prediction regarding the No-C condition is that B-Incorrect errors should not occur more often than in the C-Before condition. Otherwise, the differences between C-Before and C-After might be attributable to heightened accuracy due to greater salience of the implied information in the former condition rather than greater inaccuracy due to a failure to store the implied information in the latter condition. College subjects read eight target vignettes each containing A and B information, and C information included or not and placed as a function of which of the three conditions subjects were randomly assigned to. C information was always on a separate page from the A and B information, and subjects were instructed to not look back after reading a page. After reading all the vignettes, the subjects were tested for their memory for the vignettes. Of particular interest were the two types of questions, mentioned above, concerning the B information (remember, B was always explicit in the stories). The results supported the hypothesis that pragmatic inferences presented in text are superficially processed and do not receive a stable and enduring representation in memory. In the C-After condition, subjects tended either to report that the inference was not presented in the text or that the opposite of the inference was presented. Furthermore, confidence in these errors was as high as confidence in correct memories. It is difficult to retain the notion that inferences are deeply processed and stably encoded when the C-After manipulation can produce errors like remembering the block was not broken when the karate champion hit it. The results cannot be attributed to interference produced by the inference-vitiating C information at output, since the C-Before subjects would also be subject to such interference. Neither can the results be attributed to differential availability of C at output, perhaps due to primacy/recency effects related to the position of C in the text, since the information was almost always recalled. Also, unimportance of the B informtion is not a viable alternative since B tended to be central to the story (e.g., in a story about a karate champion's performance, information about his success in the demonstration is certainly important). One alternative interpretation that remains is that subjects do deeply process and stably encode the presented inference, but "correct" their representation when the inference-vitiating information is presented. If subjects are storing B and then changing or correcting it at the time C is presented, errors on B'should occur in the C-After condition no matter how soon the test is administered after reading. However, if the SPEI hypothesis is correct, when delay intervals are brief enough some surface memory for the superficially processed B information may remain, reducing the number of B errors. Accordingly, in the second experiment subjects were tested either immediately after reading each story (Interspersed Questions condition) or, as in the first experiment, after the entire set of stories had been read (Questions-After condition). Again, the C-Before and C-After manipulations were employed. The results of the second experiment replicated those of the first one in the Questions-After condition. Furthermore, the C-after effect was largely absent in the Interspersed Questions condition, demonstrating that the effect is not due to storing and then changing the representation of the B information (the explicit inference). Related Issues The discussion of implications of the superficial processing effect will at times be limited to reading rather than listening. Most of the following is of a speculative nature. Representation and Underlying Mechanisms Assuming some compatible representation system, what characterizes the processes that produce the superficial processing effect? At this time, only speculations about alternative possibilities can be offered. There are three potentially beneficial aspects of superficial processing of explicit predictable information: cognitive economy (the information need not be specifically stored in long-term memory), speed of processing (you can process and understand such information rapidly), and automaticity of processing (less conscious effort and working memory space are required). Two simple, preliminary accounts of the first factor, cognitive economy, can be offered. The superficial processing phenomenon appears most compatible with a schema-theoretic mode of representation. Perhaps variable bindings that are default (or at least high probability) values are not explicitly instantiated when they are explicit in discourse (but see the discussion of Determinants of Performance Variability below). However, one should not be overly persuaded by the simplicity of such an account. Other types of representation systems could also account for the phenomenon. For example, a spreading activation model (e.g., Collins & Loftus, 1975) might predict that explicit information is not tagged in memory when it has been recently activated with some greater than criterion strength. This issue will receive further discussion in the next section. Regarding speed of processing, several possibilities may be offered: the information is actual]y predicted, perhaps followed by a selective scanning for partial clues of confirmation (e.g., the word "broke" in the karate champion example; perhaps such checks could be made in the visual periphery and, when positive, result in saccades that skip the predicted information); or the expectation may be formed after beginning to read the predictable information followed by skipping ahead to the next linguistic unit ("Oh. They're talking about this now. Well there's no doubt how it will turn out. I can pass this by."); or temporary binding of a schema variable (essentially a verification of fit) may be more rapid than more durable instantiation; or less metacognitive activity (pondering, studying, rehearsing, etc.) may be devoted to predictable information, given its derivability (this also relates to automaticity, obviously). Regarding automaticity, it seems likely that the amount of conscious processing required would be negatively correlated with the goodness of fit to prior knowledge. Thus conscious attempts to make sense of predictable information would be expected less often. Also, related to the suggestions above regarding expectations and rapidity of processing, the operation of some preattentive process (in the sense of Neisser, 1967) is a possibility. Naturally, it may be the case that all of these factors are contributing. However, some of the factors may be mutually exclusive. For example, if default values are processed automatically, an expectation and confirmation process may be redundant. Determinants of Performance Variability Occurrence of superficial processing and failure to store information probably depends on more than predictability or derivability considered in isolation. For one thing, the derivability of other information in the discourse will have an effect. The greater the proportion of fit to one's schemata for the discourse as a whole, the more likely it is that conforming information will be left to be derived. If a story takes place in a restaurant, and all the restaurant-related information is typical, then that aspect of the story can be stored with the abstract schema node "typical restaurant activities." However, when the proportion of fit is poor, i.e., some atypical events occur, even typical, predictable events may have to be stored. Occurrence of superficial processing is also likely to be affected by the extent to which the system is taxed. When the system is overloaded, as when there is a large amount of information to be acquired or the time to acquire the information is limited, more superficial processing and leaving of information to be derived probably goes on. Perhaps the system has flexible criteria for derivability, reducing criteria under overload conditions and increasing them when processing load is light (and when demands for recall accuracy are high or when subsequent availability of the information is limited). Briefly digressing, there may be a temptation to confuse superficial processing of derivable information with skimming. However, skimming is a selective seeking and then deep processing of situationally important information (see FRUMP, in Schank & Abelson, 1975) whereas superficial processing involves selectively not processing deeply information perceived as derivable, however important it might be. In other words, the same information that might receive more attention while skimming may receive less attention in normal situations if the information is derivable. This will happen to the extent that skimming results in shallow processing of earlier information that is the basis for the derivability of the later information. Besides context-based variability in derivability criteria, research in the psychology of prediction indicates the potential operation of a general bias in determining the criterion for derivability and superficial processing. For example, Fischoff (1975Fischoff ( , 1977 has found that when people are told that some event has occurred, they increase their subjective probability estimate of the likelihood that the event was going to occur. Similarly, estimation of how much was known before being given a correct answer increases when the answer is provided. In the case of superficial processing of information in discourse, it is possible that the derivability of information is overestimated after it is explicitly encountered. It seems to be a fairly common experience, for example, to not write down an idea that you are sure will be derivable again later, only to find subsequent derivation impossible. What is being suggested here is a source of forgetting not usually discussed in memory theories: superficial processing of information whose derivability has been overestimated. The Form of Expression of Derivable Information Semantic content, prior knowledge, and task contexts are not the only determinants of perceived derivability. The linguistic form in which information is expressed will sometimes provide signals of what information is already known or can be taken for granted, as when information is expressed near the beginning of a sentence (c.f., Clark & Haviland, 1977, on the given-new strategy). Taking an example from Morgan and Green (in press), compare sentences (1) and (2). (1) The government has not yet acknowledged that distilled water causes cancer. (2) That distilled water causes cancer has not yet been acknowledged by the government. In (2) of the tilled there is a stronger implied presumption truth of the proposition regarding diswater and cancer than there is in (1). In general, it seems that placing information in a sentence-initial subordinate clause lowers the superficial processing criterion. Consider continuations (3) and (4) of "The karate champion hit the block." (3) The block broke, and then he bowed. (4) After the block broke, he bowed. The block's breaking would appear to be more taken for granted in (4) than in (3). Linguistic signals of predictability or derivability need not be implicit. Consider continuations (5), (6), and (7) of the same sentence as above. (5) Obviously, the block broke. (6) As you would expect, the block broke. (7) Naturally, the block broke. Words like "clearly" and phrases like "of course" are explicit linguistic signals that information to follow is predictable and can be superficially processed. However, one would expect that such signals could have their effect only for information within an acceptable range of plausibility. That is, a plausible but not predictable continuation may be more likely to be taken (erroneously) as predictable when preceded by a linguistic signal. However, if the information contains salient implausible aspects or something clearly irrelevant, a signalling phrase such as "as you would expect ~' might result in more attention being devoted to the continuation information. Implications for the Nature of Discourse Memory To the extent that discourse is superficially processed, memory must be reconstructive rather than reproductive. Rather than retrieving traces or instantiations of past experience, the past must be inferred or derived. Just as a paleontologist reconstructs a dinosaur from bone fragments, the past must be reconstructed from the incomplete data explicitly stored. Evidence for such reconstructive processes has been provided by , who found a pervasive tendency for subjects to produce predictable meaning-changing distortions and importations in text recall under certain conditions. In general, when subsequently encountered information contradicted continuation expectations derived from a target story, the story frequently was reconstructed in such a way as to reconcile or cohere with the continuation information. This process of inferring the past based on the present was termed accommodative reconstruction. After a long retention interval, subjects tended to be more confident that their accommodative recall errors had actually been included in the story than they were confident about the accurate aspects of their recall. Why should such gross errors occur and then be assigned such high confidence? Part of the answer surely involves their function in producing coherence. Still, it is somewhat surprising that subjects should be so sure they read information that bore not even a distant inferential relationship to what they actually did read. Spiro suggested that the basis for such an effect may be in the way information is treated at the time of comprehension; namely, it is superficially processed and not stored in longterm memory. Then, when remembering, individuals should know (at least tacitly) that considerable amounts of predictable or derivable information they have encountered will not be available in memory. In that case, recall would typically involve deriving a lot of missing information. Accordingly, it would not be surprising that subjects faced with memories that lack coherence would assume that missing reconciling information was presented but only superficially processed at comprehension. The information could then be derived at recall with high confidence. Hence the capacity for restructuring the past based on the present. Individual Differences A final caveat should be offered regarding the superficial processing effect, but also applicable to all research on schema-based processes in comprehension and memory. The assumption is usually made that there are no qualitative differences between individuals in the manner in which discourse is processed. However, Spiro and his colleagues have recently found that reliable style differences can be predicted in children (Spiro & Smith, 1978) and in college students (Spiro & Tirre, in preparation). Some individuals appear to be more discourse bound, tending toward over-reliance on bottom-up processes. Others are more prior knowledge bound, tending toward over-reliance on top-down processes. For the adult bottomup readers, prior knowledge obviously must be used to a certain extent in comprehension. However, where use of prior knowledge is more optional, e.g., in providing a scaffolding for remembering information (Anderson, Spiro, & Anderson, 1978), the bottom-up readers capitalize less. Whether the latter type of individual will evince less knowledge-based superficial processing (again an optional use of prior knowledge) is a question currently under investigation. Human associative memory. J R Anderson, G H Bower, WileyNew YorkAnderson, J. R., & Bower, G. H. Human associa- tive memory. New York: Wiley, 1973. Schemata as scaffolding for information in text. R C Anderson, R J Spiro, M C Anderson, American Educational Research Journal. in pressAnderson, R. C., Spiro, R. J., & Anderson, M. C. Schemata as scaffolding for information in text. American Educational Research Journal, 1978, in press. A sketch of a cognitive approach to comprehension. J D Bransford, N S Mccarrell, W. B. Weimer and D. S. PalermoErlbaumHillsdale, N.J.Cognition and the symbolic processesBransford, J. 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A M Collins, E F Loftus, Psychological Review. 8Collins, A. M., & Loftus, E. F. A spreading activation theory of semantic processing. Psychological Review, 1975, 8._2_2, 407-428. Hindsight # foresight: The effect of outcome knowledge on judgment under uncertainty. B Fischoff, Journal of Experimental Psychology: Human Perception and Performance, 1975, iFischoff, B. Hindsight # foresight: The effect of outcome knowledge on judgment under uncertainty. Journal of Experi- mental Psychology: Human Perception and Performance, 1975, i, 288-299. The representation of meaning in memory. W Kintsch, ErlbaumHillsdale, N.J.Kintsch, W. The representation of meaning in memory. Hillsdale, N.J.: Erlbaum, 1974. A framework for representing knowledge. M Minsky, Minsky, M. A framework for representing know- ledge. The psychology of computer vision. P. H. WinstonMcGraw-HillNew YorkIn P. H. Winston (Ed.), The psychology of computer vision. New York: McGraw-Hill, 1975. Pragmatics and reading comprehension. J L Morgan, G M Green, Theoretical issues in reading comprehension: Perspectives from cognitive psychology, linguistics. R. J. Spiro, B. C. Bruce, and W. F. BrewerHillsdale, N.J.Erlbaumin pressMorgan, J. L., & Green, G. M. Pragmatics and reading comprehension. In R. J. Spiro, B. C. Bruce, and W. F. Brewer (Eds.), Theoretical issues in reading comprehen- sion: Perspectives from cognitive psychology, linguistics, artificial intelligence, and education. Hillsdale, N.J.: Erlbaum, in press. . U Neisser, Cognitive, Psychology, Appleton-Century-CroftsNew YorkNeisser, U. Cognitive psychology. New York: Appleton-Century-Crofts, 1967. The representation of knowledge in memory. D E Rumelhart, A Ortony, R. C.Rumelhart, D. E., & Ortony, A. The representa- tion of knowledge in memory. In R. C. Schoolin 9 and the acquisition of knowledge. R J Anderson, Spiro, W. E. MontagueErlbaumHillsdale, N.J.Anderson, R. J. Spiro, and W. E. Montague (Eds.), Schoolin 9 and the acquisition of knowledge. Hillsdale, N.J.: Erlbaum, 1977. Scripts, plans, goals, and understanding. R C Schank, R P Abelson, ErlbaumHillsdale, N.J.Schank, R. C., & Abelson, R. P. Scripts, plans, goals, and understanding. Hillsdale, N.J.: Erlbaum, 1977. Remembering information from text: The "State of Schema" approach. R J Spiro, R. C.Spiro, R. J. Remembering information from text: The "State of Schema" approach. In R. C. Schooling and the acquisition of knowledge. R J Anderson, Spiro, W. E. MontagueErlbaumHillsdale, N.J.Anderson, R. J. Spiro, and W. E. Montague (Eds.), Schooling and the acquisition of knowledge. Hillsdale, N.J.: Erlbaum, 1977. Theoretical issues in reading comprehension: Perspectives from cognitive psychology, linguistics, artificial intelligence, and education. R J Spiro, R. J. Spiro, B. C. Bruce, and W. F. BrewerErlbaumHillsdale, N.J.Constructive processes in text comprehension and recall. in pressSpiro, R. J. Constructive processes in text comprehension and recall. In R. J. Spiro, B. C. Bruce, and W. F. Brewer (Eds.), Theoretical issues in reading comprehen- sion: Perspectives from cognitive psy- chology, linguistics, artificial intelli- gence, and education. Hillsdale, N.J.: Erlbaum, in press. Superficial processing of explicit inferences in text. R J Spiro, J Esposito, No. 60Urbana, IllCenter for the Study of Reading, University of IllinoisTech. Rep.Spiro, R. J., & Esposito, J. Superficial pro- cessing of explicit inferences in text (Tech. Rep. No. 60). Urbana, Ill.: Center for the Study of Reading, University of Illinois, 1977. Distinguishing subtypes of poor comprehenders: Patterns of over-reliance on conceptual-vs. datadriven processes. R J Spiro, D Smith, Tech. RepSpiro, R. J., & Smith, D. Distinguishing sub- types of poor comprehenders: Patterns of over-reliance on conceptual-vs. data- driven processes (Tech. Rep. No. 61). . Ill Urbana, Center for the Study of Reading, University of IllinoisUrbana, Ill.: Center for the Study of Reading, University of Illinois, 1978. Representing logical and semantic structure of knowledge acquired from discourse. C H Frederiksen, Cognitive Psychology. Frederiksen, C. H. Representing logical and semantic structure of knowledge acquired from discourse. Cognitive Psychology, 1975, ~, 371-458. Footnote This research was supported by the National Institute of Education under Contract No. B J F Meyer, US-NIE- C-400-76-0116North HollandAmsterdamThe organization of prose and its effects on memoryMeyer, B. J. F. The organization of prose and its effects on memory. Amsterdam: North Holland, 1975. Footnote This research was supported by the National Institute of Education under Contract No. US-NIE- C-400-76-0116.

Slot Tagging for Task Oriented Spoken Language Understanding in Human-to-human Conversation Scenarios

Task oriented language understanding (LU) in human-to-machine (H2M) conversations has been extensively studied for personal digital assistants. In this work, we extend the task oriented LU problem to human-to-human (H2H) conversations, focusing on the slot tagging task. Recent advances on LU in H2M conversations have shown accuracy improvements by adding encoded knowledge from different sources. Inspired by this, we explore several variants of a bidirectional LSTM architecture that relies on different knowledge sources, such as Web data, search engine click logs, expert feedback from H2M models, as well as previous utterances in the conversation. We also propose ensemble techniques that aggregate these different knowledge sources into a single model. Experimental evaluation on a four-turn Twitter dataset in the restaurant and music domains shows improvements in the slot tagging F1-score of up to 6.09% compared to existing approaches.

Slot Tagging for Task Oriented Spoken Language Understanding in Human-to-human Conversation Scenarios November 3-4, 2019 Kunho Kim Microsoft Corporation RedmondWAUSA Rahul Jha Microsoft Corporation RedmondWAUSA Kyle Williams Microsoft Corporation RedmondWAUSA Alex Marin Microsoft Corporation RedmondWAUSA Imed Zitouni izitouni@google.com Google, Mountain ViewCAUSA Slot Tagging for Task Oriented Spoken Language Understanding in Human-to-human Conversation Scenarios Proceedings of the 23rd Conference on Computational Natural Language Learning the 23rd Conference on Computational Natural Language LearningHong Kong, ChinaNovember 3-4, 2019757 Task oriented language understanding (LU) in human-to-machine (H2M) conversations has been extensively studied for personal digital assistants. In this work, we extend the task oriented LU problem to human-to-human (H2H) conversations, focusing on the slot tagging task. Recent advances on LU in H2M conversations have shown accuracy improvements by adding encoded knowledge from different sources. Inspired by this, we explore several variants of a bidirectional LSTM architecture that relies on different knowledge sources, such as Web data, search engine click logs, expert feedback from H2M models, as well as previous utterances in the conversation. We also propose ensemble techniques that aggregate these different knowledge sources into a single model. Experimental evaluation on a four-turn Twitter dataset in the restaurant and music domains shows improvements in the slot tagging F1-score of up to 6.09% compared to existing approaches. Introduction Spoken Language Understanding (SLU) is the first component in digital assistants geared towards task completion, such as Amazon Alexa or Microsoft Cortana. The input to an SLU component is a natural language utterance from the user and its output is a structured representation that can be used by the downstream dialog components to select the next action. The structured representation used by most standard dialog agents is a semantic frame consisting of domains, intents and slots (Tur and De Mori, 2011). For example, the structured representation of "Find me a cheap Italian restaurant" is the domain Restaurant, the intent find place, and slots [cheap] price range , * Work done while the author was at Microsoft Corporation Figure 1: Example of language understanding for task completion on a H2H conversation. In this work, our goal is to identify useful slots (marked with red rectangles). [Italian] cuisine , [restaurant] place type . Different sub-tasks within SLU have been extensively studied for human-to-machine (H2M) task completion scenarios (Sarikaya et al., 2016). We extend the task oriented SLU problem to human-to-human (H2H) conversations. A digital assistant can listen to the conversation between two or more humans and provide relevant information or suggest actions based on the structured representation captured with SLU. Figure 1 shows an example of capturing intents and slots expressed implicitly during a conversation between two humans. The digital assistant can show general information about the restaurant Mua, and provide the opening hours based on the captured structured representation. These types of H2H task completion scenarios may allow digital assistants to suggest useful information to users in advance without them needing to explicitly ask questions. In this paper, we investigate SLU oriented to-wards task completion for H2H scenarios with a specific focus on solving the slot tagging task. Some early conceptual ideas on this problem were presented in DARPA projects on developing cognitive assistants, such as CALO 1 and RADAR 2 . This work can be seen as an effort to formalize the problem and propose a practical framework. SLU for task completion in H2H conversations is a challenging problem. Firstly, since the problem has not been studied before, there are no existing datasets to use. Therefore, we built a multiturn dataset for two H2H domains that we found to be prevalent in Twitter conversations: Music and Restaurants. The dataset is described in more detail in Section 4. Secondly, the task is harder than H2M conversations in several aspects. It is hard to identify the semantics of noisy H2H conversation text with slang and abbreviations, and such conversations have no explicit commands toward the digital assistants requiring the assistant to indirectly infer users intent. In this work, we introduce a modular architecture with a core bi-directional LSTM network, and additional network components that utilize knowledge from multiple sources including: sentence embeddings to encode semantics and intents of noisy texts with web-data and click logs, H2M based expert feedback, and contextual models relying on previous turns in the conversation. The idea of adding components is inspired from some recent advances in H2M SLU that use additional encoded information Su et al., 2018;Kim et al., 2017;Jha et al., 2018). However, these work only considered adding a component from a single knowledge resource. Furthermore, since these additional components bring in information from different perspectives, we also experimented with deep learning based ensemble methods. Our best ensemble method outperforms existing methods by 6.09% for the Music domain and 2.62% for the Restaurant domain. In summary, this paper makes the following contributions: • A practical framework on slot tagging for task oriented SLU on H2H conversations using bidirectional LSTM architecture. • Extension of the LSTM architecture utilizing knowledge from external sources (e.g. Web data, click logs, H2M expert feedback, and pervious sentences) with deep learning based ensemble methods • Newly developed dataset for evaluating task oriented LU on H2H conversations We begin by describing our methods for H2H slot tagging in Section 3. We then describe the data used in our experiments in Section 4 and discuss results in Section 5. This is followed by a review of the related work and conclusion. Related Work LU involves domain classification, intent classification, and slot tagging (Tur and De Mori, 2011;Sarikaya et al., 2016). Recently various deep neural network (DNN) models have been studied to solve each of these task, such as deep belief network (Sarikaya et al., 2011), deep convex network (Deng et al., 2012), RNN and LSTM (Ravuri and Stolcke, 2015;Mesnil et al., 2015). Recent advances in LU use additional encoded information to improve DNN based models. There have been some attempts to use data or models from existing domains. One direction is to do transfer learning. Kim et al. (2017) and Jha et al. (2018) utilized previously trained models relevant to the target domain as expert models. They use the output of expert models as additional input to add relevant knowledge while training for the target domain. Goyal et al. (2018) reused low-level features from previously trained models and only retrained high level layers to adapt to a new domain. There have also been some attempts to use contextual information. Xu and Sarikaya (2014) used past predictions of domains and intents in the previous turn for predicting current utterance. expanded upon this work by using a set of past utterances utilizing a memory network (Sukhbaatar et al., 2015) with an attention model. Subsequent works attempted to use the order and time information. Bapna et al. (2017) additionally used the chronological order of previous sentences, and Su et al. (2018) used time decaying functions to add temporal information. Our work trains a sentence embedding that encodes the semantics and intents. DSSM and its variants (Huang et al., 2013;Shen et al., 2014;Palangi et al., 2016) are used for training sentence embedding, which were originally used for finding Figure 2: Overview of our slot tagging architecture. Our architecture consists with the core network (Section 3.1) and additional network components utilizing knowledge from multiple sources (Each discussed in Section 3.2.1, 3.2.2, 3.2.3). A network ensembling approach is applied on additional components (Section 3.3), figure shows with the attention mechanism. relevance between the query and retrieved documents in a search engine. Also there have been attempts to use sentence embeddings similar to our data (Twitter). Dhingra et al. (2016) trained an embedding for predicting hash tags of a tweet using RNNs, Vosoughi et al. (2016) used an encoderdecoder model for sentiment classification. All of the previous methods have studied LU components for task completion in H2M conversations. On the other hand, prior work on LU on H2H conversations has focused on dialog state detection and tracking for spoken dialog systems. Shi et al. (2017) used CNN model, and later extended multiple channel model for a cross-language scenario (Shi et al., 2016). Jang et al. (2018) used attention mechanism to focus on words with meaningful context, and Su et al. (2018) used a time decay model to incorporate temporal information. Figure 2 shows the overview of our slot tagging architecture. Our modular architecture is a core LSTM-based network and additional network components that encode knowledge from multiple sources. Slot prediction is done with the final feed forward layer, whose input is the composition of the output of the core network and the additional components. We first describe our core network and then the additional network components, followed by our network ensembling approach. Methods Core Network Our core network is a bidirectional model similar to Lample et al. (2016). The first characterlevel bidirectional LSTM layer extracts the encoding from a sequence of characters from each word. Each character c is represented with a character embedding e c ∈ R 25 , and the sequence of the embedding is used as the input. The layer outputs f c = LST M f orward (e c ) (1) b c = LST M backward (e c )(2) for each character, where f c , b c ∈ R 25 . The second word-level bidirectional LSTM layer extracts the encoding from a sequence of words for each sentence. For each word w i , the input of the layer is g i = f c i ⊕ b c i ⊕ e w i where f c i and b c i is the output of previous layer, e w i ∈ R 100 is the word embedding vector, and ⊕ is a concatenation operator of vectors. We use pre-trained GloVe with 2B tweets 3 (Pennington et al., 2014) for the word embedding. The forward and backward word-level LSTM's produce f w i = LST M f orward (g i ) (3) b w i = LST M backward (g i ) (4) where f w i , b w i ∈ R 100 . Finally, slot l i is predicted with the last feed forward layer with the input h i = f w i ⊕ b w i . Our model is trained using stochastic gradient descent with Adam optimizer (Kingma and Ba, 2015), with the mini batch size 64 and the learning rate 0.7×10 −3 . We also apply dropout (Srivastava et al., 2014) on embeddings and other layers to avoid overfitting. The learning rate and dropout ratio were optimized using random search (Bergstra and Bengio, 2012). The core network can be used alone for slot tagging, however we discuss our additional network components in the following sections for improving our architecture. Additional Network Components In this section, we discuss additional network components that encode knowledge from different sources. Encoded vectors are used as additional input to the feed forward layer as shown in Figure 2. Sentence Embedding for H2H Conversations Texts from H2H conversations are noisy and contain slang and abbreviations, which can make identifying their semantics challengins. In addition, it can be challenging to infer their intents since there are no explicit commands toward the digital assistants. The upper part of Figure 3 shows part of a conversation from Twitter. The sentence lacks the semantics needed to fully understand "club and country". However, if we follow the URL in the original text, we can get additional information to assist with the understanding. For instance, the figure shows texts found from two sources, 1) web page title of the URL in the tweet and 2) web search engine queries that lead to the URL in the tweet. We use web search queries and click logs from a major commercial Web search engines to find queries that lead to clicks on the URL. Using this information, we can infer from the Web page title that the "club and country" referred to in the tweet are Atletico Madrid and Nigeria. Furthermore, the search queries from the search engine logs indicates possible user intents. In our approach, we encode knowledge found from these two sources based on the URL. In our dataset, we were able to gather 2.35M pairs of tweet text with URL and web search engine queries that lead to the same URL, and 420K pairs of tweet text and web page titles of the URL. We then use this information to train a sentence embedding model that can be used to encode the semantics and implicit intents of each H2H conversation sentence. Our approach is to train a model that projects texts from H2H conversation and texts from each knowledge sources into a same embedding space, keeping the corresponding text pairs close to each other with other nonrelevant texts being apart, as shown in Figure 3. The learned embedding model F then can be used to represent any texts from H2H sentences with a vector with semantically similar texts (or similar intents) being projected close to each other in the embedding space. Embeddings are used as additional component of our modular architecture, so that the semantic and intent information can be utilized in our slot tagging model. We use the deep structured semantic model (DSSM) architecture (Huang et al., 2013) to train the sentence embedding encoder. DSSM uses letter-trigram word hashing, so it is capable of partially matching noisy spoken words so that we can get more robust sentence embeddings for H2H conversations. Let S be the set of sentences from the H2H conversations that have the URL. For each sentence s ∈ S, we find corresponding texts (web page title of the URL, web search engine queries to the URL) T + s and randomly choose non-related texts T − s from corresponding texts of other sentences (in other words, from different URLs). Like the original DSSM model, each sentence s, t + s ∈ T + s , and t − s ∈ T − s are initially encoded with letter-trigram word hashing vector x, and used as the input of two consecutive dense layers, x = W 1 x + b 1 (5) y = W 2 x + b 2(6) where x ∈ R 1000 and y ∈ R 300 . We train the model to favor choosing t + s ∈ T + s over t − s ∈ T − s for each s. So the loss function is defined as minimizing the likelihood, loss = −log s,T + s P (T + s |s)(7)P (T + s |s) = s,t + s ∈T + s exp(γsim(s, t + s )) t∈T exp(γsim(s, t))(8) sim(s, t + s ) = cos(y s , y t + s ) where cos is cosine similarity of two encoded vectors. Please refer to the original paper (Huang et al., 2013) for further details. The dropout ratio, Figure 3: Example of H2H conversation text with URL link and corresponding texts found by following the URL. We use those two sources of corresponding texts to train sentence embedding models. Each model projects the original text and its corresponding texts to a close position in the sentence embedding space, while non-relevant texts are being apart. learning rate, and γ are selected based on a random search (Bergstra and Bengio, 2012), which are 0.0275, 0.4035 × 10 −2 , and 15 respectively. The output of the second dense layer y of trained model is used as the sentence embedding: for each sentence we extract the sentence embedding v s ∈ R 300 . Contextual Information Contextual information extracted from previous sentences is known to be useful to improve understanding of human spoken language on other scenarios (Xu and Sarikaya, 2014;Su et al., 2018). To obtain knowledge from a previous sentence in the conversation, we extract a contextual encoded vector using the memory network , which uses the weighted sum of the output of word-level bidirectional LSTM h in the core network (Section 3.1) from previous sentences. We did not consider a time decaying model (Su et al., 2018) since our data has a small number of turns. We tested the model with some variations on 1) number of previous sentences to use and 2) weighting scheme (uniform or with attention). using the implementation from the original pa-per 4 . From our experiments, the best result was achieved using the previous two sentences with a uniform weight. We use this model to extract the contextual encoded vector v c ∈ R 100 . We adopt this idea to take advantage of massive amount of labeled data for H2M conversations. Instead of transferring knowledge from domain to domain, we transfer the knowledge of different tasks within a similar domain. For example, we use Places (H2M) domain for the Restaurant (H2H) domain, and Entertainment (H2M) domain for the Music (H2H) domain. We use previously trained slot tagging models on H2M conversations on similar domains as our expert model, which has the same architecture as our core net-work (Section 3.1). These H2M models were originally used for the SLU component of a commercial digital assistant. The output of word-level bidirectional LSTM h is then extracted as the encoded vector from H2M expert model v e ∈ R 200 . Human-to-Machine Expert Feedback Network Ensemble Approaches Since additional network components (sentence embedding v s , contextual information from previous turns of the conversation v c , and H2M based expert feedback v e ) bring information from different perspectives, we discuss how to compose them into a single vector k with various ensemble approaches. • Concatenation: Here, we simply concatenate all encodings into a single vector, k = v s ⊕ v c ⊕ v e(10) • Mean: We first apply a separate dense layer to each encoded vector to match dimensions and transform into the same latent space, and then take the arithmetic mean of transformed vectors. v {s,c,e} = W {s,c,e} + b {s,c,e} (11) k = mean(v s , v c , v e )(12) In the Figure 2, we denote the dense layer applied to each encoded vector v {s,c,e} as D {s,c,e} for simplicity of representation. Each transformed vector v {s,c,e} ∈ R 100 , so k ∈ R 100 . • Attention: We apply an attention mechanism to apply different weights on the encoded vectors for each sentence. For our problem, it is not straightforward to define a context vector for each sentence to calculate the importance of each encoded vector; therefore, we adopted the idea of using a global context vector (Yang et al., 2016). The global context vector u ∈ R 100 can be thought as a fixed query of "finding the informative encoded vector for slot tagging" used for each sentence. The weight of each encoded vector is calculated with the standard equation of calculating the attention weight, which is the softmax of the dot product of encoding and context vector, w {s,c,e} = exp(tanh(v {s,c,e} ) u) v ∈{v s ,v c ,v e } exp(tanh(v ) u)) (13) k = w s v s + w c v c + w e v e(14) where v {s,c,e} are same as Equation 11. The combined single vector k is then aggregated with the output of core network h, k ⊕ h is used as the input of the final feed forward layer as shown in Figure 2. The same hyperparameters (mini batch size, learning rate, dropout ratio) and optimizer is used as stated in the baseline model (Section 3.1). Data Although some datasets with H2H conversations are available (Forsythand and Martell, 2007;Danescu-Niculescu-Mizil and Lee, 2011;Nio et al., 2014;Sordoni et al., 2015;Lowe et al., 2015;Li et al., 2017), they were not feasible to use for experimenting on our task. All datasets excluding the Ubuntu Dialogue (Lowe et al., 2015) were collected without any restrictions on the domain and, as a result, there were insufficient training samples to train a slot tagging model for a specific domain. In addition, the Ubuntu Dialogue dataset (Lowe et al., 2015) focuses on questions related to Ubuntu OS, which is not an attractive domain an intelligent focus that focuses on task completion rather than question answering. Since there were no existing datasets that were sufficient for our task in H2H conversation, we built our own dataset for the experiments. It was difficult to acquire actual H2H conversations from instant messages due to privacy concerns. Therefore, we chose to use public conversations on Twitter and extracted sequences in which two users engage in a multi-turn conversation. Using this approach, we were able to collect 3.8 million sequences of four-turn conversations using Twitter Firehose. We focused on two domains for our experiments: Restaurants and Music. To acquire the dataset for each domain, we first defined a set of key phrases and found the candidate conversations with at least one of those key phrases. Key phrases consisted of the top 100 most frequently used unigrams and bigrams on each relevant domain from the H2M conversation dataset. We used the H2M Places domain to find the top n-grams for the Restaurant domain and the H2M Entertainment domain to find top n-grams for the Music domain. Places includes other type of places besides restaurants (e.g. tour sights), and also Entertainment includes other genre (e.g. movies). So we manually replaced unigrams and bigrams that were not music or entertainment related, and also some terms that are too general (e.g. time, call, find). We were able to gather 16K and 22K candidate conversations for the Restaurant and Music domains, respectively, using the keyphrases. We randomly sampled 10K conversations for each domain for annotating slots and domain. Annotation was done by managed judges, who had been trained over time for annotating SLU components such as intents, slots and domains. A guideline document was provided with the precise definition and annotated examples of each of the slots and intents. Agreement between judges and manual inspection of samples for quality assurance was done by a linguist trained for managing annotation tasks. We also ensured that judges did not attempt to guess at the underlying intents and slots, and annotate objectively within the context from the text. We only keep the conversations that are labeled relevant to each domain by annotators. Table 1 shows an example conversation from the dataset in each domain, and Table 2 shows the dataset statistics. Experiments Experimental Setup All experiments were done with 10-fold cross validation for the slot tagging task, and we generated training, development, test datasets using 80%, 10%, and 10% of the data. The development dataset is used for hyperparameter tuning with random search (Bergstra and Bengio, 2012) and early stopping. The baseline is set with core network only (Section 3.1). We evaluated the performance of each of the models with precision, recall, and F1. We checked for statistical significance over the baseline at the p-value < 0.05 using the Wilcoxon signed-rank test. Evaluation on Adding Sentence Embeddings for H2H Conversations In this section, we evaluate adding the sentence embeddings into our slot tagging architecture introduced in Section 3.2.1. Table 3 shows the results of adding sentence embeddings, compared with the baseline and existing sentence embedding methods. We extracted two months of recent tweets that had non-twitter domain URLs in the text for our method. Below is the brief description of each method: • DSSM (Deep Structured Semantic Model) (Huang et al., 2013): Pre-trained DSSM model from the authors , trained with pairs of (Major commercial web search engine queries, clicked page titles). • Tweet2Vec (Dhingra et al., 2016): The model was originally used to predict hashtags of a tweet. We use the pre-trained model from the authors, which used 2M tweets for training. • Ours (Tweets, Web Search Engine Queries): Trained our model with 2.35M pairs of (Tweet text with shared URL, Web serach engine queries that lead to the shared URL). We extracted most frequent queries (up to eight) found from the major commercial web search engine query logs. • Ours (Tweets, Web Page Titles): Trained our model with 420K pairs of (Tweet text with shared URL, web page title of URL). The result shows that adding our proposed sentence embedding network improves the slot tagging result compared to the baseline, while other previous methods have a negative effect. This implies that 1) a sentence embedding specifically trained for H2H conversation texts are needed (compared with original DSSM), 2) our idea of embedding semantics and intentions from web data and search engine query logs can help to improve the slot tagging task (compared to the Tweet2Vec). Since our sentence embedding network trained with web page titles gives the most significant improvement, we used this for further evaluation. Evaluation on Utilizing Knowledge Sources We also tested adding contextual information and H2M expert feedback network components to our slot tagging architecture. Table 4 shows the result of adding each. Results show that 1) adding network component from each knowledge source leads to an improvement on at least one of the domain, 2) improvement on each method varies with the domain. Adding sentence embeddings and contextual information led to significant improvements for the Restaurant domain while contextual information and H2M expert feedback led to significant improvements for the Music domain. Evaulation on Network Ensemble Approaches We also conducted an experiment to include all network components to see if we can improve further by considering multiple knowledge sources together. The result is shown in the lower part (row 5-7) of Table 4 with different ensembling methods introduced in Section 3.3. It shows that any of the ensemble approaches to add all of the network components leads to better results than adding either of them individually. The result implies that each of the proposed method improves the slot tagging method from different perspectives so all of them can be considered. Also, we see that attention has the best results among ensemble approaches, with 2.62% higher F1 score for the Restaurant domain, and 6.09% for the Music domain compared to the baseline. This implies the attention model can help to find the best way to ensemble additional components by predicting the importance of each component for each sentence. Especially, we could see a statistically significant improvement on the Music domain compared with other methods. We believe this is because the improvement of each network component on the Music domain is more obvious compared to the Restaurant domain. We would like to test in other domains for the future work. Table 4: Comparison on adding additional network components from each knowledge source and network ensemble approaches that adds all components. P, R, F1 stands for precision, recall, F1-score (%) respectively. * denotes the F1 score is statistically significant compared to the baseline. ** denotes the F1 score of ensemble model is also statistically significant compared to the concatenation ensemble model. Conclusion We studied slot tagging in H2H online text conversations. Starting from a core network with bidirectional LSTM, we proposed to use additional network components and ensemble them to augment useful knowledge from multiple sources (web data, search engine click logs, H2M expert feedback, and previous utterances). Experiments with our four-turn Twitter dataset on Restaurant and Music domains showed that our method improves up to 6.09%-points higher F1 on slot tagging compared to existing approaches. For future work, we plan to study our model on domain and intent classification, and also on additional domains. Kim et al. (2017) andJha et al. (2018) introduced a transfer learning method, which reuses the knowledge from existing trained models on relevant domains (i.e. expert models) to take advantage of previous knowledge to train on a new domain. They extract the output of the expert model and use it as an additional input of feed forward layer for the model on a new domain. Restaurant A: [lunch] meal type ? B: [lunch] meal type sounds good. Our routine usually involves sitting at [Nano's] place name with our packed/purchased [lunches] meal typecare to join? A: great. I'll get something from [physiol] place name and meet you there at...? B: I'll be there in [5-10 mins] time Music A: [quavo] media person got another one B: I was bout to listen earlier but it said [feat] media role [Lil Uzi Vert] media person lol A: he [rap] media genre for about two minutes you don't even gotta listen to [lil uzi] media person B: [quavo] media person already a legend man Table 1: Example conversation in each domain of our dataset Table 2 : 2Statistics of our dataset. Each column shows the number of items in the dataset. "Conv." stands for conversations. Table 3 : 3Comparison of adding sentence embedding component to our architecture. P, R, F1 stands for precision, recall, F1-score (%) respectively. * denotes the F1-score is statistically significant compared to the baseline. Contextual information is extracted from previous two sentences with uniform weighting. For the H2M expert model, we used the pretrained model of Entertainment domain for the target Music domain, and the Places domain for the target Restaurant domain for H2M LU. The upper part (rows 2-4) in Ensemble (Concatenation) 74.09 64.89 69.21 * 66.41 50.10 57.07 * + Ensemble (Mean) 73.20 65.43 69.07 * 65.18 51.01 57.11 * + Ensemble (Attention) 74.03 65.11 69.25 * 66.19 51.29 57.70 * *Model Restaurant Music P R F1 P R F1 Core Network (Baseline) 71.23 62.68 66.63 64.33 44.14 51.61 + Sentence Embedding 71.19 63.51 67.11 * 63.70 44.44 52.32 + Contextual 72.74 64.75 68.47 * 64.42 49.16 55.72 * + H2M Expert 71.91 61.21 66.63 64.14 44.67 52.64 * + https://en.wikipedia.org/wiki/CALO 2 https://www.cmu.edu/cmnews/extra/ 030718_darpa.html Downloaded from https://nlp.stanford.edu/ projects/glove/ https://github.com/yvchen/ ContextualSLU Slots in Restaurant domain include absolute location, amenities, atmosphere, cuisine, date, distance, meal type, open status, place name, place type, price range, product, rating, service provided, time. 6 Slots in Music domain include app name, media award, media category, media content rating, media genre, media keyword, media language, media lyrics, media nationality, media person, media price, media release date, media role, media source, media technical type, media title, media type, media user rating, radio call sign, radio frequency AcknowledgementWe would like to thank Eric Mei and Soumya Batra for their help on data collection and labeling. 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THE STATE OF MACHINE TRANSLATION IN EUROPE

This first half of this general survey covers MT and translation tools in use, including translators workstations, software localisation, and recent commercial and in-house MT systems. The second half covers the research scene, multilingual projects supported by the European Union, networking and evaluation.In comparison with the United States and elsewhere, the distinctive features of activity in Europe in the field of machine translation and machine-aided translation are: (i) the development and popularity of translator workstations, (ii) the strong software localisation industry, (iii) the vigorous activity in the area of lexical resources and terminology, (iv) and the broad based research on language engineering supported primarily by European Union funds.

THE STATE OF MACHINE TRANSLATION IN EUROPE John Hutchins University of East Anglia THE STATE OF MACHINE TRANSLATION IN EUROPE This first half of this general survey covers MT and translation tools in use, including translators workstations, software localisation, and recent commercial and in-house MT systems. The second half covers the research scene, multilingual projects supported by the European Union, networking and evaluation.In comparison with the United States and elsewhere, the distinctive features of activity in Europe in the field of machine translation and machine-aided translation are: (i) the development and popularity of translator workstations, (ii) the strong software localisation industry, (iii) the vigorous activity in the area of lexical resources and terminology, (iv) and the broad based research on language engineering supported primarily by European Union funds. Translator's workstations Recent years have seen great advances in the development and exploitation of support tools for translator. The four most widely used translator's workstations originate from Europe: Trados' Translation Workbench, IBM's TranslationManager, Eurolang's Optimizer, and STAR's Transit. In addition, Europe has been the centre for most of the background and current research on workstations: the TWB project funded by the European Union, and the Commission's own EURAMIS project. For professional translators, the attraction of the workstation is the integration of tools from simple word processing aids (spelling and grammar checkers) to full automatic translation. The translator can choose to make use of whichever tool seems most appropriate for the task in hand. The vendors of these systems always stress that translators do not have to change their work patterns; the systems aim to increase productivity with translator-oriented tools which are easy to use and fully compatible with existing word processing systems. In facilities and functions, each offer similar ranges: multilingual split-screen word processing, terminology recognition, retrieval and management, translation memory (pre-translation based on existing texts), alignment software for users to create their own bilingual text databases, retention of original text formatting, and support for very wide range of European languages, both as source and target languages. Integration to MT systems is now provided by three of the workstations. In the case of Trados access is provided to the Transcend software from Intergraph; IBM Translation Manager links up with Systran; and Eurolang Optimzer with Logos. After its disappointing experience with Eurotra, the European Commission has devoted most of its research support to the development of practical tools for translators and to the creation of essential lexical resources. Most of these projects will be described later. A major project was the TWB (Translator's Work Bench) project --within the ESPRIT framework --which began in 1989 and ended in 1994. The project, led by Triumph-Adler and involving 10 members from companies and universities, investigated the requirements of translators and proposed most of the features which are now commonplace in translator workstations: multilingual editor, document converters, access to lexica and terminology databases (e.g. Eurodicautom), access to MT systems (in this case, METAL), tools for term bank building, pre-translation and translation memory, and in particular a tool kit (System Quirk) for the analysis of texts and the development of lexical resource databases (thesauri, knowledge bases) from corpora and term banks. (A full description of the TWB project has recently been published: Kugler et al. 1995). A second project in this area has been TRANSLEARN, an LRE project for an interactive corpusbased translation drafting tool (a prototype translation memory system) based on EU regulations and directives from the CELEX (European Union law) database. The languages involved have been English, French, Portuguese and Greek. A third project, this time funded by Volkswagen, is investigating the use of a domain knowledge base integrated with a linguistic database as a translation tool; the languages are German and Bulgarian. The Translation Service of the Commission itself is now developing its own workstation: EURAMIS. The aim is to optimize the efficiency of the translation resources already available (e.g. the termbank Eurodicautom), to create a database of translated EU documents (as a 'translation memory'), and to provide easy access to MT systems. It will allow individual translators to develop their own tailor-made resources and facilities, with tools for text corpus management, glossary construction, and text alignment. A particular emphasis will be on the integration of MT and translation tools, including the mutual enrichment of Systran dictionaries and Eurodicautom lexical databases. The European strength in the area of terminology continues. A recent list of termbanks from InfoTerm in Vienna reveals that over two thirds of the 100 terminological databases recorded are based in Europe. The links between terminologists and translators have been a marked feature on the European scene --links which are now extending to the MT community. Terminology and the lexicon was the topic of an EAMT workshop in 1993 (Steffens 1995), and this August another EAMT workshop was held in conjunction with the international terminology conference in Vienna. MT in use As many observers have commented the take-up of MT systems in Europe has been much slower than expected; markets are small and fragmented, and professional translators remain hostile. The potential is enormous, but far from being exploited. MT systems are used primarily by large translation services and by multinational companies. Smaller organisations favour translation workbenches, sometimes networked for sharing term databases and translation memories; and increasingly workstations are being considered by individual freelance translators. The cheaper PC-based systems are generally only of interest to those with occasional translation needs, and still not being purchased on the scale apparent in North America or Japan. Some of the more notable recent installations in multinational companies to mention are: Ericsson, where the Logos system is providing 10% of translation needs (for producing manuals and documentation in French, German and Spanish); SAP, using Metal for German-English translation and Logos for English-French (totalling some 8 million words in the current year); and Siemens, providing a service based on Metal. At the European Commission, the use of Systran continues to grow, now amounting to some 200,000 pages per year. The main users are non-linguist staff needing translations for information purposes (short-lived and/or repetitive documents), and drafts for writing documents in non-native languages. The increased use is attributable to improved interfaces and access tools. For translators the main development is EURAMIS. However, perhaps the most distinctive feature of the European scene is the growth of companies providing software localisation. These services are acquiring considerable experience in the use of translation aids and MT systems (e.g. Logos, Metal and XL8). As a forum for the interchange of experience and the establishment of standards the Localisation Industry Standards Association was set up in 1990; the association publishes a newsletter (LISA Forum) and has produces a CD-Rom directory of products, standards and methods (LISA Showcase). A major centre for localisation is Ireland, which since 1994 has its own Software Localisation Group, holding conferences and workshops and recently setting up a Localisation Resources Center (with support from the Irish government and EU.) Commercial systems In comparison with the United States and Japan, there have been surprisingly few MT systems developed and manufactured by European organisations. Two come from the former Soviet Union. From St Petersburg comes the very successful PC-based Stylus Russian-English, English-Russian, and German-Russian systems oriented primarily to business correspondence. Sales have been good in both East and West Europe. From Kharkov (Ukraine) come the PARS systems for Russian and Ukrainian to and from English, with sales mainly in the Ukraine and East Europe. In Germany, changes in organisation have affected the development of Metal in recent years, however it is now available as a client/server system on Unix workstations and PCs, and as a network service for occasional users. Best developed remains the German-English system, but other languages have now been released: German-Danish, French-English, English-Spanish, and work is reported on Catalan, Italian, Portuguese. The most recent entrant from Germany is the Personal Translator, a joint product of IBM and von Rheinbaben & Busch, based on the LMT (Logic-Programming based Machine Translation) slot-grammar transfer-based system under development at IBM since 1985. LMT itself is available as an MT component for the IBM Translation Manager. The Personal Translator is a PC-based system and intended primarily for the non-professional translator (consultants, secretaries, technicians) and competing therefore with Globalink and similar products. At present the languages are German and English in both directions. Other less sophisticated PC-based systems include the following: from Italy there is Hypertrans, developed initially for translating patents in Italian and English, then expanded to include other European languages (French and German, Spanish) and currently used to translate patent abstracts for the European Patent Office, and now sold for wider general purposes; from France there is the Al-Nakil system for translating between Arabic, French and English; and from Denmark there is the PC-based Winger system for Danish-English, French-English and English-Spanish, now also marketed in North America, and the TranSmart system for Finnish-English from Kielikone Ltd. Custom-built systems Both Winger and TranSmart were initially built for specific customers. In the case of TranSmart, this was developed originally as the Kielikone translation workstation for Nokia Telecommunications. Subsequently, versions were installed at other Finnish companies and the system is now being marketed more widely. A similar story applies to GSI-Erli. This large language engineering company developed an integrated in-house translation system combining an MT engine and various translation aids and tools on a common platform AlethTrad. Recently it has been making the system available in customised versions for outside clients. Custom-built MT has become a speciality of Cap Volmac Lingware Services, a Dutch subsidiary of the Cap Gemini Sogeti Group. Over the years this software company has constructed controlled-language systems for textile and insurance companies, mainly from Dutch to English. The Dutch language has also been the focus of an in-house system built at Hook & Hatton, in this case initially for the translation of documents in chemical engineering, but now as a commercial MT service for industrial customers in other subject fields. Begun as a simple pattern matching method to find frequently recurring phrases, the system added a terminology database and simple grammar rules and is now an efficient low-cost low-quality MT system. In recent years, probably the best known success story for custom-built MT is the PaTrans system developed for LingTech A/S to translate English patents into Danish. The system is based on methods and experience gained from the Eurotra project of the European Commission. Nearly all the successful applications of MT and the in-house systems are based on the control of input texts. Research on this area of 'language engineering' has been strong in recent years in Europe. The SECC project in Leuven, supported by Siemens Nixdorf, Cap Gemini Innovation and Sietec is using MT methodology as the basis for a tool for writing in a controlled language, Simplified English; the Metal system is effectively translating English into the controlled language. Earlier this year, the University of Leuven organised a conference on controlled languages, the first devoted exclusively to this theme (CLAW, 1996). Participation was international, but European research was strongly represented with 16 of the 22 contributions; the majority on controlled English, but also German and Swedish represented. Research on MT It is undeniable that MT research in Europe has quantitatively declined since the ending of the Eurotra project (officially in 1992, but effectively some years earlier). The failure to produce a working system has been attributed mainly to the over-emphasis on linguistic formalism, the neglect of the lexical databases, and the absence of industrial participation. Nevertheless, it is agreed that the project did create in Europe a strong research community in language engineering which was able to successfully collaborate cross-nationally. Eurotra itself has however brought forth a number of continuations. One has been mentioned already: PaTrans. Another is the CAT2 system at Saarbrücken, an experimental platform (a stratificational transfer-based unification grammar system) which is the MT engine for the EUfunded (LRE) project ANTHEM. The prototype is to be a multilingual interface for natural language input and retrieval of medical diagnoses. The sublanguage system is based on the analysis of a Dutch and French corpus from the Belgian Army and on the widely used Systematised Nomenclature of Human and Veterinary Medicine. Another by-product of Eurotra is the KIT-FAST system, an experiment in knowledge-based MT with an emphasis on tackling problems of anaphora and the representation of communication functions. Two research groups are investigating MT systems where control of the input is the result of user-computer dialogue: monolinguals compose messages interactively in their own language and translation into an unknown target language is performed automatically. At the University of Manchester Institute of Science and Technology the experimental system is based on the 'pure' implementation of an example-based approach. At the University of Grenoble, the LIDIA system involves interactive disambiguation and reverse translation into the user's language. Other research efforts are taking place at ISSCO (Geneva) within a unification grammar framework on a sublanguage system for avalanche warnings, at the Sharp Laboratories of Europe (Oxford) on the shake-and-bake model of MT, at the Institute for Information Transmission Problems of the Russian Academy of Sciences (Moscow) on ETAP-3 --a continuation of the Meaning-Text Model approach to MT begun in the 1970s, initially for French-Russian but now for English-Russian translation of electrical engineering and computer science texts. Another project with East European antecedents is taking place in Berlin (Gesellschaft für Multilinguale Systeme) on a Russian-German system based on the Metal platform. The Russian analysis derives form previous research at the German Academy of Sciences and the German synthesis on systems developed for Metal. The aim is initially a SunSparc system. The application of general-purpose natural language systems to automatic translation is becoming more and more common. The large LOLITA project at Durham University is aiming to develop a problem-and domain-independent natural language engineering core for multiple applications. One of these is translation, and reports have been given of an experiment involving Italian-English translation via a conceptual representation. An outcome of the Core Language Engine project at Cambridge has been the SLT (Spoken Language Translator) developed as a prototype speech translation between Swedish and English in the domain of air travel planning, and first demonstrated in 1993. Subsequently, the team have shown the flexibility and portability of the architecture by the relatively rapid construction of a system for French and English speech translation in the same domain. The system operates with a statistically trainable processor producing quasi-logical representations. The best known spoken language system under development in Europe is, of course, the Verbmobil project funded by the German Ministry of Research and Technology and involving research groups in a number of German universities. The aim is to produce a prototype dialogue system for negotiations for German and Japanese business people with English as a common dialogue language. The system is based on language-independent disambiguated representations incorporating speech act (dialogue) information and involving domain knowledge databases. A major effort has concentrated on the analysis of dialogue in the chosen domain. Language Engineering projects Since the ending of Eurotra, research funds from the European Union have been granted on a wide range of projects within the broad field of language engineering, which includes multilingual tools of all kinds as well as translation assistance in various contexts. Practical implementation and collaboration with industrial partners is emphasised throughout, as well as the need for general-purpose and re-usable products. It is not possible to describe all those projects which involve multilinguality and translation. Only a few are highlighted. The ALEP project has been devoted to the development of a platform to support a wide range of research and technology activities related to natural language processing, including a general-purpose formalism and rule interpreter, and a text handling system. (The most recent version is described in MTNI#14.) GRAAL was a parallel project for a re-usable grammar-writing formalism for text processing, including for computer-aided translation. The development of lexical tools has been a major focus: GENELEX has defined a generic model for dictionary representations; DELIS is a tool to support lexicon building and management; MULTEXT is software for text corpora analysis and exploitation; and CRATER concentrates on bilingual corpus alignment. More specifically translation oriented is the OTELO project with members including SAP (Germany), Lotus Development (Ireland), CST (Denmark) and Logos (Germany). The aim is to design a comprehensive automated translator's environment which combines at a single interface a variety of programs including MT, translation memory and other translation tools; and which will allow access from outside for new and potential users to try out MT over networks. Some of the most recently approved projects are the following; which of them actually produce something worthwhile is yet to be seen. MABLe is to be a multilingual authoring system, guiding writers interactively to produce correspondence in a poorly known target language. MAY will provide multilingual access to yellow pages. MULTIMETEO is intended to generate weather forecasts in various languages from basic data supplied by meteorological computers. RECALL is to be a module for language learning which provides feedback translations for students. SPARKLE is a project to develop tools for syntactic analysis easily adapted to different languages and for semi-automatic lexical acquisition. SPEEDATA will provide continuous speech input for data entry in an Italian and German land registry. LINGUANET is to develop a multilingual communications system for police, based on experience with a controlled language Channel Tunnel (English and French) system. TREE will be provide multilingual access to a networked database of employment opportunities. TRADE is a project to translate social security reports in Italian, Spanish and English. As before, there are a number of lexicon projects, nearly all multilingual (e.g. EUROWORDNET, INTERVAL), and projects to support the compilation and exploitation of corpora (e.g. PAROLE, SPEECHDAT). In the latter respect an important development is the establishment of the European Language Resources Association (ELRA) for the identification, collection, classification, validation, and exploitation of language resources (spoken and written), corpora, and linguistic models. A number of European projects are tackling the automatic generation of natural language texts from databases. The APOLLO project is based on the CAT2 system mentioned earlier, with the aim of producing training documentation in French and English. DRAFTER is an experiment in the production of multilingual instruction documents for technical writers. Networking MT Europe was the location of one of the first networked MT service. This was the networking of Systran on the French Minitel system. Subsequently, other MT systems have become available over networks such as Internet: e.g. Logos, Metal and Globalink. Other efforts may be less familiar. TeleTaal is offering a PC-based word processing package with spelling and style checkers (in Dutch, French, German, Italian, and Spanish), which includes on-line support to electronic dictionaries and translation assistance through network links to the Globalink MT service. And there are experimental projects: MAITS is a EU-supported project to develop an interface to support access to MT services and translation memories, and TELELANG is investigating networked translation services (human translation as well as MT-based, terminology databases, linguistic resources, etc. Finally, mention should be made of a service from British Telecom which produces summaries of texts in a wide range of languages and not restricted by subject. NetSumm is a statistics-based program which takes as input relatively short documents and produces gists in the form of extracted sentences. It is said to work best with newspaper articles and formal reports. Evaluation With the attention turning in Europe increasingly to usability of multilingual and translation systems there is inevitably much concern with valid and appropriate evaluation methods. An important vehicle is the EAGLES working group set up for establishing criteria for the evaluation and assessment of language engineering tools (which Margaret King reported on in AMTA-94). So far it has not looked at MT as such, having concentrated as yet on grammar checkers and writing aids. Also important in this field is the TSNLP project (Essex University) which is seeking to establish test suites for natural language tools, including MT. And finally, we should remember that some of the most thorough examinations and evaluations of MT systems and translation aids have emanated from European organisations; most recently the two reports from Ovum Ltd. (London) on the global market and future prospects, and on the current translation technology available. Sources The sources of this survey are mainly conferences held in the last two years: particularly the MT Summit in Luxembourg in 1995, the Language Engineering Conventions in 1994(LEC 1994, the MT conference in Cranfield in 1995, and the Translating and the Computer conferences in 1995 (Aslib 1995), and the Sixth Theoretical and Methodological Issues in MT conference in Leuven, 1995(TMI 1995. Other sources are various reports in MT News International, particularly those on MT in Europe by Colin Brace (MTNI#12), Paul Hearn (MTNI#14), Dorothy Senez (MTNI#14) and Jörg Schütz (MTNI#15). Translating & the Computer 17. Papers from the Aslib conference held on 9th and 10th. Aslib, AslibLondonAslib (1995): Translating & the Computer 17. Papers from the Aslib conference held on 9th and 10th November 1995. London: Aslib, 1995. Proceedings of the First International Workshop on Controlled Language Applications: CLAW96. the First International Workshop on Controlled Language Applications: CLAW96Leuven, Belgium; LeuvenCLAW (1996): Proceedings of the First International Workshop on Controlled Language Applications: CLAW96, 26-27 March 1996, Leuven, Belgium. [Leuven, 1996] Cranfield, International Conference: Machine translation ten years on. LondonBritish Computer SocietyCranfield UniversityCranfield (1994): International Conference: Machine translation ten years on, 12-14 November 1994, Cranfield University. [London: British Computer Society, 1994] Translator's workbench: tools and terminology for translation and text processing. M Kugler, Research Reports ESPRIT. Springerproject 2315: TWB.Kugler, M. et al. (1995): Translator's workbench: tools and terminology for translation and text processing. (Research Reports ESPRIT, project 2315: TWB.) Berlin: Springer. Language Engineering Convention. LEC. CNIT, La DéfenseLEC (1994): Language Engineering Convention, CNIT, La Défense, Paris, July 6-7, 1994. Abstracts. [Edinburgh, 1994] Second Language Engineering Convention. LEC. Convention digestLEC (1995): Second Language Engineering Convention, Queen Elizabeth II Conference Centre, London, 16-18 October 1995. Convention digest. London, 1995 V Mt Summit, MT Summit V. Proceedings. LuxembourgLuxembourg: SEMA GroupMT Summit V (1995): MT Summit V. Proceedings, Luxembourg, July 10-13, 1995. [Luxembourg: SEMA Group, 1995] Machine translation and the lexicon. P Ed Steffens, Third International EAMT Workshop. SpringerSteffens, P. ed. (1995): Machine translation and the lexicon. Third International EAMT Workshop, Heidelberg, Germany, April 1993. Proceedings. Berlin: Springer, 1995. Proceedings of the Sixth International Conference on Theoretical and Methodological Issues in Machine Translation: TMI95. the Sixth International Conference on Theoretical and Methodological Issues in Machine Translation: TMI95Leuven, Belgium; LeuvenTMI (1995): Proceedings of the Sixth International Conference on Theoretical and Methodological Issues in Machine Translation: TMI95, July 5-7, 1995, Leuven, Belgium. [Leuven, 1995]

Parsing Chinese Synthetic Words with a Character-based Dependency Model

Synthetic word analysis is a potentially important but relatively unexplored problem in Chinese natural language processing. Two issues with the conventional pipeline methods involving word segmentation are (1) the lack of a common segmentation standard and (2) the poor segmentation performance on OOV words. These issues may be circumvented if we adopt the view of character-based parsing, providing both internal structures to synthetic words and global structure to sentences in a seamless fashion. However, the accuracy of synthetic word parsing is not yet satisfactory, due to the lack of research. In view of this, we propose and present experiments on several synthetic word parsers. Additionally, we demonstrate the usefulness of incorporating large unlabelled corpora and a dictionary for this task. Our parsers significantly outperform the baseline (a pipeline method).

Parsing Chinese Synthetic Words with a Character-based Dependency Model Fei Cheng fei-c@is.naist.jp Graduate School of Information Science Nara Institute of Science and Technology 8916-5, 630-0192Takayama, IkomaNaraJapan Kevin Duh kevinduh@is.naist.jp Graduate School of Information Science Nara Institute of Science and Technology 8916-5, 630-0192Takayama, IkomaNaraJapan Yuji Matsumoto Graduate School of Information Science Nara Institute of Science and Technology 8916-5, 630-0192Takayama, IkomaNaraJapan Parsing Chinese Synthetic Words with a Character-based Dependency Model parsinginternal structuresynthetic word Synthetic word analysis is a potentially important but relatively unexplored problem in Chinese natural language processing. Two issues with the conventional pipeline methods involving word segmentation are (1) the lack of a common segmentation standard and (2) the poor segmentation performance on OOV words. These issues may be circumvented if we adopt the view of character-based parsing, providing both internal structures to synthetic words and global structure to sentences in a seamless fashion. However, the accuracy of synthetic word parsing is not yet satisfactory, due to the lack of research. In view of this, we propose and present experiments on several synthetic word parsers. Additionally, we demonstrate the usefulness of incorporating large unlabelled corpora and a dictionary for this task. Our parsers significantly outperform the baseline (a pipeline method). Introduction Word segmentation is considered as the fundamental step in Chinese natural language processing, since Chinese has no spaces between words to indicate word boundaries. In recent years, research in Chinese word segmentation has progressed significantly, with state-of-the-art performing at around 97% in precision and recall (Xue and others, 2003;Zhang and Clark, 2007;Li and Sun, 2009). But there still remain two crucial issues. Issue 1: The lack of a common segmentation standard, due to the inherent difficulty in defining Chinese words, makes it difficult to share annotated resources. For instance, the synthetic word "中国国际广播电台" (China Radio International) is considered to be one word in the MSRA corpus. While in the PKU corpus, it is segmented as "中国 (China) / 国际(international) / 广播 (broadcast) / 电台 (station)". Our parser, however, can offer flexible segmentation level output, by analysing the internal structure of words ( Issue 2: Frequent out-of-vocabulary (OOV) words lower the accuracy of word segmentation. Chinese words can be highly productive. For instance, Penn Chinese Treebank 6.0 1 does not contain the word "成功者" (one that succeeds), even though the word "成功" (succeed) and "者" (person) appear hundreds of times. Li and Zhou (2012) defined such cases as pseudo-OOVs (i.e. words that are OOV but consisting of frequent internal parts) and estimated that over 60% of OOVs are pseudo OOVs in five common Chinese corpora. Goh et al. (2006) also claimed that most OOVs are proper nouns taking the form of Chinese synthetic words. These previous works suggest that analysing internal structures of Chinese synthetic words has the potential to improve the OOV problem. Both issues can be better handled if we knew the internal information of Chinese words. We believe that parsing internal structures of Chinese synthetic words is an overlooked but potentially important task, which can benefit other Chinese NLP tasks. However, correctly parsing Chinese synthetic words is challenging, not only because word segmentation step exists, but also for the reason that standard part-of-speech (POS) tags provide limited information. For instance, "中 国 NN / 国际 NN / 广播 NN / 电台 NN" contains a sequence of identical NN tags, giving little clue about their internal branching structure. Our work is concerned with parsing Chinese synthetic words into a parse tree without replying on POS tagging. In this paper, we first introduce the classification of Chinese words in Section 2. Then we explain our annotation work and standard in Section 3. Section 4 describes the two types of character-based dependency models. The experiment setting and the comparison between different parsers and features are described in Section 5. Section 6 describes the recent related work. Finally we make the conclusion in Section 7. Definition of Word in Chinese It is generally considered that in Chinese, there isn't a clear notion of word, unlike character. However, for native Chinese speakers, a word is a lexical entry, representing a whole meaning. We adopt the classification of Chinese word proposed by (Lu et al., 2008), which divided Chinese words into the following two types. Single-morpheme word: These words only have one morpheme inside them and cannot be segmented further. It means that the meanings of the individual parts do not indicate the meaning of the original word. The following are three subtypes of single-morpheme words. Even if we don't know the word "总工程师", we can guess the meaning as 'chief engineer', based on the meanings of its internal parts. In this paper, we treat synthetic words as our parsing target. The single-morpheme words are the smallest units (leaves) in our tree structure representation. Annotation A major challenge for synthetic word analysis is the lack of available annotated data. Therefore, we decided to annotate internal structures of Chinese synthetic words ourselves. We adopt a lexicon management system named Cradle (Lu, 2011) to annotate and represent tree structures. Annotation Standard We establish the following annotation standard based on the Chinese word definition given in Section 2. • Determine whether the target word is a synthetic word or not. If it is a single-morpheme word, the annotator skips to the next word. • Split the target word into parts on each level from top to bottom. • Stop annotating until that all the split parts are singlemorpheme words. Annotation Data The article titles of the Chinese Wikipedia is a rich resource of Chinese synthetic words. There are 826,557 article titles in our 2012 crawl of the Chinese Wikipedia. According to our annotation standard, four students randomly annotated 10,000 words 2 with the length distribution shown in Table 1. Each student's annotation is checked and revised by another student. Length Number of words 4 2292 5 1838 6 1516 7 1433 ≥ 8 2922 Table 1: The Character Length Distribution of the Annotated Words. We exclude data with less than 4 characters because two or three character words contain very limited structure types. For investigating the quality of our annotation, we required two of the students to annotate additional 200 words. We evaluate the annotation agreement in two levels. They first do word segmentation on the input words. Secondly, they annotate brackets on the gold segmented words. The Kappa-coefficient on the word boundary between characters in the first step is 0.947. The Kappa-coefficient on matching the brackets is 0.921. Character-based Dependency Model We now describe a character-based dependency model for predicting internal word structure. This model allows joint word segmentation and internal structure parsing. First, we introduce a label set ( Although Chinese is a character-based language, it's ambiguous to decide the dependency direction between two characters inside the single-morpheme words. However, the majority of the dependency between parts in the Chinese synthetic words are left arcs. For instance, the synthetic word "工程师" (Engineer) is composed by "工程" (Engineering) and "师" (master) with a left arc. But we can hardly recognize the semantic head character inside the single-morpheme word "工程". In this paper, we do not distinguish the semantic modifier and head. For any dependency between two characters, we specify the right character as the head, which means only left arcs from the head to the modifier exist in our representation. More types of semantic relation and dependency relation information annotation is conceivable and we leave it as future work. Before discussing specific examples, we define two types of morphological structures. Branching is the most common morphological process in Chinese. The branching structures of a three character word 'ABC' can be enumerated as 'AB + C', 'A + BC' and 'A + B + C'. Merging is another language phenomenon in Chinese. It means two semantically related words, which have an internal part in common, can merge into one word by removing one of the common parts. For instance, a word 'ABC' can be composed by 'AB + AC' sharing the common 'A'. For demonstrating the usage of this label set, we present all possible structure types of three-character synthetic words into our character-based dependency representation (Figure 3). Three branching structure examples are listed in Figure 3a. "副总统" (vice president) is a synthetic word composed by two single-morpheme words "副" (vice) and "总统" (president). "联系人" (contact person) is a synthetic word composed by two single-morpheme words "联 系" (contact) and "人" (person). "中日韩" (China, Japan and Korea) is composed by three single-morpheme words "中", "日" and "韩" with coordinate relation between characters. We can also present the 'Merging' type by our representation ( Figure 3b). The example "动植物" (animal and vegetation) is consisted by two single-morpheme words "动物" (animal) and "植物" (vegetation) sharing the common right character "物" (object). The example "进出口" (import and export) is consisted by two single-morpheme words "进 口" (import) and "出 口" (export) sharing the common left character "口" (port). The example "干电池" (dry cell) is consisted by two single-morpheme words "干 电" (dry power) and "电池" (battery) sharing the common middle character "电" (electricity). The internal structure of a long synthetic word 'Olympic Games' can be represented as a character-level dependency tree as shown in Figure 4. "奥林匹克" with the labels 'WB', 'WI' and 'WI' represent a single-morpheme transliterated word of 'Olympic'. "运动会" (sports competition) Figure 4: The Character-level Dependency Tree of a Long Synthetic Word. 副 总 统 B WB 联 系 人 WB B 中 日 韩 C C (a) Branching Structure 动 植 物 WB WB 进 出 口 WB C 干 电 池 WB WB (b) Merging Structure奥 林 匹 克 运 动 会 WB WI WI B WB B is composed by two single-morpheme word "运动" (sports) and "会" (competition). There is a branching relation between "奥林匹克" and "运动会". Transition-based Parser The transition-based parser is a step-wise approach to dependency parsing. In each step, the discriminative classifier uses a number of context features to check a node pair, the top node S 0 of a stack and the first node Q 0 in a queue (unprocessed sequence) to determine if a dependency should be established between them. Since only left arcs exist in our dependency representation, two actions are defined as follows: Left-arc: Add an arc from Q 0 to S 0 and pop S 0 from the stack. Shift: Push Q 0 into the stack. The implementation of the transition-based model adopted in this work is the MaltParser (Malt) (Nivre et al., 2006), which uses support vector machines to learn transition actions. Graph-based Parser Graph-based dependency parser defines the score of a dependency graph as the sum of the scores of all the arcs s(i, j, l) it contains. Here, s(i, j, l) is the arc between words i and j with label l. This problem is equivalent to finding the highest scoring directed spanning tree in the complete graph over the input sentence. It is represented by: G = arg max G=(V,A) (i,j,l)∈A s(i, j, l)(1) Second order sibling factorization (2nd-order) showed the significant improvement compared to first order parsing (McDonald, 2006;Carreras, 2007). The implementation of the graph-based model adopted in this work is the MSTParser (MST) (McDonald, 2006), which uses standard structured learning techniques, globally setting parameters to maximize parsing performance on the training set. Extra Features As we mentioned, POS information is not sufficient as features for this task. Therefore we improve our parsers by incorporating features extracted from a large-scale corpus and a dictionary. Dictionary feature: If a context character sequence exists in the NAIST Chinese dictionary 3 (with 129,560 entries), our parsers use its existing POS tags as features. It is possible for one word to correspond to multiple POS tags in the dictionary. The example entries in the dictionary are listed: "稳定,22,22,2906,NN,*,*,稳定" "稳定,44,44,3068,VV,*,*,稳定" Brown cluster feature: Koo et al. (2008) trained a dependency parser in English and Czech and used Brown clusters (Brown et al., 1992) as an additional feature. We use CRF++ 4 to implement a CRF-based model (Zhao et al., 2006) to do word segmentation on the Chinese Giga-word second edition 5 . Then we conduct a word-level Brown clustering on the segmented corpus. If a context character sequence exists in the word list of the segmented corpus, its corresponding cluster id is used as a feature. Here, we demonstrate the extra features of a node in the following synthetic word: 中 国 国 际 广 播 电 台 Q −4 Q −3 Q −2 Q −1 Q 0 Q 1 Q 2 Q 3 For the node Q 0 ("广"), we search the context character se- quences Q 0 Q 1 ("广播"), Q −1 Q 0 ("际广"), Q 0 Q 1 Q 2 ("广 播电"), Q −1 Q 0 Q 1 ("际广播"), Q −2 Q −1 Q 0 ("国际广"), Q 0 Q 1 Q 2 Q 3 ("广播电台"), Q −1 Q 0 Q 1 Q 2 ("际广播电"), Q −2 Q −1 Q 0 Q 1 ("国际广播"), Q −3 Q −2 Q −1 Q 0 ("国国际 广" ) (with a four-character window) in the Brown clustering results and the NAIST Chinese dictionary. Then we use the corresponding clustering ids and POS tags of all these context character strings as the features of the node Q 0 . Experiments Setting Since we have a small size data with 10,000 annotated words, we use a 5-fold cross-validation to evaluate parsing performance. In each round, we split the data and used 80% of it as training set and the remaining 20% as testing set. For parameters tuning, we split the training set and used 80% of it as sub-training set and 20% as development set. We adopt the feature templates in Table 3 for our Malt parser and add similar character sequence features into our MST and 2nd-order MST parsers. We found that the parsers reach the highest performance on the development set when the Brown cluster number is equal to 100. As baseline, we implement a pipeline method which first uses our CRF-based model (Section 4.3.) to perform word segmentation, then uses MaltParser with Nivre arc-eager default feature 6 to perform word-level parsing. For the comparison to our character-based dependency parsers, we convert the word-level parsing results of the baseline to character-level based on our character-level morphological relation labels defined in denote the top and second characters in the stack. i 0 , i 1 denote the first, second characters in the queue. dep, h and lc denote the dependency label, head and leftmost child of a character. cseq denotes a character sequence started from a given character. s 0 .dep means dependency label of s 0 . + denotes the combination of two or more features. Results In this section, we present the final results of our parsers and compare them to the baseline (Table 4). The evaluation metric of CoNLL 2006 shared task 7 is adopted, which includes unlabelled attachment score (UAS), unlabelled complete match (UCM), labelled attachment score (LAS) and labelled complete match (LCM). Note that we are parsing on synthetic words from Wikipedia titles (not sentences), so complete match refers to accuracy on these titles. The average length of titles is 7.04 characters. percentage points in LAS for MST. It is thus clear that all the models have the capacity to learn from these features. The results demonstrate three points: (1) Our characterbased dependency parsers outperform the baseline (the pipeline method). (2) Features extracted from a largescale corpus and a dictionary can significantly improve parsing performance. (3) Graph-based parsers outperform transition-based parsers for this task. Figure 5 show LAS relative to word length of our characterbased parsers. As expected, MST parsers outperform Malt over the entire range of word lengths. By incorporating extra features, Malt shows a strong tendency to improve LAS even outperforming the 2nd-order MST on the range of short word length. Graph-based MST and 2nd-order MST still keep higher LAS on the word length equal to or longer than 7 characters. All of the three models show very consistent increase in LAS on all word lengths compared to their base models. As expected, Malt, MST and 2nd-order MST finally reach similar LAS for short words and Malt degrade more rapidly with increasing word length because of error propagation (McDonald and Nivre, 2007). UAS UCM LAS Analysis Related work Recently, some work on using the internal structure of words to improve Chinese process show promising results on different tasks. Li (2011) claimed the importance of word structures. They proposed a new paradigm for Chinese word segmentation in which not only flat word structures were identified but with internal structures were also parsed in a sentence. They aimed to integrate word structure information to improve the performance of word segmentation, parsing or other NLP tasks on sentences. Zhang et al. (2013) manually annotated the structures of 37,382 words, which cover the entire CTB5. They build a shiftreduce parser to jointly perform word segmentation, Partof-speech tagging and phrase-structure parsing. Their system significantly outperform the state-of-art word-based pipeline methods on CTB5 test. Our character-based word parsing model is inspired by the work of (Lu et al., 2008;Zhao, 2009). Lu et al. (2008) describe the semantic relations between characters. They proposed a structure analysis model for three-character Chinese words. Zhao (2009) presented a character-level unlabelled dependency scheme as an alternative to linear representation of sentences for word segmentation task. Their results demonstrate that the character-dependency framework can obtain comparable performance compared to the state-of-art word segmentation models. Our work extends previous works, focusing on parsing long words using various character-based dependency models. In addition, we extract the features from a large unlabelled corpus and a dictionary to improve our models. Our character-based parsing model for Chinese synthetic words can also help transform existing annotated Chinese corpora to give more fine-grained and consistent segmentations. For instance, the previous example "中国国际广播电台" and "中央 / 广 播 / 电台" are inconsistently annotated in the corpus. Parsing the word into "中国 / 国际 / 广播 / 电台" can make the corpus more consistent to benefit further NLP process. Conclusion In this paper, we claim that synthetic word parsing is an important but overlooked problem in Chinese NLP. Our first contribution is that we annotated 10,000 long Chinese synthetic words, which is potentially useful to other Chinese NLP tasks. The data is to be distributed as freely available data. Our second contribution is an in-depth comparison of various parsing frameworks and features. The results show that large unlabelled corpora and a dictionary can be extremely helpful in improving parsing performance. We believe that this is a first-step toward a more robust character-based processing of Chinese that does not require explicit word segmentation. As next work, we plan to include real syntactic dependency (subject-verb, verb-object or modifier-head) into our representation. We also plan to extend the algorithm and evaluation to the sentence-level and consider applications (such as Machine Translation, Information Retrieval) that may benefit from internal structure analysis of synthetic words. Figure 1 : 1The Internal Structure of a Word •Figure 2 : 2One-character single-morpheme word: 人(human), 睡(sleep), 热(hot) • Multi-character single-morpheme word: 鹌鹑(quail), 鸳鸯(mandarin duck) • Transliterated word: Those words are translated from foreign words based on the pronunciations. 麦当劳(McDonald's), 瓦伦西亚(Valencia)Synthetic word: These words are composed of two or more single-morpheme words and represent a new meaning which can be indicated from the internal constituents.The following is the internal structure of the synthetic word 总 / 工程 / 师 (chief engineer)The Internal Structure of a Synthetic Word Figure 3 : 3The Character-based Dependency Trees of Three-character Synthetic Words Figure 5 : 5LAS Relative to Word Length, where '*' denotes that the parser incorporates the extra features. Table 2 ) 2to represent the morphological relations between two characters.Label Dependency relation B Branching relation C Coordinate relation WB Beginning inside a single-morpheme word WI Other part inside a single-morpheme word Table 2 : 2Character-level Morphological Relation Table 4 ( 4Section 4.) 3 http://cl.naist.jp/index.php?%B8%F8%B3%AB%A5%EA% A5%BD%A1%BC%A5%B9%2FNCD. 4 http://crfpp.googlecode.com/svn/trunk/doc/index.html?sourc e=navbar 5 http://catalog.ldc.upenn.edu/LDC2005T14 6 http://www.maltparser.org/userguide.html#featurespec Table 3 : 3Feature Templates for the Malt Parser. s 0 , s −1 Table 4 : 4Final Parsing Results. 'feats' denote that the extra features are incorporated into the model. In the upper part of Table 4, all the character-based dependency parsers highly outperform the baseline without using any extra features mentioned in Section 4.3. Graph-based MST and 2nd-order MST show obvious advantage on UAS, UCM and LAS and transition-based Malt gets the highest LCM. In the second part, we incorporate extra features into parsing. Malt starts to reach comparable performance to MST and gets the highest LCM score 70.86. 2nd-order MST still leads the highest UAS, UCM and LAS scores. The extra features offer the improvement of 1.8 percentage points in UAS and 2.6 percentage points in LAS for Malt and 1.8 http://catalog.ldc.upenn.edu/LDC2007T36 In this paper,we skipped all domain-specific words such as technical terms, judged by the annotators. http://ilk.uvt.nl/conll/ Class-based n-gram models of natural language. Peter F Brown, Peter V Desouza, Robert L Mercer, Pietra, J Vincent, Della, Jenifer C Lai, Computational linguistics. 184Brown, Peter F, Desouza, Peter V, Mercer, Robert L, Pietra, Vincent J Della, and Lai, Jenifer C. (1992). Class-based n-gram models of natural language. Computational lin- guistics, 18(4):467-479. 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A Confidence Index for Machine Translation

We argue that it is useful for a machine translation system to be able to provide the user with an estimate of the translation quality for each sentence. This makes it possible for bad translations to be filtered out before post-editing, to be highlighted by the user interface, or to cause an interactive system to ask for a rephrasing. A system providing such an estimate is described, and examples from its practical application to an MT system are given.

A Confidence Index for Machine Translation August 1999 Arendse Bernth arendse@watson.ibm.com IBM T.J. Watson Research Center P.O. Box 70410598Yorktown HeightsNYUSA A Confidence Index for Machine Translation TMI 8th International Conference on Theoretical and Methodological Issues in Machine TranslationChester, UK99August 1999 We argue that it is useful for a machine translation system to be able to provide the user with an estimate of the translation quality for each sentence. This makes it possible for bad translations to be filtered out before post-editing, to be highlighted by the user interface, or to cause an interactive system to ask for a rephrasing. A system providing such an estimate is described, and examples from its practical application to an MT system are given. Introduction High-quality machine translation (MT) is highly desirable in today's global community and is the goal of many computational systems. Unfortunately, natural languages are very complex, and this poses great challenges for any MT system. No MT system today is able to produce perfect translations of arbitrary text. For any given system, translations range from perfect to unintelligible. In order to guarantee high quality, some systems require that the source text be constrained more or less severely. Not only does this place a considerable burden on the author, but it also means that documents that are not specially prepared cannot be handled. Some of these combinations of Controlled Language Checker and MT system require strict conformance to the Controlled Language, e.g. the KANT system (Mitamura & Nyberg 1995;Nyberg & Mitamura 1996;Hayes et al. 1996;Kamprath et al. 1998), and the CASL system (Means & Godden 1996). Other systems, e.g. EasyEnglish (Bernth 1997;Bernth 1998a;Bernth 1998b), help the writer prepare a document for machine translation by pointing out hard-to-translate constructions without enforcing strict control. But systems like EasyEnglish neither guarantee a perfect translation nor give an indication of how well the source text would translate. Controlling the source language certainly helps the quality of translation; however, for many applications, e.g. on-the-fly translation of random Web pages , it is not possible to constrain the source text. This means that the user will be subjected to bad translations as well as to good translations, without any indication of how good the translation of any given segment may be. Bad translations cause a high degree of frustration for the user, because the user has no way of avoiding the garbled-up nonsense that is produced occasionally by even the best MT systems. It appears to be an unfortunate fact of life that MT gets a bad reputation from translations that are bad rather than a good reputation from all the translations that actually are useful. If the user could know that the translation was likely to be bad, the user would have the choice not to look at it. In other words, if the MT system could provide the user with an indication of the probable accuracy of the translation, it would be up to the user to decide to look at it or not. Previous systems such as the Logos Translatability Index (TI) assign a measure of the translatability of a complete document by the LOGOS system. The Logos Translatability Index was not expected to "provide sentence-specific information with any degree of reliability. The TI applies to the corpus or document as a whole but is not useful in pinpointing problem sentences." (Gdaniec 1994). In this paper we describe the Translation Confidence Index (TCI), which is designed to provide the user with a measure of the MT system's own confidence in its translation, segment for segment. The TCI engine associates a number, the TCI, between 0 and 100 (inclusive) to each segment. A TCI of 100 expresses perfect confidence in the translation, whereas a TCI of 0 expresses zero confidence. The TCI engine, which works with a transfer-based MT system, is fully implemented, and it has been integrated with the LMT machine translation system (McCord 1985(McCord , 1989a(McCord , 1989b(McCord , 1989cBernth & McCord 1991;Gdaniec 1998). In Section 2 we look at how the TCI can be used. Section 3 describes the basic ideas of the TCI. In Section 4 we describe the role of choices in the translation process for calculating the TCI. Section 5 gives an overview of the types of problems that contribute to the TCI. Section 6 describes tuning the TCI for a specific language pair. In Section 7 we look at the language pair profile, and in Section 8 we show some practical results for LMT English-German. Modes of Use for the TCI In this section we describe different ways the TCI can be used in different translation contexts. The TCI can be used by the MT system's interface in various ways. In one scenario, the user will have decided on a threshold based on personal preferences, and the system simply will not show translations where the TCI falls below this threshold. This scenario is particularly useful in the context of professional translators using a translation workbench. A professional translator is apt to become both annoyed and insulted by bad translations. Furthermore it may also be harder to post-edit a bad translation than to start from scratch. In any case it makes sense to protect the professional translator from bad translations produced by the MT system. In another scenario, the interface could pass on all of the translations, regardless of the TCI, but could indicate the TCI either by giving the specific number or by marking bad translations in red for example. This would be useful for the casual user, who does not know the source language. This type of user would then have the information to take the bad translations with a grain of salt. In the context of interactive translation, the TCI can be used to provide immediate feedback so that users can rephrase the input if the TCI is below the given threshold. This can be used for both text-to-text translation, where the user types the text of the source document and has it translated on-the-fly, and for speech-to-speech translation, where the system may ask the user to repeat or rephrase the input. The TCI: A Measure of Complexity The overall idea behind the design and implementation of the TCI is to measure the complexity of the translation process. As the complexity increases, the confidence decreases. The complexity depends on three major factors: The choices coded in the MT system that are encountered during the various steps of the translation process, the complexity of the source text, and how each of these two factors affects the translation for a given language pair. In the following sections we shall address these points. Generally, the TCI for a segment will be computed by assigning penalties to different kinds of complexities that could create potential problems. Penalties are integers, with a larger number representing a worse problem. The penalties for a segment S are added together to obtain the total penalty P for S. P should thus be viewed as the accumulation of potential problems, small or large, rather than as an indication of one specific large problem. Most potential problems would not be big enough to lower the TCI substantially, if they occur in isolation, but taken together, they may be a good indication that this is a problematic segment. Ambiguity in part of speech between nouns and verbs, for example, may not be a problem in itself, but combined with a short segment length (as well as other factors like the presence or absence of determiners) it can signal a degree of uncertainty that should be penalized. Given the total penalty P for the segment S, the TCI of S is then computed as TCI(S) = max(100 -P, 0). In other words, we subtract the total penalty from 100 to get the TCI, but we do not bother with scores that are less than zero, because they will certainly represent unusable translations. Obviously, the impact on the translation quality of any given type of problem will be specific to the language pair you are translating between. For this reason, the TCI makes use of language pair profiles (described in Section 7) where penalties can be set for each language pair. Choices in the Translation Process In a computational system where heuristic choices are made, each such choice introduces a potential for a mistake. Such mistakes can be of one of the following types: 1. The choices include the correct choice, but an incorrect choice is made. The choices do not include the correct choice, and an incorrect choice is inevitable. This typically reflects either "direct" lack of information (e.g. missing lexical information or missing grammar rules) or "indirect" lack of information caused by a wrong choice earlier in the process (e.g. some analysis was incorrectly pruned away). The second type of mistake is particularly difficult to handle, except indirectly as it may be reflected in mistakes of the first type; and we shall restrict our treatment to mistakes of the first type. No attempt is made to evaluate the choice made by the MT system. Rather, the number of potential choices at any given choice point contributes to the penalty (the more choices there are, the higher the probability of not making the correct choice). Also the type of choice affects the score. This is expressed in the penalties stated in the language pair profile. Monitoring the Translation Process A transfer system consists of three distinct phases that all may introduce mistakes: Source analysis, transfer, and target generation. Hooks into the LMT system at crucial points during these phases call routines that look for the various types of problems, assign penalties, and calculate intermediate values for the TCI. An option is to abandon the translation process for a given segment if the TCI falls below a specified threshold. This saves processing time in the scenario where the user is not interested at all in translations whose TCI falls below the threshold. In this section we give an overview of the types of problems that we look for during each phase of the translation process. Source Analysis The earlier in the translation process a problem occurs, the more impact it is likely to have, because the problem is likely to propagate and affect all subsequent steps. Thus problems in source analysis are given relatively higher penalties in the TCI. Due to the importance of the quality of the source analysis in producing a good translation, we are devoting a separate paper to this issue (Bernth & McCord 1999); here we give a brief overview of the major issues. The most evident problem is a sentence that cannot be given a complete parse. The parser used with LMT, the ESG parser (McCord 1980(McCord , 1990(McCord , 1993, produces in these cases a pieced-together version of a failed parse. But other, more subtle, things may go wrong during source analysis, e.g. segmentation, lexical choices, syntactic analysis, and various ambiguous or otherwise hard-to-parse constructions. One of the most serious segmentation problems is caused by footnotes, because people are not consistent in the way they use footnotes; the role of footnotes in the sentence is far from unambiguous. They may be separate segments or actual parts of the sentence. Parts of speech can be very ambiguous in short segments of one to four words. In longer segments, the context often disambiguates the part of speech. Certain constructions or words are known to increase the likelihood of a bad parse and/or translation. The most obvious case is of words that are not found in the lexicon. However, many other characteristics of the source text may cause problems. Consider e.g. the following non-exhaustive list: Occurrences of coordination may be hard to parse correctly. Missing subjects in the sentence may make it problematic to get correct subject-verb agreement in the target. All structural ambiguities, e.g. double passives, nonfinite verbs, and prepositional phrases entail a risk of incorrect attachment. Time references like next year, which may may be either adverbs or nouns, may affect the parse. Transfer Problems in transfer may stem from wrong source analysis as well as from inadequate lexical information (this includes missing semantic types and domain specifications) and wrong application (or non-application) of transformations. In addition, mixed domains in the document can be a problem. Problems during transfer fall into two different categories: 1. Lexical Transfer. Restructuring Transfer. For lexical transfer, the most glaring problem is lack of transfer for a given source word. This is a subcategory of a more general problem, viz. mistakes in the transfer lexicon. The more complex the entry, the more likely it is that the desired transfer is actually in the lexicon, but also the greater the chance that a mistake was made in creating the entry. Informal empirical studies show the latter to be a factor that should not be discounted, so we count the number of transfer elements. On the other hand, if the transfer was found in a specialized lexicon, we increase the confidence. This is done by giving this "problem" a negative penalty, which is equivalent to a reward. During restructuring transfer, the problems may arise from application of certain transformations that are known "troublemakers". The language pair profile allows the transformation writer to specify the names of these transformations and assign suitable penalties. Some transformations may also be known as real "life savers", and they can be given negative penalties in the profile. Another source of problems during restructuring transfer is transformations that apply partially; i.e. the transformations manage to make some changes in the tree structure, but they fail at a later point and do not succeed completely. This reflects mistakes in the transformations and is penalized accordingly. Target Morphological Generation Problems in this area are very insignificant, since target morphology in itself is a rather well-defined and limited area. Any problems axe likely to have been propagated from previous steps, particularly steps that assign features to words. However, in highly inflected parts of speech, a wrong feature stemming from an earlier step is likely to cause a certain amount of bad inflection, so this needs to be taken into consideration. The morphology writer has the possibility of specifying a small number of highly inflected parts of speech in the language pair profile, and whenever one of these parts of speech is encountered, the specified penalty will apply. Production Mode and Tuning Mode The TCI engine is part of the translation shell. In addition to using the engine, it is necessary to specify the penalties in the language pair profile. The purpose of production mode is simply to use the TCI to control the output of the MT system. However, the purpose of tuning mode is to tune the system to give the most accurate indication of the translation quality by setting the optimal penalties in the profile. In tuning mode, the system creates an output file that shows the TCI for each segment as well as all the individual penalties that the TCI is made up of. This analysis file is interfaced to a text editor, and the language specialist can experiment with the various penalties. The penalties assigned to the various types of potential problems reflect the current state of the MT system; hence, the TCI must be tuned every now and then as the MT system is improved, even though of course the impact of certain problems like incomplete parses is likely to remain constant over time. The process of tuning in itself provides valuable feedback to the developers about the weaknesses of the MT system. The Language Pair Profile The TCI language pair profile allows the user to set the penalties for each problem type. The problem types are identified by a code, e.g. nonfinite for ambiguous nonfinite constructions. In addition, it is possible to specify names of transformations and highly inflected parts of speech. The profile is a simple ASCII file with lines of the following form: code1 = valuel code2 = value2 where each problem type codei is assigned a penalty valuej. This profile is read in by the system and used in the calculation of the TCI for translations for a specific language pair. Every time a specific type of problem or choice is encountered, the relevant penalty is applied either as-is, or with a weight, depending on the specific problem and its context. Practical Results We have successfully integrated the TCI with LMT and tuned it for English-to-German translation. Of course the threshold below which a translation is considered unuseful is a matter of context and personal taste. But we have found with our current profile that there is a distinct separation into good and bad translations around a TCI of 65-70. Assuming a threshold of 70, the TCI divides the output of LMT English-German into reasonable translations and bad translations with a precision that turned out to be 72%. We expect to be able to improve this precision some by further tuning. Here are some examples of output from LMT English-German, where the TCI is stated in the beginning of the translation. Let us first look at some examples of bad translations, indicated by a low TCI, as in (1). (1) Instead of selling platforms, IBM can now focus on selling "best-fit" server solutions into its target, corporate-wide solution markets. ⇒ 34-85: Anstatt Plattformen zu verkaufen, kann die IBM darauf jetzt zielen, "am besten passende" Serverlösungen in sein Ziel zu verkaufen, unternehmensweite Lösung vermarktet. The play being over, we went home. ⇒ 16.80: Der spielen Sie Wesen, wir gingen nach Hause. In the first example, the parse of corporate-wide solution markets is wrong; corporatewide solution is taken as a noun and hence the subject, while markets is taken as a finite verb. This makes total nonsense of the translation. In the second example, the parse is incomplete, with being as a noun and play as a verb. This is reflected in the translation. Some examples of better translations, indicated by a higher TCI, are given in (2). (2) These include seven high-growth areas, which it clusters into the following three Conclusion We have argued that -given the state of the art of MT -it is very useful for any MT system to supply the user with an indication of the quality of translation output on a segment basis. Such a measure can be based on the general idea of monitoring choice points and noticing problematic constructions in the source text, and relating the impact of these to the language pair in a given translation process. 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School of Computer Science and Technology Soochow University Suzhou, Jiangsu 融 融 融合 合 合目 目 目标 标 标端 端 端句 句 句法 法 法的 的 的AMR-to-Text生 生 生成 成 成 朱 朱 朱杰 杰 杰, 李 李 李军 军 军辉 辉 辉 苏州大学, 计算机科学与技术学院/ 江苏省苏州市 图 1. "It begins and ends with Romneycare ." 抽象成AMR图的一个例子 抽象语义表示(Abstract Meaning Representation，简称AMR)(Banarescu et al., 2013)是 一种新型的语义表示方法，它是从文本中抽象出来捕捉核心的"谁对谁做了什么"的语义结 构，形式上是一种单根有向无环图的结构。图 1给出了一个AMR图示例，它是由句子"It begins and ends with Romneycare ."抽象而成的。文本中的实词被抽象成AMR图中的概念节 点(concept)，如图中"begin-01"和"thing"等节点称作为概念。概念之间的相互关系则被抽 象为边(edge)，表示两个概念之间存在的语义关系，比如":ARG0"和":op1"等。AMR图在语 义表示中已经得到了广泛的应用，并且在机器翻译 (Tamchyna et al., 2015)，问答系统 (Mitra and Baral, 2016)，事件抽取 (Li et al., 2015)等自然语言处理相关任务也得到了实践。与此同 时，AMR-to-Text生成在近年来也受到了越来越多的关注。 AMR-to-Text生成是在给定AMR图的条件下，自动生成相同语义的文本。该任务现存的一 些方法 (Flanigan et al., 2016;Konstas et al., 2017;Song et al., 2016;Song et al., 2018;Beck et al., 2018;Damonte and Cohen, 2019;Zhu et al., 2019)都着重在考虑如何对图关系进行建模，从 而忽略了生成时存在的句法约束。 最初的工作是采用基于统计的方法 (Pourdamghani et al., 2016;Song et al., 2017;Flanigan et al., 2016)，随后Konstas (2017) ( 将该任务引入到了序列到序列(sequence-to-sequence， 简 称S2S) 模 型 上 ， 使 用 双 向 长 短 时 记 忆 网 络 (Bi-LSTM) 进 行 编 码 。 但 是S2S模 型 需 要 将AMR图进行序列化去适应模型的输入，这样会损失大量的图结构信息。因此，为了更好的对 图 关 系 进 行 建 模 ，Beck等 原始句子： It begins and ends with Romneycare . 原始AMR图： (a / and :op1 (b / begin-01 :ARG1 (i / it) :ARG2 (t / thing :wiki "Massachusetts_health_care_reform" :name (n / name :op1 "Romneycare"))) :op2(e / end-01 :ARG1 i :ARG2 t)) 线性化之后的AMR图： and :op1 ( begin :arg1 it :arg2 ( thing :name ( name :op1 romneycare ) ) ) :op2 ( end :arg1 it :arg2 thing ) 图 2. 一个AMR图线性化示例 2 相 相 相关 关 关工 工 工作 作 作 目前AMR-to-Text生成的任务大致可以分为两类：基于统计的方法和基于神经网络的方 法。而基于神经网络的方法，现在又可以分为seq2seq和graph2seq两类。 2.1 基 基 基于 于 于统 统 统计 计 计的 的 的方 方 方法 法 法 早 期 神 经 网 络 未 普 及 时 候 ， 在AMR-to-Text生 成 上 的 工 作 大 都 使 用 基 于 统 计 的 方 法。Flanigan等(2016)将AMR图转换为合适的生成树，并应用树-串(tree-to-string)转换器 生成文本。Song等(2016)将一个AMR图拆分成了许多小的片段，并生成所有片段的翻译，最终 通过采用非对称广义旅行商问题解法来对片段确定其顺序。Song等(2017)使用同步节点替换语 法来对AMR图进行解析，并生成相应的句子。Pourdamghani等(2016)采用基于短语的机器翻译 模型来对线性化AMR图进行建模。 2.2 基 基 基于 于 于神 神 神经 经 经网 网 网络 络 络的 的 的方 方 方法 法 法 随着神经网络的兴起，最近的研究都是使用神经网络来生成。在Sutskever等(2014)证明 了深度神经网络的优越性之后，Konstas等(2017)提出使用序列到序列(S2S)模型来生成文 本，利用双向LSTM来对线性化的AMR图进行编码。为了限制生成的文本具有更合理的句 法，Cao等(2019)将AMR-to-Text生成的任务拆分成两个步骤，先使用句法模型去预测最优的目 标端句法结构，再利用预测的句法信息去辅助生成模型更好的生成句子。但是也相应的损失了 深度神经网络端到端的特性，并且和本文方法相比更加复杂，增加了网络的复杂度和参数。 随后，为了解决seq2seq模型将AMR图线性化之后信息损失的问题，大家的研究热点都 着重在研究图神经网络上。图到序列(Graph-to-Sequence)模型常优于序列到序列(S2S) 模型，包括图状态LSTM(2018),GGNN(2018)等。图状态LSTM通过每步的迭代交换相邻节点 的信息来更新节点。同时也对每个节点增加一个向量单元保存历史信息。GGNN是一个基 于门控的图神经网络，将AMR图结构完整的融入模型中，并且将边信息也转化为节点，解 决了参数爆炸问题的同时，也给了解码器更丰富的信息。着重于解决AMR图中重入节点 的问题，Damonte等(2019)提出了一种堆栈式的编码器，由图卷积神经网络和双向LSTM堆 栈而成。Guo等(2019)提出了一种深度连接图卷积网络(GCN)更好的获取局部与非局部信 息。Zhu等(2019) 在Transformer的基础上，受到(Shaw et al., 2018)对相对位置建模的启发，提 出了一种Structure-Aware Self-Attention的编码方法可以对图结构中任意两两节点进行完整的 建模(不论节点之间是否直接相连)，在该任务上取得了最高的性能。 本文采用了两种方法作为基准模型(Baseline)。 1. Transformer，最先进的seq2seq模型，最初使用于神经机器翻译和句法分析任务(Vaswani et al., 2017)。 2. Zhu等(2019)提出的Structure-Aware Self-Attention模型，目前在AMR-to-Text生成的任务上 取得了最高的性能。 3.1 基 基 基准 准 准模 模 模型 型 型1( ( (Baseline1) ) ) 3.1.1 Trasnformer Baseline1是使用的Transformer模型，它采用了编码器-解码器(Encoder-Decoder)的架 构，由许多编码器和解码器堆栈组成。每一个编码器都存在两个子层：自注意力机制层(self- attention)后面紧接着前馈神经网络层(position-wise feed forward)。自注意力层使用了多 个注意力头(attention head)，将每个注意力头的结果进行连接和转换之后，形成自注意 机制层的输出。每个注意力头使用点乘注意力机制(scaled dot-product)来计算输入一个序 列x = (x 1 , · · · , x n )，得到一个同样长度的新的序列z = (z 1 , · · · , z n ): z = Attention (x) (1) 其中x i ∈ R dx ，z ∈ R n×dz 。每一个输出元素z i 是输入元素的线性变换的加权和: z i = n j=1 α ij x j W V (2) 其中W V ∈ R dx×dz 是一个可学习的参数矩阵. 公式2中的向量α i = (α i1 , · · · , α in ) 是通过自注意 力机制模型得到的，该机制捕获了x i 和其它元素之间的对应关系。具体来说，每个元素x j 的自 注意力权重α ij 是通过一个softmax函数计算得到： α ij = exp(e ij ) n k=1 exp(e ik ) (3) 其中 e ij = x i W Q x j W K T √ d z (4) 是一个对齐函数，它用来度量输入元素x i 和x j 的匹配程度. W Q , W K ∈ R dx×dz 是可学习的参 数矩阵。 3.1.2 线 线 线性 性 性化 化 化预 预 预处 处 处理 理 理 因为Transformer是seq2seq模型，输入只支持序列化的输入，所以需要对AMR图进行线性 化的预处理。本文采用Konstas等(2017)提出的深度优先遍历的线性化方法来对AMR图进行预 处理，从而得到简化版的AMR图。在线性化之前，首先移除了图中的变量、wiki链接和语义标 签。图2展示了一个AMR图线性化示例。 3.2 基 基 基准 准 准模 模 模型 型 型2( ( (Baseline2) ) ) 3.2.1 Structure-Aware Self-Attention Zhu等(2019)扩展了传统的自注意力机制框架,提出了一种新颖的结构化的注意力机制，在 对齐函数中显式地对元素对(x i , x j )之间的关系进行编码，用公式5替换公式4。 e ij = x i W Q x j W K + r ij W R T √ d z(5) x i x j 结 结 结构 构 构标 标 标签 签 签序 序 序列 列 列 begin-01 and :ARG1↑ begin-01 Romneycare :ARG2↓ :name↓ begin-01 begin-01 None (Li et al., 2013) 表 1. 图1中一些概念对之间的结构路径示例。 其中W R ∈ R dz×dz 是一个参数矩阵. 然后, 再相应地更新公式2，将结构信息传播到子层的输 出。 z i = n j=1 α ij x j W V + r ij W F (6) 其中，W F ∈ R dz×dz 是一个参数矩阵。r ij ∈ R dz 代表了元素对(x i , x j )之间的关系，它是通 过3.2.2学习到的一个向量表示。 3.2.2 学 学 学习 习 习图 图 图概 概 概念 念 念( ( (concept) ) )对 对 对之 之 之间 间 间的 的 的向 向 向量 量 量表 表 表示 示 示r 上述的Structure-Aware Self-Attention机制可以用来获取到图中任意两两概念对(concept pairs)之间的图结构关系。定义使用沿着概念x i 到x j 之间边标签(edge label)组成的一条路径 当作概念对之间的图结构关系 0 。同时，为了区分方向，也给每条边标签相应的增加了方向符 号。Table 1展示了图 1中的几个概念对之间的结构标签序列。 现在已经给定了一个结构标签路径s = s 1 , · · · , s k ，然后获取到它的向量表示l = l 1 , · · · , l k , 最后本文使用基于卷积神经网络(Kalchbrenner et al., 2014)(CNN-based) 1 的方法来获得公 式 5和公式 6中的向量表示 r ij 。 CNN-based 使用CNN来卷积标签序列l获得一个向量r： conv = Conv1D(kernel size = (m), strides = 1, f ilters = d z , input shape = d z activation = relu ) (7) r = conv (l) (8) 实验中 m 的大小常设置为4。 3.3 数 数 数据 据 据稀 稀 稀疏 疏 疏性 性 性 在训练AMR-to-，本文进一步证明了在生成任务中目标端融入句法信息同样可以有着显著的提升。 表 2也 给 出 了 与 其 它 现 存 模 型 在 该 任 务 上 的 性 能 比 较 。 值 得 注 意 的 是，LDC2015E86和LDC2017T10的验证集和测试集是相同的，区别只是训练集的数量相差 了一倍左右。从表2可以看到，与seq2seq模型相比，本文的baseline1就已经显著的超越了它 们，并且在融入句法信息(+Syntax)之后，性能依然有着明显的提升。目前最高的性能 是Zhu等(2019)提出的Structure-Aware Self-Attention模型，本文在它们的基础之上也同样有着 有效的提高，创造了新的最高的性能(SOTA)。可以证明本文的方法无论用在seq2seq模型或 者graph2seq模型上都有效。 4.4 参 参 参数 数 数数 数 数量 量 量和 和 和训 训 训练 练 练时 时 时间 间 间 本文融合目标端句法信息的方法是将目标端句子替换为线性化结构句法树，不会对模型进 行任何修改，也就意味着并不会给模型增加参数，这也是本文方法的一大优点。但是，目标端 句子替换成线性化结构句法树之后，它的序列长度会相应的变长，这就会导致训练的时间略微增加。据统计，本文baseline1基准模型在LDC2015E86上进行训练，完场一轮训练的时间大概 2018),Song等(2018),Damonte等(2019),Guo等(2019),Zhu等(2019)提 出了图到序列(graph-to-seq，简称G2S)的框架，使用图模型来对AMR图进行建模。然而， 他们的工作都将句子表示为单词序列，并没有考虑到句子中潜在的句法信息。最近的一些研究 也表明，即使百万级的平行语料，模型仍然无法从中捕获深层的句法信息(Li et al., 2017)。 针对上述存在的问题，本文提出一种显示的方法来融入句法信息，从而给定生成时一些句 法约束,并且不需要对模型本身进行任何修改。为了更好的证明本文方法的有效性，本文选取 了S2S中最优的Transformer模型和G2S中现存最优的模型(Zhu et al., 2019)进行了实验。最终， 在两份标准的英文数据集LDC2015E86和LDC2017T10上都取得了显著的提升。 c 2020 中国计算语言学大会 根据《Creative Commons Attribution 4.0 International License》许可出版 融 融 融合 合 合句 句 句法 法 法信 信 信息 息 息 Stanford Parser)(Manning et al., 2014)解析训练集和验证集语料，从而获得对应结构语法树 的银语料(Silver-Standard)。 4 实 实 实验 验 验 4.1 数 数 数据 据 据集 集 集 为了评估方法的有效性，本文使用LDC发行的现存的两份标准英文语料集进行实验，分别 是 LDC2015E86 和 LDC2017T10。两份语料集分别包含了16,833 和 36,521 条训练数据，并且 共享了 1,368 条验证集和 1,371 条测试集。训练集和验证集使用斯坦福解析器 2 获取到目标端句 子所对应的 Penn treebank-style 风格的结构句法树。 4.2 实 实 实验 验 验设 设 设置 置 置Text模型的时候，因为语料数量的限制，常常会受到数据稀疏性的影响。 为了解决这个问题，前人的工作有采用匿名化的方法来删除命名实体和罕见词(Konstas et al., 2017)，或者使用复制机制(Gulcehre et al., 2016)来学习，使模型可以学会从源端输入复制未 登录词到目标端。在本文中，我们提出使用字节对编码(BPE)(Sennrich et al., 2016)将未登 录词拆分成更细粒度，更高频的单词。再根据该任务的特性考虑，共享了源端和目标端的词 表。Zhu等(2019)也在实验中证明了该方法的有效性。 3.4 前人的工作都是使用平行语料来进行训练，输入源端AMR图去生成对应的句子。他们大都 是将句子视为单词序列，但是却忽略了句子本身的一些外部知识，没有考虑到句子中潜藏的句 0 当同时存在多条路径组合时，默认选择最短的那一条。 1 (Zhu et al., 2019)使用了多种方法来学习图结构表示方法，本文选择了CNN-based这一方法作为基线模型。 计算语言学 目标端句子： The girl took apples from a bag 短语结构句法树： 线性化之后的句法树： S NP DT the NN girl VP VBD took NP NNS apples PP P from NP DT a NN bag S NP VP DT NN VBD NP PP The girl took NNS P NP apples from DT NN a bag 图 3. 一个目标端句子解析的短语结构句法树及其线性化示例 法信息。为了使模型能够学习到目标端句子的句法信息和内部结构，本文提出一种显式的方法 来融入目标端的句法信息。 融合目标端句法信息的基本思想是将目标端句子经过解析得到句法树，之后再通过深度优 先遍历得到最终的句法标签序列。句法的标注形式大致有两种，短语结构句法树和依存句法 树。本文在训练时选择使用线性化的短语结构句法树(Vinyals et al., 2015)来替换目标端的句 子。如图3所示，给出了一个目标端句子解析的短语结构句法树和其线性化结果。之所以选择短 语结构句法树，是因为与依存树相比，它具有良好的线性化顺序的优点。此外，短语结构句法 树也更容易实现，因为它们有效的对应句子中单词的顺序。在解码阶段，只需要将句法标签去 除之后，就是最终预测生成的句子。 不幸的是，AMR标注数据并没有发放句法标注数据。因此，本文使用斯坦福解析器 (本文分别通过使用 10K 和 20K 的操作数来对 LDC2015E86 和 LDC2017T10 两份语料进 行BPE操作。从BPE处理之后的训练集中根据词频建立词汇表，参考Ge等(2019)的工作，共享 了源端和目标端的词汇表。为了公平的对比，模型中的词向量使用随机初始化的方式。 本文使用OpenNMT (Klein et al., 2017)的框架作为Transformer的基准模型 3 。在超参数的 设置上，模型的编码器和解码器为 6 层。在优化器方面，本文使用beta1=0.1(Kingma and Ba, 2015)的Adam优化算法。自注意头的数量设置为 8。此外，模型中向量和隐藏状态的维度位置 为 512，批处理大小(batch size)设置为 4096。为了模型计算速度考虑，限定路径标签的最大 System LDC2015E86 LDC2017T10 BLEU Meteor chrF++ BLEU Meteor chrF++ (Konstas et al., 2017) * 22.00 - - - - - (Cao and Clark, 2019) * 23.5 - - 26.8 - - (Song et al., 2018) † 23.30 - - - - - (Beck et al., 2018) † - - - 23.3 - 50.4 (Damonte and Cohen, 2019) † 24.40 23.60 - 24.54 24.07 - (Guo et al., 2019) † 25.7 - - 27.6 - 57.3 (Zhu et al., 2019)(CNN-based) † 29.10 35.00 62.10 31.82 36.38 64.05 (Song et al., 2016) ‡ 22.44 - - - - - Baseline1 25.13 33.08 59.36 26.98 34.36 61.05 + Syntax 26.39 33.63 59.84 28.13 34.82 61.73 Baseline2 28.64 34.83 61.89 31.10 36.07 63.87 + Syntax 29.71 35.49 62.52 32.28 36.81 64.61 表 2. 本文方法在LDC2015E86和LDC2017T10测试集上的实验结果及与其它模型的比较。 * 代 表seq2seq模型， † 代表graph2seq模型， ‡ 代表其它模型。 长度为 4。解码时，默认的额外长度从50增加至150，该值表示模型解码时允许生成句子长度是 源端最大长度加150。在所有实验中，以学习率 0.5 在 Tesla P40 GPU上训练 300K 步停止。本 文实验代码已开源公布至https://github.com/Amazing-J/structural-transformer。 为了更好的体现本文方法的有效性，采用了BLEU(Papineni et al., 2002)，Meteor(Banerjee and Lavie, 2005; Denkowski and Lavie, 2014)，chrF++(Popović, 2017)三种评测指标。BLEU是 基于语料级的评估，后两者是基于句子级的评估。相对来说，后两者的分数更接近于人工评 测。 4.3 实 实 实验 验 验结 结 结果 果 果 表2给出了本文两个基准模型在融合了目标端句法信息前后AMR-to-Text生成的性能对比。 从表2可以看出，融合目标端句法信息之后，AMR-to-Text生成的性能有着显著的提升。在两个 基准模型上，分别提高了1.26和1.07(LDC2015E86)，1.15和1.18(LDC2017T10)BLEU。这 也有力的证明，在目标端融入句法信息，可以帮助模型学习到句子中潜藏的一些知识，从而在 生成时考虑到句法信息的约束。该方法与融合源端句法和语义角色信息的机器翻译方法类似 需要288秒(约4.80分钟)，而融合目标端句法信息之后，大概花费345秒(约5.75分钟)。 4.5 融 融 融合 合 合不 不 不同 同 同形 形 形式 式 式句 句 句法 法 法信 信 信息 息 息的 的 的影 影 影响 响 响 从实验结果可以得到融合目标端句子的句法信息可以显著的提升AMR-to-Text生成的性 能，但是为了探究哪种形式的句法信息对生成性能最为有效，本文做了进一步的实验分析。 一 一 一 (S (NP (DT the )DT (NN girl )NN (VP (VBD took )VBD (NP (NNS apples )NNS (PP (P from )P (NP (DT a )DT (NN bag )NN )NP )PP )NP )VP )NP )S 二 二 二 S NP the girl VP took NP apples PP from NP a bag 三 三 三 S NP DT the NN girl VP VBD took NP NNS apples PP P from NP DT a NN bag 表 3. 三种线性化结构句法树的示例 5 结 结 结论 论 论 本文提出了一种直接而有效的方法融合目标端句子的句法信息，并且在最优的seq2seq模 型Transformer上以及AMR-to-Text生成任务中最优的模型上都进行了实验。实验结果表明， 使用该方法可以有效的对目标端句子的句法信息进行学习，从而提高AMR-to-Text生成的性 能。在该任务最优模型的基础上同样也有着 1.07 和 1.18 BLEU值的提升，创立了新的最高性 能。未来的工作中，由于句法分析和生成任务有着比较高的关联性，所以将会探索句法分析 与AMR-to-Text生成任务之间的联合学习方向。一 一 一 二 二 二 三 三 三 BLEU 24.84 25.89 26.39 表 4. 三种线性化结构句法树在LDC2015E86测试集的性能对比 如表3所示，本文探索了三种线性化短语结构句法树对生成性能的影响。第一种形式包 括了一个完整的句法树，不仅保留了句法树的所有节点，还给相应节点增加了结束标签，例 如)NN,)NP,)S等。第二种形式则没有增加节点的结束标签，并且为了缩减句子的长度，把句法 树中的词性标签删除，仅保留句法树的主干成份，剔除如DT，NN，VBD等词性标签。第三种 形式则是在第二种形式的基础上保留了单词的词性标签。 本文在LDC2015E86的测试集上，对上述三种句法树的形式做了实验。从表4可以看出，当 使用第一种形式时候，性能最差。因为它会将目标端的句子长度几倍的增长，极大地增加了模 型的学习难度。从第二种和第三种形式得到的性能可以看出，保留短语结构句法树中的词性标 签信息是对生成有着明显的贡献，有着0.5BLEU值的提升。本文则是使用的第三种短语结构句 法树的形式。 https://nlp.stanford.edu/software/lex-parser.html 3 https://github.com/OpenNMT/OpenNMT-py Pointing the unknown words. 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Amr-to-text generation as a traveling salesman problem. Linfeng Song, Yue Zhang, Xiaochang Peng, Zhiguo Wang, Daniel Gildea, Proceedings of EMNLP. EMNLPLinfeng Song, Yue Zhang, Xiaochang Peng, Zhiguo Wang, and Daniel Gildea. 2016. Amr-to-text gener- ation as a traveling salesman problem. In Proceedings of EMNLP, pages 2084-2089. Amr-to-text generation with synchronous node replacement grammar. Linfeng Song, Xiaochang Peng, Yue Zhang, Zhiguo Wang, Daniel Gildea, Proceedings of ACL. ACLLinfeng Song, Xiaochang Peng, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2017. Amr-to-text gener- ation with synchronous node replacement grammar. In Proceedings of ACL, pages 7-13. A graph-to-sequence model for AMR-to-text generation. Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea, Proceedings of ACL. ACLLinfeng Song, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2018. A graph-to-sequence model for AMR-to-text generation. In Proceedings of ACL, pages 1616-1626. 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Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text

For the purpose of POS tagging noisy user-generated text, should normalization be handled as a preliminary task or is it possible to handle misspelled words directly in the POS tagging model? We propose in this paper a combined approach where some errors are normalized before tagging, while a Gated Recurrent Unit deep neural network based tagger handles the remaining errors. Word embeddings are trained on a large corpus in order to address both normalization and POS tagging. Experiments are run on Contact Center chat conversations, a particular type of formal Computer Mediated Communication data.

Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text Géraldine Damnati geraldine.damnati@orange.com Orange Labs 2) Aix-Marseille UnivLannionFrance Université de Toulon CNRS LIS Jeremy Auguste jeremy.auguste@univ-amu.fr Alexis Nasr alexis.nasr@univ-amu.fr Delphine Charlet delphine.charlet@orange.com Orange Labs 2) Aix-Marseille UnivLannionFrance Université de Toulon CNRS LIS Johannes Heinecke johannes.heinecke@orange.com Orange Labs 2) Aix-Marseille UnivLannionFrance Université de Toulon CNRS LIS Frédéric Béchet frederic.bechet@univ-amu.fr Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text Part of Speech TaggingComputer Mediated CommunicationSpelling Error Correction For the purpose of POS tagging noisy user-generated text, should normalization be handled as a preliminary task or is it possible to handle misspelled words directly in the POS tagging model? We propose in this paper a combined approach where some errors are normalized before tagging, while a Gated Recurrent Unit deep neural network based tagger handles the remaining errors. Word embeddings are trained on a large corpus in order to address both normalization and POS tagging. Experiments are run on Contact Center chat conversations, a particular type of formal Computer Mediated Communication data. Introduction Contact Center chat conversation is a particular type of noisy user generated text in the sense that it is a formal Computer Mediated Communication (CMC) interaction mode. It shares some normalization issues with other CMC texts such as chatroom conversations or social media interactions but unlike the aforementioned cases, the professional context implies some specificities. For instance, contact center logs are hardly prone to Internet slang. Another characteristic is that they are dyadic conversations with asymmetric levels of orthographic or grammatical errors. Agents may write with mistakes but are usually recruited for their linguistic skills, and can rely on predefined utterance libraries. Customers on the other hand can make mistakes for several different reasons, be it their educational background, linguistic skills, or even the importance they pay to the social perception of the errors they would make. Some of them will make no mistake at all while some others will misspell almost every word. The purpose of this paper is to perform POS tagging on this particular type of Noisy User Generated text. Our goal is to study to which extent it is worth normalizing text before tagging it or directly handling language deviations in the design of the tagger. We will show that a good compromise is to handle some of the errors through lexical normalization but also to design a robust POS tagger that handles orthographic errors. We propose to use word embeddings at both levels: for text normalization and for POS tagging. Related work Text normalization has been studied for several years now, with different perspectives over time. When studying SMS style language, researchers tried to handle new phenomena including voluntary slang shortcuts through phonetic models of pronunciation (Toutanova and Moore, 2002;Kobus et al., 2008). Recently, the effort has been more particularly set on Social Media text normalization with specific challenges on Twitter texts (Baldwin et al., 2015), which has been shown to be more formal (Hu et al., 2013) that what is commonly expected. The typology of errors is slightly different and most recent works focus on one-to-one lexical errors (replacing one word by another). The availability of large corpora has led to the design of normalization lexicons (Han et al., 2012) that directly map correct words to there common ill-formed variants. (Sridhar, 2015) learns a normalization lexicon and converts it into a Finite State Transducer. More recently, the construction of normalization dictionaries using word embeddings on Twitter texts were performed for Brazilian Portuguese (Bertaglia and Nunes, 2016). In this paper, we focus on out-of-vocabulary words. We propose to generate variants of such words using a lexical corrector based on a customized edit distance and to use word embeddings as distributed representations of words to re-rank these hypotheses thanks to contextual distance estimation. In order to adapt POS tagging systems for noisy text, several approaches have proposed to use word clusters provided by hierarchical clustering approaches such as the Brown algorithm. (Owoputi et al., 2013) use word clusters along with dedicated lexical features to enrich their tagger in the context of online conversations. (Derczynski et al., 2013) use clustering approaches to handle linguistic noise, and train their system from a mixture of hand-annotated tweets and existing POS-labeled data. (Nasr et al., 2016) address the issue of training data mismatch in the context of online conversations and show that equivalent performance can be obtained by training on a small in domain corpus rather than using generic POS-labeled resources. Text normalization Our text normalization process operates in two steps, the first one produces in-lexicon variants for an out of lexicon form. The second one reranks the forms produced by the first step, using a distributional distance. The first step is based on a lexicon and an edit distance while the second relies on word embeddings. We focus on one-to-one normalization, avoiding the issue of agglutinations or split words. Defining a target lexicon In order to generate correct hypotheses of an out of vocabulary form, we need to define a target lexicon. A lexicon should both reflect general language common terms and company related specific terms. If the general common terms lexicon is very large, a lexical corrector would have more chances to propose irrelevant out of domain alternatives. Hence, we have chosen to reduce the size of our lexicon by selecting words that appear more than 500 times in the French Wikipedia, resulting in 36,420 words. Additionally a set of 388 manually crafted domain specific terms was added to the lexicon. The latter were obtained by selecting words in the manually corrected training corpus that were not covered by the general lexicon. Finally, as case is not a reliable information in such data we reduce all words of the lexicon to their lower case form. Contrastive experiments have been run but are not reported in this extended absract, showing that the choice of the lexicon is important for the whole process. Including a general knowledge lexicon from Wikipedia is more helpful for correcting Agent errors than for correcting Customer errors. Edit-distance based normalization The corrector built with this lexicon is based on the Damerau-Levenshtein (DL) distance. The code of the lexical corrector is available at the above mentioned url 1 . In contrast to standard DL we assign weights to error types: missing or superfluous diacritics only add 0.3 to the distance. Additionally, adjacent letters on the keyboard (like an e instead of an r, which sits just next to each other on QWERTY and AZERTY keyboards), add 0.9 to the editdistance. Letter transpositions (such as teh instead of the) also account for 0.9. All other differences account for 1 in the global distance. These weights are configurable and have been optimized for our task. Words to be processed are all transformed to their lower case form before applying the corrector with the lower case lexicon described in 3.1. The original case is reintroduced before applying the POS tagger. The lexical corrector provides a list of candidates for correction, until a maximum cost is reach. This upper bound is proportional to the word length n in terms of number of letters and is computed as follows: max cost = n × γ In these experiments γ is set to 0.3. Here again contrastive experiments can be provided showing the impact of the γ parameter. As we are dealing with formal interactions,we did not apply the modification on the edit distance proposed by (Hassan and Menezes, 2013) where edit distance is computed on consonant skeletons, nor do we use Longest Common Subsequence Ratio (LCSR) as it didn't reveal to be helpful in our case. Rescoring with word embeddings The edit distance based variant generation process described above does not take into account the context of a word when generating variants. In order to take it into account, we propose to rescore the hypothesized alternatives using a distance metric derived from the cosine similarity between word embeddings. We have gathered a large amount of unannotated chat conversations from the same technical assistance domain, resulting in a 16.2M words corpus, denoted BIG. For the particular purpose of lexical normalization we are more interested in paradigmatic associations than in syntagmatic associations. Hence word2vec is used with a small window size of 4. Furthermore, in order to capture as many tokens as possible we have chosen to keep all tokens occurring at least twice in the corpus when learning the word embeddings. The lexicon produced contains 43.4K forms. Let w be an observed form and α i (w) be the i th alternative proposed by the edit distance based lexical corrector. Let V emb be the vocabulary of the word vector model estimated on the large unannotated corpus, and v w denote the vector of word w. The word embeddings based distance d emb (w, α i (w)) is defined as 1 − cos(v w , v αi(w) ). If ei- ther v or α i (w) does not belong to V emb , d emb (w, α i (w)) is set to 1, meaning that it will not have any effect on the re-scoring process. Let C(w, α i (w)) be the edit cost provided by the lexical corrector between w and the proposed alternative α i (w), the rescoring process simply consists in multiplying the edit score by the distance derived from the embeddings. C emb (w, α i (w)) = C(w, α i (w)) × d emb (w, α i (w)) Part of speech tagging The part of speech tagger used in our experiment is based on Gated Recurrent Units (GRU). GRUs, introduced by (Cho et al., 2014), are recurrent neural networks that work in a similar fashion than LSTMs. GRUs are simpler than LSTMs: they do not have an output gate, and the input and forget gates are merged into an update gate. This property allows GRUs to be computationally more efficient. The ability of GRUs to handle long distance dependencies make them suitable for sequence labeling tasks, such as POS tagging. Our tagger uses a bidirectional GRU making use of past and future features for each specific word in a sentence. The bidirectionnal GRU consists of a forward layer and a backward layer which outputs are concatenated. The forward layer processes the sequence from the start to the end, while the backward layer processes it from the end to the start. The input of the network is a sequence of words with their associated morphological and lexical features. The words are encoded using a lookup table which associates each word with its word embedding representation. These word embeddings can be initialized with pretrained embeddings and/or learned when training the model. For the morphological and typographic features, we use a boolean value for the presence of an uppercase character as the first letter of the word as well as the word suffixes of length 3 and 4 represented as onehot vectors. Finally, we also input as onehot vectors external lexicon information, constructed using the Lefff lexicon (Sagot, 2010). Such vectors represent the possible part-of-speech labels of a word. On the output layer, we use a softmax activation. During training, categorical cross-entropy is used as the loss function and the Adam optimiser (Kingma and Ba, 2014) is used for the gradient descent optimisation. Experiments and results The corpus used for our experiments has been extracted from chat conversation logs of a French technical assistance contact center. A set of 91 conversations has been fully manually corrected and POS tagged. This corpus has been split in two equal parts: the TRAIN part being used to train the POS tagger and the TEST part for evaluation. Both sets contain around 17K words, with 5.4K words from the Customer side 11.6K words from the Agent side. The typology of errors follow the one proposed in (Nasr et al., 2016). DIACR stands for diacritic errors which are common in French, APOST for missing or misplaced apostrophe, AGGLU for agglutinations and SPLIT for words split into two words. It is common in French to find confusions INFPP between past participles and infinitives for verbs ending with er (j'ai changé ↔ j'ai changer). Morpho-syntactic inflection INFL in French is error prone as it is common that different inflected forms of a same word are homophones. MOD1C correspond to one modified character (substituted, deleted or inserted) or when two adjacent letters are switched. Text Normalization Evaluation In Table 1, we present the results of the text normalization steps, on the whole corpus. editonly refers to text processed by the edit-based correction. editembed refers to the full correction process with semantic rescoring based on word embedding distances. We show in the first line the amount of word errors that are potentially correctable by the proposed approach (i.e. errors leading to Out-of-Vocabulary words) and the remaining subset of word errors which can not be corrected by our approach (errors resulting in in-vocabulary words and words discarded from the correction process). Among the total amount of 1646 erroneous words, 53% (870) are potentially correctable. The other 47% are words that do appear in our lexicon. After the edit-based correction step, 76.7% of these errors have been corrected, leading to 202 remaining errors. When rescoring with semantic similarity, the editembed approach enables to correct 5% additional words, leading to an overall correction of 81.6% of the potentially correctable errors. It is worth noticing that in our approach, as the lexicon used in the correction step is not exhaustive, we observe 80 added errors due to the fact that some words, which were correct in the raw text, but not present in the lexicon, have been erroneously modified into an in-vocabulary form. Overall, the word error rate (WER) on raw text was 4.37% and is reduced to 2.81% after editonly, and to 2.7% after editembed semantic rescoring. When restricting the corpus to the Customer messages, the initial WER reaches 9.82% and the normalization process leads to 5.07%. Detailed error numbers according to the type of errors, on TEST only, can be found in Table 3. As expected, the proposed approach is efficient for diacritics, apostrophes and 1 letter modifications (DIACR, APOST, MOD1C). However it is inefficient for agglutination AGGLU and SPLIT, Table 1: Evaluation of normalization. Number of correctable and non-correctable errors (second and third lines) and word error rates (lines four to six). Third and fourth columns indicate the errors after normalizing the text with respectively the edit distance based and the word embedding distance based normalization. for confusion of verbal homophonic forms (INFPP) and for inflexion errors (INFL). Part of speech tagging results Three different taggers have been trained on the corrected version of the train corpus 2 . They differ in the embeddings that were used to represent the words. The first tagger does not use any pre-trained embeddings, the second uses embeddings trained on the raw corpus while the third one uses embeddings trained on the automatically corrected corpus. Three versions of the test corpus have been taken as input to evaluate the taggers. The raw version, the gold version, which has been manually corrected and the auto version, which has been automatically corrected. The accuracy of the three taggers on the three versions of the test corpus are represented in Table 2. POS accuracy has been computed on the whole TEST corpus as well as on subsets of the TEST corpus produced by the agents and the customers. Table 2 shows that the three taggers reach almost the same performances on the gold version of the TEST corpus. The best performances on the raw TEST corpus are obtained by the second tagger, which word embeddings have been trained on the raw BIG corpus. This result does not come as a surprise since the raw TEST corpus contains spelling errors that could have occurred in the raw BIG corpus and therefore have word embedding representations. Although the tagger that uses pretrained word embeddings yields better results than the first tagger, it is still beneficial to automatically correct the input prior to tagging. Table 2 also shows that the benefits of using word embeddings trained on the raw BIG corpus is higher on the customer side, which was also expected since this part of the corpus contains more errors. Using embeddings trained on the automatically corrected BIG corpus doesn't yield any further improvements, suggesting that the initial embeddings trained on the raw corpus already capture the relevant information. The influence of the spelling errors on the tagging process is analysed in Table 3. Each line of the table corresponds to one type T of spelling error. The left part of the table presents the results on the raw version of the test data and the right part, on its corrected version. The first column in each part is the total number of occurrences of type T errors, the second column is the number of type T errors that also correspond to a tagging error and the third column, the part of T errors that correspond to a tagging error (ratio of columns 1 and 2). The tagger used here is the second one, which uses word embeddings trained on raw data. Table 3 shows that the type of spelling error that is the more POS error prone is the INFPP type, which almost always lead to a tagging error. More generally, the table shows that the correction process tends to correct errors that are not very harmful to the tagger. This is especially true for the diacritic errors: the correction process corrects 67% of them but the number of tagging errors on this type of spelling errors is only decreased by 32.3%. Actually the remaining diacritic errors are typically errors on frequent function words in French that have different categories (où ↔ ou,à ↔ a meaning where ↔ or, to ↔ have). Table 3: POS tagging errors with respect to spelling error types, on raw and on corrected input. Column Spell is the number of errors of the corresponding type, Column Tag is the number of errors of the type that also correspond to tagging errors, column ratio is the ratio of column Tag and Spell. Conclusion We have shown in this paper that word embeddings trained on a noisy corpus can help for both tasks of correcting misspelled words and POS tagging noisy input. We have also quantified the impact of spelling errors of different categories on the POS tagging task. We plan as future work to combine both processes in a single one that performs both POS tagging and correction. Table 2 : 2POS tagging accuracy of the three taggers on the test corpus. For each tagger results are given on three ver- sions of the corpus: manually corrected (gold), automati- cally corrected (auto) and raw. The two last columns indi- cate accuracy on the Agent and Customer parts of the cor- pus. https://github.com/Orange-OpenSource/lexical-corrector Taggers trained on the raw versions of the corpus yielded lower results. AcknowledgementsThis work was partially funded by the French Agence Nationale pour la Recherche (ANR) under the grant ANR-15-CE23-0003 7. Bibliographical References Shared tasks of the 2015 workshop on noisy user-generated text: Twitter lexical normalization and named entity recognition. T Baldwin, M C De Marneffe, B Han, Y.-B Kim, A Ritter, W Xu, Proceedings of the Workshop on Noisy User-generated Text (WNUT 2015). the Workshop on Noisy User-generated Text (WNUT 2015)Beijing, ChinaBaldwin, T., De Marneffe, M. C., Han, B., Kim, Y.-B., Rit- ter, A., and Xu, W. (2015). Shared tasks of the 2015 workshop on noisy user-generated text: Twitter lexi- cal normalization and named entity recognition. 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Acquiring Synonyms from Monolingual Comparable Texts

This paper presents a method for acquiring synonyms from monolingual comparable text (MCT). MCT denotes a set of monolingual texts whose contents are similar and can be obtained automatically. Our acquisition method takes advantage of a characteristic of MCT that included words and their relations are confined. Our method uses contextual information of surrounding one word on each side of the target words. To improve acquisition precision, prevention of outside appearance is used. This method has advantages in that it requires only part-ofspeech information and it can acquire infrequent synonyms. We evaluated our method with two kinds of news article data: sentence-aligned parallel texts and document-aligned comparable texts. When applying the former data, our method acquires synonym pairs with 70.0% precision. Re-evaluation of incorrect word pairs with source texts indicates that the method captures the appropriate parts of source texts with 89.5% precision. When applying the latter data, acquisition precision reaches 76.0% in English and 76.3% in Japanese.

Acquiring Synonyms from Monolingual Comparable Texts Mitsuo Shimohata shimohata363@oki.com Oki Electric Industry Co., Ltd 2-5-7, Chuo-kuHonmachi, Osaka CityJapan Eiichiro Sumita eiichiro.sumita@atr.jp ATR Spoken Language Translation Research Laboratories 2-2-2 Hikaridai, Keihanna Science CityKyotoJapan Acquiring Synonyms from Monolingual Comparable Texts This paper presents a method for acquiring synonyms from monolingual comparable text (MCT). MCT denotes a set of monolingual texts whose contents are similar and can be obtained automatically. Our acquisition method takes advantage of a characteristic of MCT that included words and their relations are confined. Our method uses contextual information of surrounding one word on each side of the target words. To improve acquisition precision, prevention of outside appearance is used. This method has advantages in that it requires only part-ofspeech information and it can acquire infrequent synonyms. We evaluated our method with two kinds of news article data: sentence-aligned parallel texts and document-aligned comparable texts. When applying the former data, our method acquires synonym pairs with 70.0% precision. Re-evaluation of incorrect word pairs with source texts indicates that the method captures the appropriate parts of source texts with 89.5% precision. When applying the latter data, acquisition precision reaches 76.0% in English and 76.3% in Japanese. Introduction There is a great number of synonyms, which denote a set of words sharing the same meaning, in any natural language. This variety among synonyms causes difficulty in natural language processing applications, such as information retrieval and automatic summarization, because it reduces the coverage of lexical knowledge. Although many manually constructed synonym resources, such as WordNet [4] and Roget's Thesaurus [12], are available, it is widely recognized that these knowledge resources provide only a small coverage of technical terms and cannot keep up with newly coined words. We propose a method to acquire synonyms from monolingual comparable text (MCT). MCT denotes sets of different texts 1 that share similar contents. MCT are appropriate for synonym acquisition because they share not only many synonymous words but also the relations between the words in a each text. Automatic MCT construction can be performed in practice through state-ofthe-art clustering techniques [2]. News articles are especially favorable for text clustering since they have both titles and date of publication. Synonym acquisition is based on a distributional hypothesis that words with similar meanings tend to appear in similar contexts [5]. In this work, we adopt loose contextual information that considers only the surrounding one word from each side of the target words. This narrow condition enables extraction from source texts 2 that have different structures. In addition, we use another constraint, prevention of outside appearance, which reduces improper extraction by looking over outside places of other texts. This constraint eliminates many nonsynonyms having the same surrounding words by chance. Since our method does not cut off acquired synonyms by frequency, synonyms that appear only once can be captured. In this paper, we describe related work in Sect. 2. Then, we present our acquisition method in Sect. 3 and describe its evaluation in Sect. 4. In the experiment, we provide a detailed analysis of our method using monolingual parallel texts. Following that, we explain an experiment on automatically constructed MCT data of news articles, and conclude in Sect. 5 Related Work Word Clustering from Non-comparable Text There have been many studies on computing similarities between words based on their distributional similarity [6,11,7]. The basic idea of the technique is that words sharing a similar characteristic with other entities form a single cluster [9,7]. A characteristic can be determined from relations with other entities, such as document frequency, co-occurrence with other words, and adjectives depending on target nouns. However, this approach has shortcomings in obtaining synonyms. First, words clustered by this approach involve not only synonyms but also many nearsynonyms, hypernyms, and antonyms. It is difficult to distinguish synonyms from other related words [8]. Second, words to be clustered need to have high frequencies to determine similarity, therefore, words appearing only a few times are outside the scope of this approach. These shortcomings are greatly reduced with synonym acquisition from MCT owing to its characteristics. Lexical Paraphrase Extraction from MCT Here, we draw comparisons with works sharing the same conditions for acquiring synonyms (lexical paraphrases) from MCT. Barzilay et al. [1] shared the same conditions in that their extraction relies on local context. The difference is that their method introduces a refinement of contextual conditions for additional improvement, while our method introduces two non-contextual conditions. Pang et al. [10] built word lattices from MCT, where different word paths that share the same start nodes and end nodes represent paraphrases. Lattices are formed by top-down merging based on structural information. Their method has a remarkable advantage in that synonyms do not need to be surrounded with the same words. On the other hand, their method is not applicable to structurally different MCTs. Shimohata et al. [13] extracted lexical paraphrases based on the substitution operation of edit operations. Text pairs having more than three edit distances are excluded from extraction. Therefore, their method considers sentential word ordering. Our findings, however, suggest that local contextual information is reliable enough for extracting synonyms. Synonym Acquisition Synonym extraction relies on word pairs that satisfy the following three constraints: (1) agreement of context words; (2) prevention of outside appearance; and (3) POS agreement. Details of these constraints are described in the following sections. Then, we describe refinement of the extracted noun synonyms in Sect. 3.4. Agreement of Context Words Synonyms in MCTs are considered to have the same context since they generally share the same role. Therefore, agreement of surrounding context is a key feature for synonym extraction. We define contextual information as surrounding one word on each side of the target words. This minimum contextual constraint permits extraction from MCT having different sentence structures. Figure 1 shows two texts that have different structures. From this text pair, we can obtain the following two word pairs WP-1 and WP-2 with context words (synonym parts are written in bold). These two word pairs placed in different parts would be missed if we used a broader range for contextual information. Sentence 1 The severely wounded man was later rescued by an armored personnel carrier. Troops arived in an armored troop carrier and saved the seriously wounded man. Sentence 2 Fig. 1. Extracting Synonyms with Context Words WP-1 "the severely wounded" ⇔ "the seriously wounded" WP-2 "armored personnel carrier" ⇔ "armored troop carrier" Words are dealt with based on their appearance, namely, by preserving their capitalization and inflection. Special symbols representing "Start-of-Sentence" and "End-of-Sentence" are attached to sentences. Any contextual words are accepted, but cases in which the surrounding words are both punctuation marks and parentheses/brackets are disregarded. Prevention of Outside Appearance Prevention of outside appearance is a constraint based on characteristics of MCT. It filters incorrect word pairs by looking into outside of synonym words and context words in the other text (we call this outside region the "outside part."). This constraint is based on the assumption that an identical context wordeither a noun, verb, adjective, or adverb -appears only once in a text. Actually, our investigation of English texts in the Multiple-Translation Chinese Corpus data (MTCC data described in Sect. 4.1) proves that 95.2% of either nouns, verbs, adjectives, or adverbs follow this assumption. This constraint eliminates word pairs that have a word satisfying the following two constraints. C1 The word appears in the outside part of the other text. C2 The word does not appear in the synonym part of the other text. The constraint C1 means that the word in the outside part of the other text is considered as a correspondent word, and a captured word is unlikely to be corresponding. In other words, appearance of the word itself is more reliable than local context coincidence. The constraint C2 means that if the word is included in the synonym part of the other text, this word pair is considered to capture a corresponding word independent of the outside part. Figure 2 illustrates an example of outside appearance. From S1 and S2, the word pair "Monetary Union" and "Finance Minister Engoran" can be extracted. However, the word "Monetary" in S1 does appear in the synonym part of S2 but does appear in another part of S2. This word pair is eliminated due to outside appearance. However, if the word appears in the synonym part of S2, it remains independent of the outside part. This constraint is a strong filtering tool for reducing incorrect extraction, although it inevitably involves elimination of appropriate word pairs. When applying this constraint to the MTCC data (described in Sect. 4.1), this filtering reduces acquired noun pairs from 9,668 to 2,942 (reduced to 30.4% of non-filtered pairs). POS Agreement Word pairs to be extracted should have the same POS. This is a natural constraint since synonyms described in ordinary dictionaries share the same POS. In addition, we focus our target synonym on content words such as nouns, verbs, adjectives, and adverbs. A definition of each POS is given below. Adverbs Consist of one adverb. The word pair WP-1 satisfies the constraint for adverbs, and WP-2 satisfies that for nouns. The MCT in Fig. 1 can produce the word pair "the severely wounded man" and "the seriously wounded man." This word pair is eliminated because the synonym part consists of an adverb and an adjective and does not satisfy the constraint. Refinement of Noun Synonym Pairs Acquired noun pairs require two refinement processes, incorporating context words and eliminating synonyms that are subsets of others, since nouns are allowed to contain more than one word. After the extraction process, we can obtain noun pairs with their surrounding context words. If these context words are considered to be a part of compound nouns, they are incorporated into the synonym part. A context word attached to the front of the synonym part is incorporated if it is either a noun or an adjective. One attached to the back of the synonym part is incorporated if it is a noun. Thus, when the noun pair "air strike operation" = "air attack operation" is extracted, both context words remain since they are nouns. Next, a noun pair included in another noun pair is deleted since the shorter noun pair is considered a part of the longer noun pair. If the following noun pairs Noun-1 and Noun-2 are extracted 3 , Noun-1 is deleted by this process. Noun-1 "British High" ⇔ "British Supreme" Noun-2 "British High Court" ⇔ "British Supreme Court" Experiment We used two types of MCT data: sentence-aligned parallel texts (MTCC) and document-aligned comparable texts (Google News). Both data are based on news articles, and their volumes are relatively small. The former data are used for detailed analysis and the latter data are employed to show practical performance. The Google News data consists of both English and Japanese versions. Table 1 shows the statistics of the experimental data, with the major difference between MTCC and Google News data being "Words per Text." The text length of Google News data is much longer than MTCC data since texts in Google News data denote a whole article whereas those in MTCC data denote a sentence. These two English data and the one Japanese data originally contained plain text data. We applied the Charniak parser [3] to the English data and Chasen 4 to the Japanese data to obtain POS information. It should be noted that we do not use any information except that of POS from parsed results. Multiple-Translation Chinese Corpus The Linguistic Data Consortium (LDC) releases several multiple-translation corpora to support the development of automatic means for evaluating translation quality. The Multiple-Translation Chinese Corpus 5 (MTCC) is one of those, and it contains 105 news stories and 993 sentences selected from three sources of journalistic Mandarin Chinese text. Each Chinese sentence was independently translated into 11 English sentences by translation teams. We applied the Charniak parser to these 10,923 translations and obtained 10,655 parsed results. This data comprises high-quality comparable texts, namely parallel texts. We applied our method to the data and obtained 2,952 noun pairs, 887 verb pairs, 311 adjective pairs, and 92 adverb pairs. Samples of acquired synonyms are shown in Appendix A. Roughly speaking, the number of acquired word pairs for each POS is proportional to the frequency of occurrence for that POS in the MTCC data. Extracted word pairs were manually evaluated by two methods: evaluation with source texts and without source texts. First, an evaluator judged whether extracted word pairs were synonyms or not without source texts. If two words could be considered synonyms in many cases, they were marked "yes," otherwise "no." The criterion for judgment conformed to that of ordinary dictionaries, i.e., the evaluator judges whether given a word pair would be described as a synonym by an ordinary dictionary. Therefore, word pairs heavily influenced by the source texts are judged as "no," since these word pairs are not synonymous in general situations. Morphological difference (e.g. singular/plural in nouns) is not taken into consideration. Next, word pairs evaluated as non-synonyms were re-evaluated with their source texts. This evaluation is commonly used in paraphrase evaluation [1,10]. When word pairs could be considered to have the same meaning for the given sentence pair, the evaluator marked "yes," otherwise "no." This evaluation clarifies the ratio of the these two causes of incorrect acquisition. 1. The method captures proper places in sentences from source texts, but the semantic difference between words in this place pair exceeds the range of synonyms. 2. The method captures improper places in sentences from source texts that have the same local context by chance. An example of evaluation with source texts and without source texts is shown in Fig. 3. Samples of this evaluation are also shown in Appendix A. The precision, the ratio of "yes" to the total, on MTCC data by each POS is shown in Fig. 4, where the All POS precision with source texts reaches 89.5%. This result suggests that our method could capture proper places of MCT pairs with this level of precision. However, this precision falls to 70.0% without source texts that represents synonym acquisition precision. This is because some of the extracted word pairs have a hypernymous relationship or have great influence on context in source texts. Acquired word pairs include those occurring only once since our method does not cut off according to word frequency. The amount of those occurring only once accounts for 88.8% of the total. This feature is advantageous for acquiring proper nouns; acquired word pairs including proper nouns account for 63.9% of the total noun pairs. Word pair judged as non-synonym Synonym-1 Muslim robe Synonym-2 sarong Source Text Pair Sentence-1 A resident named Daxiyate wears a turban and Muslim robe. Sentence-2 A citizen named Daciat wore a Moslem hat and sarong. Here, we discuss our method's coverage of all the synonyms in the training data. Since it is very difficult to list all synonyms appearing in the training data, we substitute identical word pairs for synonym pairs to estimate coverage. We counted identical word pairs from all MCT pairs (Total) and those that have the same context words (Same Context). The ratio of "Same Context" to "Total" denotes coverage of our method and it was found to be 27.7%. If the tendency of local context for identical word pairs is equal to that of synonym word pairs, our method can capture 27.7% of the embedded synonyms in the training data. We looked up acquired word pairs in WordNet 6 , a well-known publicly available thesaurus, to see how much general synonym knowledge is included in the acquired synonyms. We could obtain 1,001 different word pairs of verbs, adjectives, and adverbs after unifying conjugation 7 . WordNet knows, i.e., both words are registered as entries, 951 word pairs (95.0%) among the 1,001 acquired pairs. The thesaurus covers, i.e., both words are registered as synonyms, 205 word pairs (21.6%) among 951 known pairs. This result shows that our method can actually capture general synonym information. The remaining acquired word pairs are still valuable since they include either general knowledge not covered by WordNet or knowledge specific to news articles. For example, extracted synonym pairs, "express"="say," "present"="report," and "decrease"="drop" are found from the data and are not registered as synonyms in WordNet. Google News Data We applied our method to Google News data acquired from "Google News, 8 " provided by Google, Inc. This site provides clustered news articles that describe the same events from among approximately 4,500 news sources worldwide. From the Google News site, we gathered articles with manual layout-level checking. This layout-level checking eliminates unrelated text such as menus and advertisements. Our brief investigation found that clustered articles often have a small overlap in described facts since each news site has its own interest and viewpoint in spite of covering the same topic. We use entire articles as "texts" and do not employ an automatic sentence segmentation and alignment tool. This is because the results derived from automatic sentence segmentation and alignment on the Google News data would probably be unreliable, since the articles greatly differ in format, style, and content. Since our method considers only one-word-length context in each direction, it can be applied to this rough condition. On the other hand, this condition enables us to acquire synonyms placed at distant places in articles. The next issue for the experimental conditions is the range for outsideappearance checking. Following the condition of MTCC data, the outside-appearance checking range covers entire texts, i.e., outside appearance should be checked throughout an article. However, this condition is too expensive to follow since text length is much longer than that of MTCC data. We tested various ranges of 0 (no outside-appearance checking), 10, 20, 40, 70, 100, 200, and unlimited words. Figure 5 illustrates the range of outside-appearance checking. We limit the words to be tested to nouns since the acquired amounts of other POS types are not sufficient. Acquired noun pairs are evaluated without source -20 words +20 words The tendencies of these two data are similar, as the range expands, precision increases and the amount of acquired pairs decreases at an exponential rate. When the range is close to unlimited, precision levels off. The average precision at this stable range is 76.0% in English data and 76.3% in Japanese. The precision improvement (from 13.8% to 76.0% in English data and from 9.5% to 76.3% in Japanese data) shows the great effectiveness of prevention of outside appearance. Conclusions We proposed a method to acquire synonyms from monolingual comparable texts. MCT data are advantageous for synonym acquisition and can be obtained automatically by a document clustering technique. Our method relies on agreement of local context, i.e., the surrounding one word on each side of the target words, and prevention of outside appearance. The experiment on monolingual parallel texts demonstrated that the method acquires synonyms with a precision of 70.0%, including infrequent words. Our simple method captures the proper place of MCT text pairs with a precision of 89.5%. The experiment on comparable news data demonstrated the robustness of our method by attaining a precision of 76.0% for English data and 76.3% for Japanese data. In particular, prevention of outside-appearance played an important role by improving the precision greatly. The combination of our acquisition method, an automatic document clustering technique, and daily updated Web texts enables automatic and continuous synonym acquisition. We believe that the combination will bring great practical benefits to NLP applications. Fig. 2 . 2Text Pair Having Outside Appearance Nouns Consist of a noun sequence. Length of sequences is not limited. Verbs Consist of one verb. Adjectives Consist of one adjective. Fig. 3 .Fig. 4 . 34Example Precisions for MTCC Data Fig. 5 .Fig. 6 .Fig. 7 . 567Range Precisions of Google (E) by Outside-Appearance Checking Range Precisions of Google (J) by Outside-Appearance Checking Range texts. Appendix B shows examples. Figures 6 and 7 display the amount and precision for acquired nouns in each range of English data and Japanese data, respectively. Table 1 . 1Statistics of Three Experimental DataMTCC Google News (E) Google News (J) Text Clusters 993 61 88 Texts 10,655 394 417 Words 302,474 176,482 127,482 Texts per Cluster (Mean) 10.7 6.5 4.7 Words per Text (Mean) 28.4 447.9 305.7 (Variance) 364.5 64591.3 55495.7 MTCC: Multiple-reference Data from LDC In this paper, "text" can denote various text chunks, such as documents, articles, and sentences. We call texts that yield synonyms as "source texts." All words in these expressions belong to "proper noun, singular" (represented as NNP in the Penn Treebank manner). http://chasen.naist.jp/hiki/ChaSen/ 5 Linguistic Data Consortium (LDC) Catalog Number LDC2002T01. http://www.cogsci.princeton.edu/˜wn/ 7 Acquired nouns are excluded from the consulting since many proper names are acquired but are not covered in WordNet. 8 English version: http://news.google.com/ Japanese version: http://news.google.com/nwshp?ned=jp AcknowledgmentThe research reported here was supported in part by a contract with the National Institute of Information and Communications Technology entitled "A study of speech dialogue translation technology based on a large corpus".AppendixA Samples of Acquired Words from MTCC and Their EvaluationB Samples of Acquired Nouns from Google News (E) and Their Evaluation Extracting paraphrases from a parallel corpus. R Barzilay, K Mckeown, Proc. of ACL-01. of ACL-01R. Barzilay and K. McKeown. Extracting paraphrases from a parallel corpus. In Proc. of ACL-01, pages 50-57, 2001. Survey of Text Mining Clustering, Classification, and Retrieval. M.W. BerrySpringerM.W. Berry, editor. Survey of Text Mining Clustering, Classification, and Re- trieval. Springer, 2004. A maximum-entropy-inspired parser. E Charniak, Proc. of the 1st Conference of the North American Chapter of the Association for Computational Linguistics. of the 1st Conference of the North American Chapter of the Association for Computational LinguisticsE. Charniak. A maximum-entropy-inspired parser. In Proc. of the 1st Conference of the North American Chapter of the Association for Computational Linguistics, 2000. WordNet: An Electronic Lexical Database. C Fellbaum, MIT PressC. Fellbaum. WordNet: An Electronic Lexical Database. MIT Press, 1998. Mathematical Structures of Language. Z Harris, Interscience PublishersZ. Harris. Mathematical Structures of Language. Interscience Publishers, 1968. Noun classification from predicate-argument structures. D Hindle, Proc. of ACL-90. of ACL-90D. Hindle. Noun classification from predicate-argument structures. In Proc. of ACL-90, pages 268-275, 1990. Automatic retrieval and clustering of similar words. D Lin, Proc. of COLING-ACL 98. of COLING-ACL 98D. Lin. Automatic retrieval and clustering of similar words. In Proc. of COLING- ACL 98, pages 768-774, 1998. Identifying synonyms among distributionally similar words. D Lin, S Zhao, L Qin, M Zhou, Proc. of the 18th International Joint Conference on Artificial Intelligence (IJCAI). of the 18th International Joint Conference on Artificial Intelligence (IJCAI)D. Lin, S. Zhao, L. Qin, and M. Zhou. Identifying synonyms among distributionally similar words. In Proc. of the 18th International Joint Conference on Artificial Intelligence (IJCAI), pages 1492-1493, 2003. C D Manning, H Schütze, Foundations of Statistical Natural Language Processing. MIT PressC.D. Manning and H. Schütze, editors. Foundations of Statistical Natural Language Processing, pages 265-314. MIT Press, 1999. Syntax-based alignment of multiple translations: Extracting paraphrases and generating new sentences. B Pang, K Knight, D Marcu, Proc. of HLT-NAACL 2003. of HLT-NAACL 2003B. Pang, K. Knight, and D. Marcu. Syntax-based alignment of multiple trans- lations: Extracting paraphrases and generating new sentences. In Proc. of HLT- NAACL 2003, pages 181-188, 2003. Distributional clustering of English words. F Pereira, N Tishby, L Lee, Proc. of ACL-93. of ACL-93F. Pereira, N. Tishby, and L. Lee. Distributional clustering of English words. In Proc. of ACL-93, pages 183-190, 1993. Roget's International Thesaurus. P M Roget, Thomas Y. CrowellP.M. Roget. Roget's International Thesaurus. Thomas Y. Crowell, 1946. Identifying synonymous expressions from a bilingual corpus for example-based machine translation. M Shimohata, E Sumita, Proc. of the 19th COLING Workshop on Machine Translation in Asia. of the 19th COLING Workshop on Machine Translation in AsiaM. Shimohata and E. Sumita. Identifying synonymous expressions from a bilin- gual corpus for example-based machine translation. In Proc. of the 19th COLING Workshop on Machine Translation in Asia, pages 20-25, 2002.

An Interactive Tool for Supporting Error Analysis for Text Mining

This demo abstract presents an interactive tool for supporting error analysis for text mining, which is situated within the Summarization Integrated Development Environment (SIDE). This freely downloadable tool was designed based on repeated experience teaching text mining over a number of years, and has been successfully tested in that context as a tool for students to use in conjunction with machine learning projects.

An Interactive Tool for Supporting Error Analysis for Text Mining Association for Computational LinguisticsCopyright Association for Computational LinguisticsJune 2010. 2010 Elijah Mayfield elijah@cmu.edu Language Technologies Institute Carnegie Mellon University 5000 Forbes Ave Pittsburgh15216PAUSA Carolyn Penstein-Rosé Language Technologies Institute Carnegie Mellon University 5000 Forbes Ave Pittsburgh15216PAUSA An Interactive Tool for Supporting Error Analysis for Text Mining Proceedings of the NAACL HLT 2010: Demonstration Session the NAACL HLT 2010: Demonstration SessionLos Angeles, California; cAssociation for Computational LinguisticsJune 2010. 2010 This demo abstract presents an interactive tool for supporting error analysis for text mining, which is situated within the Summarization Integrated Development Environment (SIDE). This freely downloadable tool was designed based on repeated experience teaching text mining over a number of years, and has been successfully tested in that context as a tool for students to use in conjunction with machine learning projects. Introduction In the past decade, more and more work in the language technologies community has shifted from work on formal, rule-based methods to work involving some form of text categorization or text mining technology. At the same time, use of this technology has expanded; where it was once accessible only to those within studying core language technologies, it is now almost ubiquitous. Papers involving text mining can currently be found even in core social science and humanities conferences. The authors of this demonstration are involved in regular teaching of an applied machine learning course, which attracts students from virtually every field, including a variety of computer science related fields, the humanities and social sciences, and the arts. In five years of teaching this course, what has emerged is the finding that the hardest skill to impart to students is the ability to do a good error analysis. In response to this issue, the interactive error analysis tool presented here was designed, developed, and successfully tested with students. In the remainder of this demo abstract, we offer an overview of the development environment that provides the context for this work. We then describe on a conceptual level the error analysis process that the tool seeks to support. Next, we step through the process of conducting an error analysis with the interface. We conclude with some directions for our continued work, based on observation of students' use of this interface. Overview of SIDE The interactive error analysis interface is situated within an integrated development environment for building summarization systems. Note that the SIDE (Kang et al., 2008) software and comprehensive user's manual are freely available for download 1 . We will first discuss the design of SIDE from a theoretical standpoint, and then explore the details of practical implementation. Design Goals SIDE was designed with the idea that documents, whether they are logs of chat discussions, sets of posts to a discussion board, or notes taken in a course, can be considered relatively unstructured. Nevertheless, when one thinks about their interpretation of a document, or how they would use the information found within a document, then a structure emerges. For example, an argument written in a paper often begins with a thesis statement, followed by supporting points, and finally a conclusion. A reader can identify with this structure even if there is nothing in the layout of the text that indicates that certain sentences within the argument have a different status from the others. Subtle cues in the language can be used to identify those distinct roles that sentences might play. Conceptually, then, the use of SIDE proceeds in two main parts. The first part is to construct filters that can impose that structure on the texts to be summarized, to identify the role a sentence is playing in a document; and the second part is constructing specifications of summaries that refer to that structure, such as subsets of extracted text or data visualizations. This demo is primarily concerned with supporting error analysis for text mining. Thus, the first of these two stages will be the primary focus. This approach to summarization was inspired by the process described in (Teufel and Moens, 2002). That work focused on the summarization of scientific articles to describe a new work in a way which rhetorically situates that work's contribution within the context of related prior work. This is done by first overlaying structure onto the documents to be summarized, categorizing the sentences they contain into one of a number of rhetorical functions. Once this structure is imposed, using the information it provides was shown to increase the quality of generated summaries. Building Text Mining Models with SIDE This demo assumes the user has already interacted with the SIDE text mining interface for model building, including feature extraction and machine learning, to set up a model. Defining this in SIDE terms, to train the system and create a model, the user first has to define a filter. Filters are trained using machine learning technology. Two customization options are available to analysts in this process. The first and possibly most important is the set of customization options that affect the design of the attribute space. The standard attribute space is set up with one attribute per unique feature -the value corresponds to the number of times that feature occurs in a text. Options include unigrams, bigrams, part-of-speech bigrams, stemming, and stopword removal. The next step is the selection of the machine learning algorithm that will be used. Dozens of op-tions are made available through the Weka toolkit (Witten and Frank, 2005), although some are more commonly used than others. The three options that are most recommended to analysts beginning work with machine learning are Naïve Bayes (a probabilistic model), SMO (Weka's implementation of Support Vector Machines), and J48, which is one of many Weka implementations of a Decision Tree learner. SMO is considered state-of-the-art for text classification, so we expect that analysts will frequently find that to be the best choice. As this error analysis tool is built within SIDE, we focus on applications to text mining. However, this tool can also be used on non-text data sets, so long as they are first preprocessed through SIDE. The details of our error analysis approach are not specific to any individual task or machine learning algorithm. High Level View of Error Analysis In an insightful usage of applied machine learning, a practitioner will design an approach that takes into account what is known about the structure of the data that is being modeled. However, typically, that knowledge is incomplete, and there is thus a good chance that the decisions that are made along the way are suboptimal. When the approach is evaluated, it is possible to determine based on the proportion and types of errors whether the performance is acceptable for the application or not. If it is not, then the practitioner should engage in an error analysis process to determine what is malfunctioning and what could be done to better model the structure in the data. In well-known machine learning toolkits such as Weka, some information is available about what errors are being made. Predictions can be printed out, to allow a researcher to identify how a document is being classified. One common format for summarizing these predictions is a confusion matrix, usually printed in a format like: appearing, it gives no indication of why the errors are being made. This is where a more extensive error analysis is necessary. Two common ways to approach this question are top down, which starts with a learned model, and bottom up, which starts with the confusion matrix from that model's performance estimate. In the first case, the model is examined to find the attributes that are treated as most important. These are the attributes that have the greatest influence on the predictions made by the learned model, and thus these attributes provide a good starting point. In the second case, the bottom-up case, one first examines the confusion matrix to identify large off-diagonal cells, which represent common confusions. The error analysis for any error cell is then the process of determining relations between three sets of text segments 2 related to that cell. Within the "classified as DR but actually PT" cell, for instance, error analysis would require finding what makes these examples most different from examples correctly classified as PT, and what makes these examples most similar to those correctly classified as DR. This can be done by identifying attributes that mostly strongly differentiate the first two sets, and attributes most similar between the first and third sets. An ideal approach would combine these two directions. Error Analysis Process Visitors to this demo will have the opportunity to experiment with the error analysis interface. It will be set up with multiple data sets and previously trained text mining models. These models can first be examined from the model building window, which contains information such as: • Global feature collection, listing all features that were included in the trained model. • Cross-validation statistics, including variance and kappa statistics, the confusion matrix and other general information. • Weights or other appropriate information for the text mining model that was trained. By moving to the error analysis interface, the user can explore a model more deeply. The first step is to select a model to examine. By default, all text segments that were evaluated in cross-validation display in a scrolling list in the bottom right corner of the window. Each row contains the text within a segment, and the associated feature vector. Users will first be asked to examine this data to understand the magnitude of the error analysis challenge. Clicking on a cell in the confusion matrix (at the top of the screen) will fill the scrolling list at the bottom left corner of the screen with the classified segments that fall in that cell. A comparison chooser dropdown menu gives three analysis options -full, horizontal, and vertical. By default, full comparison is selected, and shows all text segments used in training. The two additional modes of comparison allow some insight into what features are most representative of the subset of segments in that cell, compared to the correct predictions aligned with that cell (either vertically or horizontally within the confusion matrix). By switching to horizontal comparison, the scrolling list on the right changes to display only text segments that fall in the cell which is along the confusion matrix diagonal and horizontal to the selected cell. Switching to vertical comparison changes this list to display segments categorized in the cell which is along the diagonal and vertically aligned with the selected error cell. Once a comparison method is selected, there is a feature highlighting dropdown menu which is of use. The contents in this menu are sorted by degree of difference between the segments in the two lists at the bottom of the screen. This means, for a horizontal comparison, that features at the top of this list are the most different between the two cells (this difference is displayed in the menu). We compute this difference by the difference in expected (average) value for that feature between the two sets. In a vertical comparison, features are ranked by similarity, instead of difference. Once a feature is selected from this menu, two significant changes are made. The first is that a second confusion matrix appears, giving the confusion matrix values (mean and standard deviation) for the highlighted feature. The second is that the two segment lists are sorted according to the feature being highlighted. User interface design elements were important in this design process. One option available to users is the ability to "hide empty features," which removes features which did not occur at all in one or both of the sets being studied. This allows the user to focus on features which are most likely to be causing a significant change in a classifier's performance. It is also clear that the number of different subsets of classified segments can become very confusing, especially when comparing various types of error in one session. To combat this, the labels on the lists and menus will change to reflect some of this information. For instance, the left-hand panel gives the predicted and actual labels of the segments you have highlighted, while the right-hand panel is labelled with the name of the category of correct prediction you are comparing against. The feature highlighting dropdown menu also changes to reflect similar information about the type of comparison being made. Future Directions This error analysis tool has been used in the text mining unit for an Applied Machine Learning course with approximately 30 students. In contrast to previous semesters where the tool was not available to support error analysis, the instructor noticed that many more students were able to begin surpassing shallow observations, instead forming hypotheses about where the weaknesses in a model are, and what might be done to improve performance. Based on our observations, however, the error analysis support could still be improved by directing users towards features that not only point to differences and similarities between different subsets of instances, but also to more information about how features are being used in the trained model. This can be implemented either in algorithm-specific ways (such as displaying the weight of features in an SVM model) or in more generalizable formats, for instance, through information gain. Investigating how to score these general aspects, and presenting this information in an intuitive way, are directions for our continued development of this tool. lists, for example, that 19 text segments were classified as type DR but were actually type PT. While this gives a rough view of what errors are Figure 1 : 1The error analysis interface with key functionality locations highlighted. SIDE and its documentation are downloadable from http://www.cs.cmu.edu/˜cprose/SIDE.html Our interface assumes that the input text has been segmented already; depending on the task involved, these segments may correspond to a sentence, a paragraph, or even an entire document. AcknowledgementsThis research was supported by NSF Grant DRL-0835426. SIDE: The Summarization Integrated Development Environment. Proceedings of the Association for Computational Linguistics. Moonyoung Kang, Sourish Chaudhuri, Mahesh Joshi, Carolyn Penstein-Rosé, Demo AbstractsMoonyoung Kang, Sourish Chaudhuri, Mahesh Joshi, and Carolyn Penstein-Rosé 2008. SIDE: The Summa- rization Integrated Development Environment. Pro- ceedings of the Association for Computational Lin- guistics, Demo Abstracts. Summarizing Scientific Articles: Experiments with Relevance and Rhetorical Status. Simone Teufel, Marc Moens, Computational Linguistics. 281Simone Teufel and Marc Moens 2002. Summarizing Scientific Articles: Experiments with Relevance and Rhetorical Status. Computational Linguistics, Vol. 28, No. 1. Ian Witten, Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques. San Fransiscosecond editionIan Witten and Eibe Frank 2005. Data Mining: Prac- tical Machine Learning Tools and Techniques, second edition. Elsevier: San Fransisco.

Building and Evaluating a Distributional Memory for Croatian

We report on the first structured distributional semantic model for Croatian, DM.HR. It is constructed after the model of the English Distributional Memory (Baroni and Lenci, 2010), from a dependencyparsed Croatian web corpus, and covers about 2M lemmas. We give details on the linguistic processing and the design principles. An evaluation shows state-of-theart performance on a semantic similarity task with particularly good performance on nouns. The resource is freely available.

Building and Evaluating a Distributional Memory for Croatian August 4-9 Janšnajder jan.snajder@fer.hrpado@cl.uni-heidelberg.dezagic@ffzg.hr Sebastian Padó Institut für Computerlinguistik Heidelberg University 69120HeidelbergGermany Eljko Agić Faculty of Humanities and Social Sciences Ivana Lučića 3 University of Zagreb 10000ZagrebCroatia Faculty of Electrical Engineering and Computing Unska 3 University of Zagreb 10000ZagrebCroatia Building and Evaluating a Distributional Memory for Croatian Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics the 51st Annual Meeting of the Association for Computational LinguisticsSofia, BulgariaAugust 4-9 We report on the first structured distributional semantic model for Croatian, DM.HR. It is constructed after the model of the English Distributional Memory (Baroni and Lenci, 2010), from a dependencyparsed Croatian web corpus, and covers about 2M lemmas. We give details on the linguistic processing and the design principles. An evaluation shows state-of-theart performance on a semantic similarity task with particularly good performance on nouns. The resource is freely available. Introduction Most current work in lexical semantics is based on the Distributional Hypothesis (Harris, 1954), which posits a correlation between the degree of words' semantic similarity and the similarity of the contexts in which they occur. Using this hypothesis, word meaning representations can be extracted from large corpora. Words are typically represented as vectors whose dimensions correspond to context features. The vector similarities, which are interpreted as semantic similarities, are used in numerous applications (Turney and Pantel, 2010). Most vector spaces in current use are either wordbased (co-occurrence defined by surface window, context words as dimensions) or syntax-based (cooccurrence defined syntactically, syntactic objects as dimensions). Syntax-based models have several desirable properties. First, they are model to fine-grained types of semantic similarity such as predicate-argument plausibility (Erk et al., 2010). Second, they are more versatile - Baroni and Lenci (2010) have presented a generic framework, the Distributional Memory (DM), which is applicable to a wide range of tasks beyond word similarity. Third, they avoid the "syntactic assumption" inherent in word-based models, namely that context words are relevant iff they are in an n-word window around the target. This property is particularly relevant for free word order languages with many long distance dependencies and non-projective structure (Kübler et al., 2009). Their obvious problem, of course, is that they require a large parsed corpus. In this paper, we describe the construction of a Distributional Memory for Croatian (DM.HR), a free word order language. To do so, we parse hrWaC (Ljubešić and Erjavec, 2011), a 1.2B-token Croatian web corpus. We evaluate DM.HR on a synonym choice task, where it outperforms the standard bag-of-word model for nouns and verbs. Related Work Vector space semantic models have been applied to a number of Slavic languages, including Bulgarian (Nakov, 2001a), Czech (Smrž and Rychlý, 2001), Polish (Piasecki, 2009;Broda and Piasecki, 2008), and Russian (Nakov, 2001b;Mitrofanova et al., 2007). Previous work on distributional semantic models for Croatian dealt with similarity prediction (Ljubešić et al., 2008;Janković et al., 2011) and synonym detection (Karan et al., 2012), however using only wordbased and not syntactic-based models. So far the only DM for a language other than English is the German DM.DE by Padó and Utt (2012), who describe the process of building DM.DE and the evaluation on a synonym choice task. Our work is similar, though each language has its own challenges. Croatian, like other Slavic languages, has rich inflectional morphology and free word order, which lead to errors in linguistic processing and affect the quality of the DM. Distributional Memory DM represents co-occurrence information in a general, non-task-specific manner, as a tensor, i.e., a three-dimensional matrix, of weighted word-linkword tuples. Each tuple is mapped onto a number by scoring function σ : W × L × W → R + , that reflects the strength of the association. When a particular task is selected, a vector space for this task can be generated from the tensor by matricization. Regarding the examples from Section 1, synonym discovery would use a word by link-word space (W × LW ), which contains vectors for words w represented by pairs l, w of a link and a context word. Analogy discovery would use a word-word by link space (W W × L), which represents word pairs w 1 , w 2 by vectors over links l. The links can be chosen to model any relation of interest between words. However, as noted by Padó and Utt (2012), dependency relations are the most obvious choice. Baroni and Lenci (2010) introduce three dependency-based DM variants: De-pDM, LexDM, and TypeDM. DepDM uses links that correspond to dependency relations, with subcategorization for subject (subj tr and subj intr) and object (obj and iobj). Furthermore, all prepositions are lexicalized into links (e.g., sun, on, Sunday ). Finally, the tensor is symmetrized: for each tuple w 1 , l, w 2 , its inverse w 2 , l −1 , w 1 is included. The other two variants are more complex: LexDM uses more lexicalized links, encoding, e.g., lexical material between the words, while TypeDM extends LexDM with a scoring function based on lexical variability. Following the work of Padó and Utt (2012), we build a DepDM variant for DM.HR. Although Baroni and Lenci (2010) show that TypeDM can outperform the other two variants, DepDM often performs at a comparable level, while being much simpler to build and more efficient to compute. Building DM.HR To build DM.HR, we need to collect co-occurrence counts from a corpus. Since no sufficiently large suitable corpus exists for Croatian, we first explain how we preprocessed, tagged, and parsed the data. Corpus and preprocessing. We adopted hrWaC, the 1.2B-token Croatian web corpus (Ljubešić and Erjavec, 2011), as starting point. hrWaC was built with the aim of obtaining a cleaner-than-usual web corpus. To this end, a conservative boilerplate re-moval procedure was used; Ljubešić and Erjavec (2011) report a precision of 97.9% and a recall of 70.7%. Nonetheless, our inspection revealed that, apart from the unavoidable spelling and grammatical errors, hrWaC still contains non-textual content (e.g., code snippets and formatting structure), encoding errors, and foreign-language content. As this severely affects linguistic processing, we additionally filtered the corpus. First, we removed from hrWaC the content crawled from main discussion forum and blog websites. This content is highly ungrammatical and contains a lot of non-diacriticized text, typical for user-generated content. This step alone removed one third of the data. We processed the remaining content with a tokenizer and a sentence segmenter based on regular expressions, obtaining 66M sentences. Next, we applied a series of heuristic filters at the document-and sentence-level. At the document level, we discard all documents (1) whose length is below a specified threshold, (2) contain no diacritics, (3) contain no words from a list of frequent Croatian words, or (4) contain a single word from lists of distinctive foreign-language words (for Serbian). The last two steps serve to eliminate foreign-language content. In particular, the last step serves to filter out the text in Serbian, which at the sentence-level is difficult to automatically discriminate from Croatian. At the sentence-level, we discard sentences that are (1) shorter than a specified threshold, (2) contain non-standard symbols, (3) contain non-diacriticized Croatian words, or (4) contain too many foreign words from a list of foreign-language words (for English and Slovene). The last step filters out specifically the sentences in English and Slovene, as we found that these often occur mixed with text in Croatian. The final filtered version of hrWaC contains 51M sentences and 1.2B tokens. The corpus is freely available for download, along with a more detailed description of the preprocessing steps. 1 Tagging, lemmatization, and parsing. For morphosyntactic (MSD) tagging, lemmatization, and dependency parsing of hrWaC, we use freely available tools with models trained on the new SETimes Corpus of Croatian (SETIMES.HR), based on the Croatian part of the SETimes parallel corpus. 2 SE-TIMES.HR and the derived tools are prototypes (Erjavec, 2012), with the help of the Croatian Lemmatization Server (Tadić, 2005). It is used also as a basis for a novel formalism for syntactic annotation and dependency parsing of Croatian (Agić and Merkler, 2013). On the basis of previous evaluation for Croatian (Agić et al., 2008;Agić et al., 2009;Agić, 2012) and availability and licensing considerations, we chose HunPos tagger (Halácsy et al., 2007), CST lemmatizer (Ingason et al., 2008), and MST-Parser (McDonald et al., 2006) to process hrWaC. We evaluated the tools on 100-sentence test sets from SETIMES.HR and Wikipedia; performance on Wikipedia should be indicative of the performance on a cross-domain dataset, such as hrWaC. In Table 1 we show lemmatization and tagging accuracy, as well as dependency parsing accuracy in terms of labeled attachment score (LAS). The results show that lemmatization, tagging and parsing accuracy improves on the state of the art for Croatian. The SETIMES.HR dependency parsing models are publicly available. 3 Syntactic patterns. We collect the co-occurrence counts of tuples using a set of syntactic patterns. The patterns effectively define the link types, and hence the dimensions of the semantic space. Similar to previous work, we use two sorts of links: unlexicalized and lexicalized. For unlexicalized links, we use ten syntactic patterns. These correspond to the main dependency relations produced by our parser: Pred for predicates, Atr for attributes, Adv for adverbs, Atv for verbal complements, Obj for objects, Prep for prepositions, and Pnom for nominal predicates. We subcategorized the subject relation into Sub tr (sub-3 http://zeljko.agic.me/resources/ (2012), we use Verb as an underspecified link between subjects and objects linked by non-auxiliary verbs. For lexicalized links, we use two more extraction patterns for prepositions and verbs. Prepositions are directly lexicalized as links; e.g., mjesto, na, sunce ( place, on, sun ). The same holds for nonauxiliary verbs linking subjects to objects; e.g., država, kupiti, količina ( state, buy, amount ). Tuple extraction and scoring. The overall quality of the DM.HR depends on the accuracy of extracted tuples, which is affected by all preprocessing steps. We computed the performance of tuple extraction by evaluating a sample of tuples extracted from a parsed version of SETIMES.HR against the tuples extracted from the SETIMES.HR gold annotations (we use the same sample as for tagging and parsing performance evaluation). Table 2 shows Precision, Recall, and F 1 score. Overall, we achieve the best performance on the Atr links, followed by Pred links. The performance is generally higher on unlexicalized links than on lexicalized links (note that performance on unlexical- ized Verb links is identical to overall performance on lexicalized verb links). The overall F 1 score of tuple extraction is 74.6%. Following DM and DM.DE, we score each extracted tuple using Local Mutual Information (LMI) (Evert, 2005): LMI(i, j, k) = f (i, j, k) log P (i, j, k) P (i)P (j)P (k) For a tuple (w 1 , l, w 2 ), LMI scores the association strength between word w 1 and word w 2 via link l by comparing their joint distribution against the distribution under the independence assumption, multiplied with the observed frequency f (w 1 , l, w 2 ) to discount infrequent tuples. The probabilities are computed from tuple counts as maximum likelihood estimates. We exclude from the tensor all tuples with a negative LMI score. Finally, we symmetrize the tensor by introducing inverse links. Model statistics. The resulting DM.HR tensor consists of 2.3M lemmas, 121M links and 165K link types (including inverse links). On average, each lemma has 53 links. This makes DM.HR more sparse than English DM (796 link types), but less sparse than German DM (220K link types; 22 links per lemma). Table 3 shows an example of the extracted tuples for the verb kupiti (to buy). DM.HR tensor is freely available for download. 4 5 Evaluating DM.HR Task. We present a pilot evaluation DM.HR on a standard task from distributional semantics, namely synonym choice. In contrast to tasks like predicting word similarity We use the dataset created by Karan et al. (2012), with more than 11,000 synonym choice questions. Each question consists of one target word (nouns, verbs, and adjectives) with Table 4: Results on synonym choice task four synonym candidates (one is correct). The questions were extracted automatically from a machinereadable dictionary of Croatian. An example item is težak (farmer): poljoprivrednik (farmer), umjetnost (art), radijacija (radiation), bod (point). We sampled from the dataset questions for nouns, verbs, and adjectives, with 1000 questions each. 5 Additionally, we manually corrected some errors in the dataset, introduced by the automatic extraction procedure. To make predictions, we compute pairwise cosine similarities of the target word vectors with the four candidates and predict the candidate(s) with maximal similarity (note that there may be ties). Evaluation. Our evaluation follows the scheme developed by Mohammad et al. (2007), who define accuracy as the average number of correct predictions per covered question. Each correct prediction with a single most similar candidate receives a full credit (A), while ties for maximal similarity are discounted (B: two-way tie, C: three-way tie, D: four-way tie): A + 1 2 B + 1 3 C + 1 4 D. We consider a question item to be covered if the target and at least one answer word are modeled. In our experiments, ties occur when vector similarities are zero for all word pairs (due to vector sparsity). Note that a random baseline would perform at 0.25 accuracy. As baseline to compare against the DM.HR, we build a standard bag-of-word model from the same corpus. It uses a ±5-word within-sentence context window, and the 10,000 most frequent context words (nouns, adjectives, and verbs) as dimensions. We also compare against BOW-LSA, a state-ofthe-art synonym detection model from Karan et al. (2012), which uses 500 latent dimensions and paragraphs as contexts. We determine the significance of differences between the models by computing 95% confidence intervals with bootstrap resampling (Efron and Tibshirani, 1993). Results. Table 4 shows the results for the three considered models on nouns (N), adjectives (A), and verbs (V). The performance of BOW-LSA differs slightly from that reported by Karan et al. (2012), because we evaluate on a sample of their dataset. DM.HR outperforms the baseline BOW model for nouns and verbs (differences are significant at p < 0.05). Moreover, on these categories DM.HR performs slightly better than BOW-LSA, but the differences are not statistically significant. Conversely, on adjectives BOW-LSA performs slightly better than DM.HR, but the difference is again not statistically significant. All models achieve comparable and almost perfect coverage on this dataset (BOW-LSA achieves complete coverage because of the way how the original dataset was filtered). Overall, the biggest improvement over the baseline is achieved for nouns. Nouns occur as heads and dependents of many link types (unlexicalized and lexicalized), and are thus well represented in the semantic space. On the other hand, adjectives seem to be less well modeled. Although the majority of adjectives occur as heads or dependents of the Atr relation, for which extraction accuracy is the highest (cf. Table 2), it is likely that a single link type is not sufficient. As noted by a reviewer, more insight could perhaps be gained by comparing the predictions of BOW-LSA and DM.HR models. The generally low performance on verbs suggests that their semantic is not fully covered in word-and syntax-based spaces. Conclusion We have described the construction of DM.HR, a syntax-based distributional memory for Croatian built from a dependency-parsed web corpus. To the best of our knowledge, DM.HR is the first freely available distributional memory for a Slavic language. We have conducted a preliminary evaluation of DM.HR on a synonym choice task, where DM.HR outperformed the bag-of-word model and performed comparable to an LSA model. This work provides a starting point for a systematic study of dependency-based distributional semantics for Croatian and similar languages. Our first priority will be to analyze how corpus preprocessing and the choice of link types relates to model performance on different semantic tasks. Better modeling of adjectives and verbs is also an important topic for future research. Table 2 : 2predati (to hand in/out) vs. predati se (to surrender). With subject subcategorization, reflexive and irreflexive readings will have different tensor representations; e.g., student, Subj tr, zadaća ( student, Subj tr, homework ) vs. trupe, Subj intr, napadač ( troops, Subj intr, invadors ). Finally, similar to Padó and UttTuple extraction performance on SE- TIMES.HR jects of transitive verbs) and Sub intr (subject of intransitive verbs). The motivation for this is better modeling of verb semantics by capturing diathe- sis alternations. In particular, for many Croatian verbs reflexivization introduces a meaning shift, e.g., Table 3 : 3Top 16 LMI-scored tuples for the verb kupiti (to buy) http://takelab.fer.hr/data 2 http://www.nljubesic.net/resources/ corpora/setimes/ http://takelab.fer.hr/dmhr Available at: http://takelab.fer.hr/crosyn AcknowledgmentsThe first author was supported by the Croatian Science Foundation (project 02.03/162: "Derivational Semantic Models for Information Retrieval"). 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Topic Extraction from Microblog Posts Using Conversation Structures

Conventional topic models are ineffective for topic extraction from microblog messages since the lack of structure and context among the posts renders poor message-level word co-occurrence patterns. In this work, we organize microblog posts as conversation trees based on reposting and replying relations, which enrich context information to alleviate data sparseness. Our model generates words according to topic dependencies derived from the conversation structures. In specific, we differentiate messages as leader messages, which initiate key aspects of previously focused topics or shift the focus to different topics, and follower messages that do not introduce any new information but simply echo topics from the messages that they repost or reply. Our model captures the different extents that leader and follower messages may contain the key topical words, thus further enhances the quality of the induced topics. The results of thorough experiments demonstrate the effectiveness of our proposed model.

Topic Extraction from Microblog Posts Using Conversation Structures Association for Computational LinguisticsCopyright Association for Computational LinguisticsAugust 7-12, 2016. 2016 Jing Li The Chinese University of Hong Kong Shatin, Hong KongN.T MoE Key Laboratory of High Confidence Software Technologies China Ming Liao The Chinese University of Hong Kong Shatin, Hong KongN.T MoE Key Laboratory of High Confidence Software Technologies China Wei Gao Qatar Computing Research Institute Hamad Bin Khalifa University DohaQatar Yulan He School of Engineering and Applied Science Aston University UK Kam-Fai Wong kfwong@se.cuhk.edu.hk1 The Chinese University of Hong Kong Shatin, Hong KongN.T MoE Key Laboratory of High Confidence Software Technologies China Topic Extraction from Microblog Posts Using Conversation Structures Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics the 54th Annual Meeting of the Association for Computational LinguisticsBerlin, GermanyAssociation for Computational LinguisticsAugust 7-12, 2016. 2016 Conventional topic models are ineffective for topic extraction from microblog messages since the lack of structure and context among the posts renders poor message-level word co-occurrence patterns. In this work, we organize microblog posts as conversation trees based on reposting and replying relations, which enrich context information to alleviate data sparseness. Our model generates words according to topic dependencies derived from the conversation structures. In specific, we differentiate messages as leader messages, which initiate key aspects of previously focused topics or shift the focus to different topics, and follower messages that do not introduce any new information but simply echo topics from the messages that they repost or reply. Our model captures the different extents that leader and follower messages may contain the key topical words, thus further enhances the quality of the induced topics. The results of thorough experiments demonstrate the effectiveness of our proposed model. Introduction The increasing popularity of microblog platforms results in a huge volume of user-generated short posts. Automatically modeling topics out of such massive microblog posts can uncover the hidden semantic structures of the underlying collection and can be useful to downstream applications such as microblog summarization (Harabagiu and Hickl, 2011), user profiling (Weng et al., 2010), event tracking (Lin et al., 2010) and so on. Popular topic models, like Probabilistic Latent Semantic Analysis (pLSA) (Hofmann, 1999) * * Part of this work was conducted when the first author was visiting Aston University. and Latent Dirichlet Allocation (LDA) (Blei et al., 2003b), model the semantic relationships between words based on their co-occurrences in documents. They have demonstrated their success in conventional documents such as news reports and scientific articles, but perform poorly when directly applied to short and colloquial microblog content due to severe sparsity in microblog messages (Wang and McCallum, 2006;Hong and Davison, 2010). A common way to deal with short text sparsity is to aggregate short messages into long pseudodocuments. Most of the studies heuristically aggregate messages based on authorship (Zhao et al., 2011;Hong and Davison, 2010), shared words (Weng et al., 2010), or hashtags (Ramage et al., 2010;Mehrotra et al., 2013). Some works directly take into account the word relations to alleviate document-level word sparseness (Yan et al., 2013;Sridhar, 2015). More recently, a self-aggregation-based topic model called SATM (Quan et al., 2015) was proposed to aggregate texts jointly with topic inference. However, we argue that the existing aggregation strategies are suboptimal for modeling topics in short texts. Microblogs allow users to share and comment on messages with friends through reposting or replying, similar to our everyday conversations. Intuitively, the conversation structures can not only enrich context, but also provide useful clues for identifying relevant topics. This is nonetheless ignored in previous approaches. Moreover, the occurrence of non-topic words such as emotional, sentimental, functional and even meaningless words are very common in microblog posts, which may distract the models from recognizing topic-related key words and thus fail to produce coherent and meaningful topics. We propose a novel topic model by utilizing the structures of conversations in microblogs. We link microblog posts using reposting and replying rela-tions to build conversation trees. Particularly, the root of a conversation tree refers to the original post and its edges represent the reposting/replying relations. [O] Just an hour ago, a series of coordinated terrorist attacks occurred in Paris !!! [R2] Gunmen and suicide bombers hit a concert hall. More than 100 are killed already. [R1] OMG! I can't believe it's real. Paris?! I've just been there last month. [R3] Oh no! @BonjourMarc r u OK? please reply me for god's sake!!! . These messages are named as leaders, which contain salient content in topic description, e.g., the italic and underlined words in Figure 1. The remaining messages, named as followers, do not raise new issues but simply respond to their reposted or replied messages following what has been raised by the leaders and often contain non-topic words, e.g., OMG, OK, agree, etc. Conversation tree structures from microblogs have been previously shown helpful to microblog summarization (Li et al., 2015), but have never been explored for topic modeling. We follows Li et al. (2015) to detect leaders and followers across paths of conversation trees using Conditional Random Fields (CRF) trained on annotated data. The detected leader/follower information is then incorporated as prior knowledge into our proposed topic model. Our experimental results show that our model, which captures parent-child topic correlations in conversation trees and generates topics by considering messages being leaders or followers separately, is able to induce high-quality topics and outperforms a number of competitive baselines. In summary, our contributions are three-fold: • We propose a novel topic model, which ex-plicitly exploits the topic dependencies contained in conversation structures to enhance topic assignments. • Our model differentiates the generative process of topical and non-topic words, according to the message where a word is drawn from being a leader or a follower. This helps the model distinguish the topic-specific information from background noise. • Our model outperforms state-of-the-art topic models when evaluated on a large real-world microblog dataset containing over 60K conversation trees, which is publicly available 1 . Related Works Topic models aim to discover the latent semantic information, i.e., topics, from texts and have been extensively studied. One of the most popular and well-known topic models is LDA (Blei et al., 2003b). It utilizes Dirichlet priors to generate document-topic and topic-word distributions, and has been shown effective in extracting topics from conventional documents. Nevertheless, prior research has demonstrated that standard topic models, essentially focusing on document-level word co-occurrences, are not suitable for short and informal microblog messages due to severe data sparsity exhibited in short texts (Wang and McCallum, 2006;Hong and Davison, 2010). Therefore, how to enrich and exploit context information becomes a main concern. Weng et al. (2010), Hong et al. (2010) andZhao et al. (2011) first heuristically aggregated messages posted by the same user or sharing the same words before applying classic topic models to extract topics. However, such a simple strategy poses some problems. For example, it is common that a user has various interests and posts messages covering a wide range of topics. Ramage et al. (2010) and Mehrotra et al. (2013) used hashtags as labels to train supervised topic models. But these models depend on large-scale hashtag-labeled data for model training, and their performance is inevitably compromised when facing unseen topics irrelevant to any hashtag in training data due to the rapid change and wide variety of topics in social media. SATM (Quan et al., 2015) combined short texts aggregation and topic induction into a unified model. But in their work, no prior knowledge was given to ensure the quality of text aggregation, which therefore can affect the performance of topic inference. In this work, we organize microblog messages as conversation trees based on reposting/reply relations, which is a more advantageous message aggregation strategy. Another line of research tackled the word sparseness by modeling word relations instead of word occurrences in documents. For example, Gaussian Mixture Topic Model (GMTM) (Sridhar, 2015) utilized word embeddings to model the distributional similarities of words and then inferred clusters of words represented by word distributions using Gaussian Mixture Model (GMM) that capture the notion of latent topics. However, GMTM heavily relies on meaningful word embeddings that require a large volume of high-quality external resources for training. Biterm Topic Model (BTM) (Yan et al., 2013) directly explores unordered word-pair cooccurrence patterns in each individual message. Our model learns topics from aggregated messages based on conversation trees, which naturally provide richer context since word co-occurrence patterns can be captured from multiple relevant messages. LeadLDA Topic Model In this section, we describe how to extract topics from a microblog collection utilizing conversation tree structures, where the trees are organized based on reposting and replying relations among the messages 2 . To identify key topic-related content from colloquial texts, we differentiate the messages as leaders and followers. Following Li et al. (2015), we extract all root-to-leaf paths on conversation trees and utilize the state-of-the-art sequence learning model CRF (Lafferty et al., 2001) to detect the leaders 3 . As a result, the posterior probability of each node being a leader or follower is obtained by averaging the different marginal probabilities of the same node over all the tree paths that contain the node. Then, the obtained probability distribution is considered as the observed prior variable input into our model. 2 Reposting/replying relations are straightforward to obtain by using microblog APIs from Twitter and Sina Weibo. 3 The CRF model for leader detection was trained on a public corpus with all the messages annotated on the tree paths. Details are described in Section 4. Topics and Conversation Trees Previous works (Zhao et al., 2011;Yan et al., 2013;Quan et al., 2015) have proven that assuming each short post contains a single topic is useful to alleviate the data sparsity problem. Thus, given a corpus of microblog posts organized as conversation trees and the estimated leader probabilities of tree nodes, we assume that each message only contains a single topic and a tree covers a mixture of multiple topics. Since leader messages subsume the content of their followers, the topic of a leader can be generated from the topic distribution of the entire tree. Consequently, the topic mixture of a conversation tree is determined by the topic assignments to the leader messages on it. The topics of followers, however, exhibit strong and explicit dependencies on the topics of their ancestors. So, their topics need to be generated in consideration of local constraints. Here, we mainly address how to model the topic dependencies of followers. Enlighten by the general Structural Topic Model (strTM) (Wang et al., 2011), which incorporates document structures into topic model by explicitly modeling topic dependencies between adjacent sentences, we exploit the topical transitions between parents and children in the trees for guiding topic assignments. Intuitively, the emergence of a leader results in potential topic shift. It tends to weaken the topic similarities between the emerging leaders and their predecessors. For example, [R7] in Figure 1 transfers the topic to a new focus, thus weakens the tie with its parent. We can simplify our case by assuming that followers are topically responsive just up to (hence not further than) their nearest ancestor leaders. Thus, we can dismantle each conversation tree into forest by removing the links between leaders and their parents hence producing a set of subgraphs like [R2]-[R6] and [R7]-[R9] in Figure 1. Then, we model the internal topic dependencies within each subgraph by inferring the parent-child topic transition probabilities satisfying the first-order Markov properties in a similar way as estimating the transition distribution of adjacent sentences in strTM (Wang et al., 2011). At topic assignment stage, the topic of a follower will be assigned by referring to its parent's topic and the transition distribution that captures topic similarities of followers to their parents (see Section 3.2). In addition, every word in the corpus is either a topical or non-topic (i.e., background) word, which highly depends on whether it occurs in a leader or a follower message. Figure 2 illustrates the graphical model of our generative process, which is named as LeadLDA. Topic Modeling Formally, we assume that the microblog posts are organized as T conversation trees. Each tree t contains M t message nodes and each message m contains N t,m words in the vocabulary. The vocabulary size is V and there are K topics embedded in the corpus represented by word distribution φ k ∼ Dir(β) (k = 1, 2, ..., K). Also, a background word distribution φ B ∼ Dir(β) is included to capture the general information, which is not topic specific. φ k and φ B are multinomial distributions over the vocabulary. A tree t is modeled as a mixture of topics θ t ∼ Dir(α) and any message m on t is assumed to contain a single topic z t,m ∈ {1, 2, ..., K}. (1) Topic assignments: The topic assignments of LeadLDA is inspired by that combines syntactic and semantic dependencies between words. LeadLDA integrates the outcomes of leader detection with a binomial switcher y t,m ∈ {0, 1} indicating whether m is a leader (y t,m = 1) or a follower (y t,m = 0), given each message m on the tree t. y t,m is parameterized by its leader probability l t,m , which is the posterior probability output from the leader detection model and serves as an observed prior variable. According to the notion of leaders, they initiate key aspects of previously discussed topics or signal a new topic shifting the focus of its descendant followers. So, the topics of leaders on tree t are directly sampled from the topic mixture θ t . To model the internal topic correlations within the subgraph of conversation tree consisting of a leader and all its followers, we capture parentchild topic transitions π k ∼ Dir(γ), which is a distribution over K topics, and use π k,j to denote the probability of a follower assigned topic j when the topic of its parent is k. Specifically, if message m is sampled as a follower and the topic assignment to its parent message is z t,p(m) , where p(m) indexes the parent of m, then z t,m (i.e., the topic of m) is generated from topic transition distribution π z t,p(m) . In particular, since the root of a conversation tree has no parent and can only be a leader, we make the leader probability l t,root = 1 to force its topic only to be generated from the topic distribution of tree t. (2) Topical and non-topic words: We separately model the distributions of leader and follower messages emitting topical or non-topic words with τ 0 and τ 1 , respectively, both of which are drawn from a symmetric Beta prior parametererized by δ. Specifically, for each word n in message m on tree t, we add a binomial background switcher x t,m,n controlled by whether m is a leader or a follower, i.e., x t,m,n ∼ Bi(τ yt,m ), which indicates n is a topical word if x t,m,n = 0 or a background word if x t,m,n = 1, and x t,m,n controls n to be generated from the topic-word distribution φ zt,m , where z t,m is the topic of m, or from background word distribution φ B modeling non-topic information. (3) Generation process: To sum up, conditioned on the hyper-parameters Θ = (α, β, γ, δ), the generation process of a conversation tree t can be described as follows: • Draw θ t ∼ Dir(α) • For message m = 1 to M t on tree t -Draw y t,m ∼ Bi(l t,m ) -If y t,m == 1 * Draw z t,m ∼ M ult(θ t ) -If y t,m == 0 * Draw z t,m ∼ M ult(π z t,p(m) ) -For word n = 1 to N t,m in m * Draw x t,m,n ∼ Bi(τ yt,m ) * If x t,m,n == 0 · Draw w t,m,n ∼ M ult(φ zt,m ) * If x t,m,n == 1 · Draw w t,m,n ∼ M ult(φ B ) C LB s,(r) # of words with background switchers assigned as r and occurring in messages with leader switchers s. C LB s,(·) # of words occurring in messages whose leader switchers are s, i.e., r∈{0,1} C LB s,(r) . N B (r) # of words occurring in message (t, m) and with background switchers assigned as r. N B (·) # of words in message (t, m), i.e., N B (·) = r∈{0,1} N B (r) . C T W k,(v) # of words indexing v in vocabulary, sampled as topic (nonbackground) words, and occurring in messages assigned topic k. C T W k,(·) # of words assigned as topic (non-background) word and occurring in messages assigned topics k, i.e., C T W k,(·) = V v=1 C T W k,(v) . N W (v) # of words indexing v in vocabulary that occur in message (t, m) and are assigned as topic (non-background) word. N W (·) # of words assigned as topic (non-background) words and occurring in message (t, m), i.e., N W (·) = V v=1 N W (v) . C T R i,(j) # of messages sampled as followers and assigned topic j, whose parents are assigned topic i. C T R i,(·) # of messages sampled as followers whose parents are as- signed topic i, i.e., C T R i,(·) = K j=1 C T R i,(j) . I(·) An indicator function, whose value is 1 when its argument inside () is true, and 0 otherwise. N CT (j) # of messages that are children of message (t, m), sampled as followers and assigned topic j. N CT (·) # of message (t, m)'s children sampled as followers, i.e., N CT (·) = K j=1 N CT (j) C T T t,(k) # of messages on conversation tree t sampled as leaders and assigned topic k. C T T t,(·) # of messages on conversation tree t sampled as leaders, i.e., C T T t,(·) = K k=1 C T T t,(k) C BW (v) # of words indexing v in vocabulary and assigned as background (non-topic) words C BW (·) # of words assigned as background (non-topic) words, i.e., C BW (1) and (2). (t, m): message m on conversation tree t. (·) = V v=1 C BW (v) Inference for Parameters We use collapsed Gibbs Sampling (Griffiths, 2002) to carry out posterior inference for parameter learning. The hidden multinomial variables, i.e., message-level variables (y and z) and wordlevel variables (x) are sampled in turn, conditioned on a complete assignment of all other hidden variables. Due to the space limitation, we leave out the details of derivation but give the core formulas in the sampling steps. We first define the notations of all variables needed by the formulation of Gibbs sampling, which are described in Table 1. In particular, the various C variables refer to counts excluding the message m on conversation tree t. For each message m on a tree t, we sample the leader switcher y t,m and topic assignment z t,m according to the following conditional probability distribution: p(yt,m = s, zt,m = k|y ¬(t,m) , z ¬(t,m) , w, x, l, Θ) ∝ Γ(C LB s,(·) + 2δ) Γ(C LB s,(·) + N B (·) + 2δ) r∈{0,1} Γ(C LB s,(r) + N B (r) + δ) Γ(C LB s,(r) + δ) · Γ(C T W k,(·) + V β) Γ(C T W k,(·) + N W (·) + V β) V v=1 Γ(C T W k,(v) + N W (v) + β) Γ(C T W k,(v) + β) ·g(s, k, t, m)(1) where g(s, k, t, m) takes different forms depending on the value of s: g(0, k, t, m) = Γ(C T R z t,p(m) ,(·) + Kγ) Γ(C T R z t,p(m) ,(·) + I(z t,p(m) = k) + Kγ) · Γ(C T R k,(·) + Kγ) Γ(C T R k,(·) + I(z t,p(m) = k) + N CT (·) + Kγ) · K j=1 Γ(C T R k,(j) + N CT (j) + I(z t,p(m) = j = k) + γ) Γ(C T R k,(j) + γ) · Γ(C T R z t,p(m) ,(k) + I(z t,p(m) = k) + γ) Γ(C T R z t,p(m) ,(k) + γ) · (1 − lt,m) and g(1, k, t, m) = C T T t,(k) + α C T T t,(·) + Kα · l t,m For each word n in m on t, the sampling formula of its background switcher is given as the following: p(xt,m,n = r|x ¬(t,m,n) , y, z, w, l, Θ) ∝ C LB y t,m ,(r) + δ C LB y t,m ,(·) + 2δ · h(r, t, m, n) where h(r, t, m, n) =      C T W z t,m ,(w t,m,n ) +β C T W z t,m ,(·) +V β if r = 0 C BW (w t,m,n ) +β C BW (·) +V β if r = 1 Data Collection and Experiment Setup To evaluate our LeadLDA model, we conducted experiments on real-world microblog dataset collected from Sina Weibo that has the same 140character limitation and shares the similar market penetration as Twitter (Rapoza, 2011). For the hyper-parameters of LeadLDA, we fixed α = 50/K, β = 0.1, following the common practice in previous works Quan et al., 2015). Since there is no analogue of γ and δ in prior works, where γ controls topic dependencies of follower messages to their ancestors and δ controls the different tendencies of leaders and followers covering topical and nontopic words. We tuned γ and δ by grid search on a large development set containing around 120K posts and obtained γ = 50/K, δ = 0.5. Because the content of posts are often incomplete and informal, it is difficult to manually annotate topics in a large scale. Therefore, we follow Yan et al. (2013) to utilize hashtags led by '#', which are manual topic labels provided by users, as ground-truth categories of microblog messages. We collected the real-time trending hashtags on Sina Weibo and utilized the hashtag-search API 4 to crawl the posts matching the given hashtag queries. In the end, we built a corpus containing 596,318 posts during May 1 -July 31, 2014. To examine the performance of models on various topic distributions, we split the corpus into 3 datasets, each containing messages of one month. Similar to Yan et al. (2013), for each dataset, we manually selected 50 frequent hashtags as topics, e.g. #mh17, #worldcup, etc. The experiments were conducted on the subsets of posts with the selected hashtags. Table 2 shows the statistics of the three subsets used in our experiments. We preprocessed the datasets before topic extraction in the following steps: 1) Use FudanNLP toolkit (Qiu et al., 2013) for word segmentation, stop words removal and POS tagging for Chinese Weibo messages; 2) Generate a vocabulary for each dataset and remove words occurring less than 5 times; 3) Remove all hashtags in texts before input them to models, since the models are expected to extract topics without knowing the hashtags, which are ground-truth topics; 4) For LeadLDA, we use the CRF-based leader detection model (Li et al., 2015) to classify messages as leaders and followers. The leader detection model was implemented by using CRF++ 5 , which was trained on the public dataset composed of 1,300 conversation paths and achieved state-of-the-art 73.7% F1score of classification accuracy (Li et al., 2015 Experimental Results We evaluated topic models with two sets of K, i.e., the number of topics. One is K = 50, to match the count of hashtags following Yan et al. (2013), and the other is K = 100, much larger than the "real" number of topics. We compared LeadLDA with the following 5 state-of-the-art basedlines. TreeLDA: Analogous to Zhao et al. (2011), where they aggregated messages posted by the same author, TreeLDA aggregates messages from one conversation tree as a pseudo-document. Additionally, it includes a background word distribution to capture non-topic words controlled by a general Beta prior without differentiating leaders and followers. TreeLDA can be considered as a degeneration of LeadLDA, where topics assigned to all messages are generated from the topic distributions of the conversation trees they are on. StructLDA: It is another variant of LeadLDA, where topics assigned to all messages are generated based on topic transitions from their parents. The strTM (Wang et al., 2011) utilized a similar model to capture the topic dependencies of adjacent sentences in a document. Following strTM, we add a dummy topic T start emitting no word to the "pseudo parents" of root messages. Also, we add the same background word distribution to capture non-topic words as TreeLDA does. The hyper-parameters of BTM, SATM and GMTM were set according to the best hyperparameters reported in their original papers. For TreeLDA and StructLDA, the parameter settings were kept the same as LeadLDA since they are its variants. And the background switchers were parameterized by symmetric Beta prior on 0.5, following Chemudugunta et al. (2006). We ran Gibbs samplings (in LeadLDA, TreeLDA, StructLDA, BTM and SATM) and EM algorithm (in GMTM) with 1,000 iterations to ensure convergence. Topic model evaluation is inherently difficult. In previous works, perplexity is a popular metric to evaluate the predictive abilities of topic models given held-out dataset with unseen words (Blei et al., 2003b). However, Chang et al. (2009) have demonstrated that models with high perplexity do not necessarily generate semantically coherent topics in human perception. Therefore, we conducted objective and subjective analysis on the coherence of produced topics. Objective Analysis The quality of topics is commonly measured by coherence scores (Mimno et al., 2011), assuming that words representing a coherent topic are likely to co-occur within the same document. However, due to the severe sparsity of short text posts, we modify the calculation of commonly-used topic coherence measure based on word co-occurrences in messages tagged with the same hashtag, named as hashtag-document, assuming that those messages discuss related topics 8 . Specifically, we calculate the coherence score of a topic given the top N words ranked by likelihood as below: C = 1 K · K k=1 N i=2 i−1 j=1 log D(w k i , w k j ) + 1 D(w k j ) ,(3) where w k i represents the i-th word in topic k ranked by p(w|k), D(w k i , w k j ) refers to the count of hashtag-documents where word w k i and w k j cooccur, and D(w k i ) denotes the number of hashtagdocuments that contain word w k i . Table 3 shows the absolute values of C scores for topics produced on three evaluation datasets (May, June and July), and the top 10, 15, 20 words of topics were selected for evaluation. Lower scores indicate better coherence in the induced topic. We have the following observations: • GMTM gave the worst coherence scores, which may be ascribed to its heavy reliance on relevant large-scale high-quality external data, with- 8 We sampled posts and their corresponding hashtags in our evaluation set and found only 1% mismatch. out which the trained word embedding model failed to capture meaningful semantic features for words, and hence could not yield coherent topics. • TreeLDA and StructLDA produced competitive results compared to the state-of-the-art baseline models, which indicates the effectiveness of using conversation structures to enrich context and thus generate topics of reasonably good quality. • The coherence of topics generated by LeadLDA outperformed all the baselines on the three datasets, most of time by large margins and was only outperformed by BTM on the May dataset when K = 50 and N = 10. The generally higher performance of LeadLDA is due to three reasons: 1) It effectively identifies topics using the conversation tree structures, which provide richer context information; 2) It jointly models the topics of leaders and the topic dependencies of other messages on a tree. TreeLDA and StructLDA, each only considering one of the factors, performed worse than LeadLDA; 3) LeadLDA separately models the probabilities of leaders and followers containing topical or nontopic words while the baselines only model the general background information regardless of the different types of messages. This implies that leaders and followers do have different capacities in covering key topical words or background noise, which is useful to identify key words for topic representation. Subjective Analysis To evaluate the coherence of induced topics from human perspective, we invited two annotators to subjectively rate the quality of every topic (by displaying the top 20 words) generated by different models on a 1-5 Likert scale. A higher rating indicates better quality of topics. The Fless's Kappa of annotators' ratings measured for various models on different datasets given K = 50 and 100 range from 0.62 to 0.70, indicating substantial agreements (Landis and Koch, 1977). Table 4 shows the overall subjective ratings. We noticed that humans preferred topics produced given K = 100 to K = 50, but coherence scores gave generally better grades to models for K = 50, which matched the number of topics in ground truth. This is because models more or less mixed more common words when K is larger. Coherence score calculation (Equation (3)) penalizes common words that occur in many documents, whereas humans could somehow "guess" the meaning of topics based on the rest of words thus gave relatively good ratings. Nevertheless, annotators gave remarkably higher ratings to LeadLDA than baselines on all datasets regardless of K being 50 or 100, which confirmed that LeadLDA effectively yielded goodquality topics. For a detailed analysis, Figure 3 lists the top 20 words about "MH17 crash" induced by different models 9 when K = 50. We have the following 9 As shown in Table 3 and 4, the topic coherence scores of GMTM were the worst. Hence, the topic generated by Table 4: Subjective ratings of topics. The meanings of K50, K100, TREE, STR and LEAD are the same as in Table 3. observations: • BTM, based on word-pair co-occurrences, mistakenly grouped "Fok's family" (a tycoon family in Hong Kong), which co-occurred frequently with "Hong Kong" in other topics, into the topic of "MH17 crash". "Hong Kong" is relevant here as a Hong Kong passenger died in the MH17 crash. • The topical words generated by SATM were mixed with words relevant to the bus explosion in Guangzhou, since it aggregated messages according to topic affinities based on the topics learned in the previous step. Thus the posts about bus explosion and MH17 crash, both pertaining to disasters, were aggregated together mistakenly, which generated spurious topic results. • Both TreeLDA and StructLDA generated topics containing non-topic words like "microblog" and "dear". This means that without distinguishing leaders and followers, it is difficult to filter out non-topic words. The topic quality of StructLDA nevertheless seems better than GMTM is not shown due to space limitation. TreeLDA, which implies the usefulness of exploiting topic dependencies of posts in conversation structures. • LeadLDA not only produced more semantically coherent words describing the topic, but also revealed some important details, e.g., MH17 was shot down by a missile. Conclusion and Future Works This paper has proposed a novel topic model by considering the conversation tree structures of microblog posts. By rigorously comparing our proposed model with a number of competitive baselines on real-world microblog datasets, we have demonstrated the effectiveness of using conversation structures to help model topics embedded in short and colloquial microblog messages. This work has proven that detecting leaders and followers, which are coarse-grained discourse derived from conversation structures, is useful to model microblogging topics. In the next step, we plan to exploit fine-grained discourse structures, e.g., dialogue acts (Ritter et al., 2010), and propose a unified model that jointly inferring discourse roles and topics of posts in context of conversation tree structures. Another extension is to extract topic hierarchies by integrating the conversation structures into hierarchical topic models like HLDA (Blei et al., 2003a) to extract fine-grained topics from microblog posts. Figure 2 : 2Graphical Model of LeadLDA BTM: Biterm Topic Model (BTM) 6 (Yan et al., 2013) directly models topics of all word pairs (biterms) in each post, which outperformed LDA, Mixture of Unigrams model, and the model proposed by Zhao et al. (2011) that aggregated posts by authorship to enrich context. SATM: A general unified model proposed by Quan et al. (2015) that aggregates documents and infers topics simultaneously. We implemented SATM and examined its effectiveness specifically on microblog data. GMTM: To tackle word sparseness, Sridhar et al. (2015) utilized Gaussian Mixture Model (GMM) to cluster word embeddings generated by a log-linear word2vec model 7 . Figure 3 : 3The extracted topics describing MH17 crash. Each column represents the similar topic generated by the corresponding model with the top 20 words. The 2nd row: original Chinese words; The 3rd row: English translations. Figure 1 illustrates an example of a conversation tree, in which messages can initiate a new topic such as [O] and [R7] or raise a new aspect (subtopic) of the previously discussed topics such as [R2] and [R10][R4] My gosh!!! that sucks Poor on u guys… [R7] For the safety of US, I'm for Trump to be president, especially after this. [R8] I repost to support @realDonaldTrump. Can't agree more [R10] R U CRAZY?! Trump is just a bigot sexist and racist. …… …… …… [R9] thanks dude, you'd never regret …… [R5] Don't worry. I was home. [R6] poor guys, terrible Figure 1: An example of conversation tree. [O]: the original post; [Ri]: the i-th repost/reply; Ar- row lines: reposting/replying relations; Dark black posts: leaders to be detected; Underlined italic words: key words representing topics Table 1 : 1The notations of symbols in the sampling formulas Table 2 : 2Statistics of our three evaluation datasets ).4 http://open.weibo.com/wiki/2/search/ topics 5 https://taku910.github.io/crfpp/ Table 3 : 3Absolute values of coherence scores. Lower is better. K50: 50 topics; K100: 100 topics; N: # of top words ranked by topic-word probabil- ities; TREE: TreeLDA; STR: StructLDA; LEAD: LeadLDA. 香港 微博 马航 家属 证 实 入境处 客机 消息 曹 格 投给 二胎 选项 教父 滋养 飞机 外国 心情 坠 毁 男子 同胞 乌克兰 航空 亲爱 国民 绕开 飞行 航班 领空 所 有 避开 宣布 空域 东部 俄罗斯 终于 忘记 公司 绝望 看看 珍贵 香港 入境处 家属 证实 男子 护照 外国 消息 坠 毁 马航 报道 联系 电台 客机 飞机 同胞 确认 事 件 霍家 直接 马航 祈祷 安息 生命 逝 者 世界 艾滋病 恐怖 广 州 飞机 无辜 默哀 远离 事件 击落 公交车 中国 人 国际 愿逝者 真的 乌克兰 马航 客机 击落 飞机 坠毁 导弹 俄罗斯 消息 乘客 中国 马来西 亚 香港 遇难 事件 武装 航班 恐怖 目前 证实TreeLDA StructLDA BTM SATM LeadLDA http://www1.se.cuhk.edu.hk/˜lijing/ data/microblog-topic-extraction-data.zip https://github.com/xiaohuiyan/BTM 7 https://code.google.com/archive/p/ word2vec/ AcknowledgmentThis work is supported by General Research Fund of Hong Kong (417112), the Innovation and Technology Fund of Hong Kong SAR (ITP/004/16LP), Shenzhen Peacock Plan Research Grant (KQCX20140521144507925) and Innovate UK (101779). 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Une métagrammaire pour les noms prédicatifs du français Barrier Sébastien Nicolas Barrier nbarrier@linguist.jussieu.fr Laboratoire LLF Université Paris UFR de Linguistique 2 Place Jussieu75251, Cedex 05Paris Une métagrammaire pour les noms prédicatifs du français Mots-clefs -Keywords Grammaires d'arbres adjointsFTAGnoms prédicatifsverbes supportsmétagrammaireordre des constituants Tree-Adjoining GrammarsFTAGpredicative nounsupport verb constructionsmetagrammarconstituency order La grammaire FTAG du français a vu ces dernières années ses données s'accroître très fortement. D'abord écrits manuellement, les arbres qui la composent, ont ensuite été générés semi-automatiquement grâce à une Métagrammaire, développée tout spécialement. Après la description des verbes en 1999, puis celle des adjectifs en 2001-2002, c'est maintenant au tour des verbes supports et des noms prédicatifs de venir enrichir les descriptions syntaxiques de la grammaire. Après un rappel linguistique et technique des notions de verbe support et de métagrammaire, cet article présente les choix qui ont été entrepris en vue de la description de ces nouvelles données.We present here a new implementation of support verbs for the FTAG grammar, a french implementation of the Tree Adjoining Grammar model. FTAG has know over the years many improvements.(Candito, 1999)hence integrated an additional layer of syntactic representation within the system. The layer, we called MetaGrammar let us improve the syntactic coverage of our grammar by genrating semi-automatically thousands of new elementary trees. Résumé -Abstract La grammaire FTAG du français a vu ces dernières années ses données s'accroître très fortement. D'abord écrits manuellement, les arbres qui la composent, ont ensuite été générés semi-automatiquement grâce à une Métagrammaire, développée tout spécialement. Après la description des verbes en 1999, puis celle des adjectifs en 2001-2002, c'est maintenant au tour des verbes supports et des noms prédicatifs de venir enrichir les descriptions syntaxiques de la grammaire. Après un rappel linguistique et technique des notions de verbe support et de métagrammaire, cet article présente les choix qui ont été entrepris en vue de la description de ces nouvelles données. We present here a new implementation of support verbs for the FTAG grammar, a french implementation of the Tree Adjoining Grammar model. FTAG has know over the years many improvements. (Candito, 1999) hence integrated an additional layer of syntactic representation within the system. The layer, we called MetaGrammar let us improve the syntactic coverage of our grammar by genrating semi-automatically thousands of new elementary trees. Les verbes supports Nous rappelons ici brièvement les principaux intérêts linguistiques des constructions à verbe support. Pour davantage de renseignements, le lecteur pourra par exemple se tourner vers (Giry-Schneider, 1978), (Giry-Schneider, 1987) ou encore (Danlos, 1992). Les travaux du LADL dans le domaine restent une riche source d'informations, avec ceux de (Harris, 1968) sur les verbes opérateurs. Le rôle du verbe support est essentiellement de porter des informations concernant le temps et l'aspect (Gross, 1981). Il introduit avec lui un nom, appelé nom prédicatif, qui donne la valeur sémantique au prédicat de la phrase. Ce nom peut être employé au sein d'un syntagme nominal 1 (que nous notons SNvsupp) ou d'une construction à verbe support 2 , tout en conservant un lien paraphrasetique entre ces deux usages : Max commet un crime contre Luc vs. Le crime de Max contre Luc. Comme le nom prédicatif a une valeur lexicale, il ne peut être ni cliticisé, ni interrogé. Il peut en revanche être relativisé 3 ou extrait. Mais alors qu'un verbe ordinaire accepte seulement un seul type d'extraction : 1 De la forme ¢ ¡ ¤ £ ¦ ¥ § © ¤ ¡ ¥ ! # " % $ & ¡ (' 0 )¡ £ ¢ ¥ ¤ § ¦ © ¢ " ! $ # % ¦ & # est Les Métagrammaires Les Métagrammaires sont au sein de nombreux projets de développement. Elles nous permettent ici d'encoder les verbes supports pour la grammaire FTAG. Nous présentons donc le fonctionnement de la Métagrammaire mise au point par (Candito, 1999) et repris par (Barrier, 2002), qui est utilisée pour le français, l'italien et le coréen 5 . Principes et fonctionnement La Métagrammaire mise au point par (Candito, 1999), suivant la proposition de (Vijay-Shanker & Schabes, 1992), est un réseau d'héritage multiple, un mécanisme de partage de propriétés syntaxiques entre des unités structurées dans une hiérarchie à trois dimensions qui induit un raisonnement quasi-monotone. Chaque propriété syntaxique de cette hiérarchie est déclaréee comme un ensemble de descriptions partielles d'arbres -intuitivement des "bouts" d'arbres. Ces définitions peuvent laisser sous-spécifiées certaines relations entre noeuds -chaque sous-classe du réseau venant enrichir ces contraintes, en spécifiant certaines de ces relations. Afin de construire des structures pré-lexicalisées respectant le principe de co-occurence prédicat/argument et de grouper les structures appartenant à la même famille d'arbres, la MG utilise, en plus des descriptions partielles, des fonctions syntaxiques. La sous-catégorisation est exprimée comme une liste de parties du discours possible à laquelle est associée une liste de fonctions. Cette sous-catégorisation initiale est celle du cas non marqué, qu'une redistribution peut venir modifier. Les arbres élémentaires partageant la même sous-catégorisation initiale ne diffèrent que par la réalisation de surface de leurs fonctions syntaxiques, et leurs redistributions. Chaque classe relève donc nécessairement d'une des trois dimensions définies, à savoir, la sous-catégorisation initiale pour la dimension 1, la redistribution des fonctions syntaxiques pour la dimension 2, et la réalisation de surface des fonctions syntaxiques pour la dimension 3. La logique de description de ces classes utilise un langage de type déclaratif. Concrètement, les informations s'organisent autour de variables (' ) ( 1 0 3 2 5 4 6 ( ¢ 8 7 ) auxquelles est associée une liste possible de catégories du discours, de fonctions ( 7 @ 9 6 A B ¤ 4 D C F E H G 8 I ¢ 7 ), et éventuellement d'index -chaque variable désignant un noeud de l'arbre. Ces variables sont utilisées comme autant d'éléments à unifier permettant une description partielle d'arbres, dans une notation père-fils. Un exemple Pour illustrer notre propos, nous fournissons maintenant un exemple tiré de la Métagrammaire des noms prédicatifs 6 . Le programme croise les classes (SUJ-NOM), (OBJ-NOM), (ACTIF) et (N0VN), issues de la hiérarchie, dont les données sont les suivantes : Dimension 1 La classe N0VN hérite des classes NOM-PRED et SUJET-INITIAL Contenu de NOM-PRED Contenu de SUJET-INITIAL Contenu de N0VN Constante Npred = ¢ ¡ Constante arg0 = ¤ £ ¦ ¥ § © £ Constante arg0 = £ ¦ ¥ § fonction6 7 7 8 9 A @ C B E D ) F H G " @ C I 3 F Q P R F T S © U W V 9 A @ C B E D ) F H G " @ C I 3 F H X Ỳ a I b d c f e g I i h p U q D ) b s r t W u v D w h x t W y C @ 9 A @ C B E D ) F H G " @ C I 3 F T q D ) Q q h F T I 3 B rD ) r h F T I 3 B T V d e V % f Les noms prédicatifs et FTAG Ordre des constituants Avec l'essor des MG, des problèmes d'un ordre nouveau sont apparus. Alors que l'on s'intéressait autrefois au pur phénomène syntaxique, puis à la façon adéquate de le représenter, nous sommes maintenant confrontés à un problème d'une autre envergure. Puisque la MG se veut généralisante, il nous faut non seulement comprendre ce que les différentes descriptions syntaxiques ont en commun et ce qu'elles partagent comme informations, mais aussi savoir comment encoder l'ordre entre constituants, car chaque élément est libre de se déplacer au sein d'un arbre tant que les contraintes qui le définissent sont satisfaites. Nous considérons donc le problème de l'ordre entre constituants en termes de dominance et de précédence, ie que tout syntagme qui ne serait pas à la place à laquelle il serait effectivement attendu serait considéré comme problématique. La première des solutions que l'on pourrait envisager serait d'encoder l'ordre en dimension 1 ou 2, mais la propagation de ces contraintes serait réalisée trop tôt, alors que d'autres choix pourraient s'appliquer et seraient guidés par la réalisation des arguments en dimension 3 7 . La seconde idée serait de construire des niveaux syntaxiques intermédiaires pour permettre le rattachement de noeuds deux à deux. En pratique, cette solution n'est cependant guère applicable car trop contraignante et directement liée à la théorie syntaxique dans laquelle elle veut s'appliquer. Une troisième solution serait de se reposer sur le calcul du modèle minimal 8 et d'introduire des contraintes d'ordre en dimension 3, comme suggéré par (Gerdes, 2002). Si nous considérons l'exemple des verbes bi-transitifs à 3 arguments en anglais comme mentionné, où il s'agit d'encoder la place de l'objet direct ¡ par rapport à l'objet indirect avec la préposition ¤ 0 9 , nous pouvons introduire indifféremment une contrainte d'ordre linéaire ¡ £ ¢ E en dimension 3 dans la classe de l'objet direct ¡ , ou dans la classe de l'objet indirect . Puisque chacun des éléments à ordonner doit figurer dans la structure résultante, la contrainte d'ordre peut donc s'appliquer du fait d'un partage correct de l'information entre les deux classes. On peut cependant noter que quand la classe des verbes bi-transitifs exige la réalisation de ces deux objets, les classes ¡ et sont appelées indépendamment l'une de l'autre sans pour autant pouvoir porter d'hypothèse sur le contenu de la classe concurrente, si bien que rien n'impose la présence de l'autre classe. La position de (Gerdes, 2002) présente donc, selon nous, certains désavantages. En particulier, elle engendre un déséquilibre entre les différentes classes mises en jeu, et alors que l'application d'une contrainte d'ordre devrait se faire de façon partagée, c'est uniquement la classe qui porte la contrainte qui dicte son comportement à l'autre. Bien évidemment, nous pouvons pour "l'ergonomie de la représentation (vouloir) réutiliser la classe dans un autre contexte", avec cependant un double risque potentiel, puisque rien n'impose, d'une part, que la constante "inconnue" ait à s'unifier avec une autre lors du calcul du modèle minimal, et qu'il nous faille d'autre part gérer une accumulation à priori non quantifiable des contraintes d'ordre à définir pour les autres placements à réaliser. Ceci nous amène donc tout naturellement au problème de la lisibilité, puisque nos contraintes ne s'appliqueraient finalement qu'au cas par cas, et qu'elles sembleraient imposer la présence d'autres classes facultatives. Jusqu'ici aucune des solutions que nous avons étudiées ne semble satisfaisante tant d'un point de vue linguistique qu'implémental. La dernière proposition que nous avons abordée ne partageait pas correctement l'information et imposait certains choix non anodins et en particulier la position de la contrainte d'ordre n'était pas sans conséquences. A défaut de vouloir systématiser la réalisation de l'ordre entre les différents constituants de la phrase en faisant rentrer des contraintes dans des modules de dimension 1, 2 ou 3, nous proposons donc une approche moins généraliste en choisissant d'encoder l'ordre linéaire au sein de nouvelles classes de dimension 4, "indépendamment" des classes de dimension 1, 2 ou 3, à l'aide d'un système de règles explicites et conditionnelles. (Candito, 1999) avait déjà défini de tels modules sans pour autant leur accorder de statut particulier. Nous avons donc repris et étendu son implémentation qui permettait d'ajouter aux croisements déjà définis de nouvelles classes dans la liste de précédence 10 . Conclusion Cette nouvelle réalisation permet d'enrichir les Metagrammaires du français déjà encodées, et présente la MGC comme un outil mature et robuste, particulièrement adapté au français. Les corrections que nous y avons apportées la rendent beaucoup plus souple et simple à l'usage 11 . 5 des 18 familles que nous avons définies sont d'ores et déjà finalisées, et totalisent à elles seules, plus de 2050 arbres. Ce travail demeure cependant incomplet, dans la mesure où une évaluation sur corpus n'a pas encore été entamée. 10 On pourrait également envisager un système à la (Gaiffe et al., 2002) où les croisements avec la dimension 4 seraient obtenus à l'aide d'un système à base de traits polaires. Mais pour les raisons que nous avons exposées précédemment, nous avons écarté cette solution. 11 Pour faciliter le développement, nous avons revu les messages de ¡ ¡ £ ¢ ¥ ¤ ¦ ¤ ¦ § © ¤ pour les étendre ; chaque schème se voit également maintenant renseigné d'une description en structure de traits, similaire dans l'esprit à celle de (Danlos, 1998) ou (Gaiffe et al., 2002, qui permet d'une part d'établir un pont entre lexique et syntaxe, et d'autre part, d'obtenir une "vue" de l'entrée appréhendable par un non expert TAG. donc analysée de 2 façons concurrentes : l'une comme simple constituant (i.e. un syntagme nominal), l'autre comme deux constituants (un syntagme nominal et un syntagme prépositionnel) 4 Chaque nom prédicatif peut accepter un verbe support qui peut fournir diverses variantes aspectuelles : neutre : Max a l'espoir de retrouver son livre inchoative : Max prend l'espoir de retrouver son livre durative : Max garde l'espoir de retrouver son livre terminative : Max a perdu l'espoir de retrouver son livre Mais ces variations ne sont pas toujours si nettes, et on a alors à faire à une variante stylistique : Max (caresse/nourrit) l'espoir de retrouver son livre. Figure 1 : 1Exemple de réalisation pour Max prend une doucheLes choix linguistiques imposés par cette nouvelle implémentation suivent ceux déjà définis par(Abeillé, 1991) et (Candito, 1999, pour leur propre implémentation d'une grammaire TAG. Nous renvoyons le lecteur à(Abeillé & Rambow, 2000) pour tout renseignement complémentaire concernant les grammaires TAG et FTAG. . Un verbe support ne peut se construire avec SNvsupp. Une phrase comme *Max a commis le crime de Luc contre Léa, n'est pas acceptable, sauf si elle a le sens spécial de Max a commis le même crime que Luc contre Léa.1 ¥ % ) ¢ 2 2 De la forme ¥ 4 3 0 5 7 6 § ¤ § ¢ ¡ ¤ £ 8 ¥ § © 9 ( # " % $ & ¡ ' ) ¥ ) 2 . Dans cette structure, Extraction : C'est un crime contre Luc que Max a raconté, mais pas : *C'est contre Luc que Max a raconté un crime. il faut pouvoir rendre compte d'une double analyse avec verbe support comme : Max commet un crime contre Luc Extraction : C'est un crime contre Luc que Max commet, et C'est contre Luc que Max commet un crime La séquence La classe SUJ-NOM hérite de la classe FONCTION-SUJET Contenu de POSITION-SUJET Contenu de SUJ-NOMobjet fonction sujet Après héritage la classe N0VN contient alors Constante Npred = ¢ ¡ -fonction objet Constante arg0 = £ ¥ § -fonction sujet Dimension 2 La classe ACTIF hérite de la classe MORPHO-VERBALE Contenu de MORPHO-VERBALE Contenu de ACTIF Constantes Npred = ¢ ¡ , vsupp = , Sd = ¦ ! " ! Après héritage la classe ACTIF contient alors Constante Npred = ¢ ¡ $ # % & # ' ¦ ! ¦ ! Dimension 3 sujet = -fonction sujet ' ) ( " 0 ' 1 ' ¦ ! ¦ ! Après héritage la classe SUJ-NOM contient alors Constante sujet -fonction sujet 2 # ' ) ( ) 0 1 3 ' ¦ ! " ! La classe OBJ-NOM hérite de la classe POSITION-OBJ Contenu de POSITION-OBJ Contenu de OBJ-NOM ' ¦ ! " ! 4! ¤ 5 0 Après héritage, le contenu de la classe OBJ-NOM reste inchangé Le compilateur commence alors par engendrer toutes les classes croisées pour les traduire ensuite en arbre(s) élé- mentaires(s) en spécifiant totalement les relations de dominance et de précédence linéaire laissées sous spécifiées dans les descriptions partielles. Chaque classe croisée hérite précisément d'une classe terminale de dimension 1, puis d'une classe terminale de dimension 2, puis d'autant de classes terminales de dimension 3 qu'il y a de fonc- tions syntaxiques à réaliser (ici Sujet et Objet) issues de dimension 1. L'arbre obtenu est donc le suivant : Elle nous permet cependant de placer le syntagme prépositionnel avant le nom prédicatif. L'annexe présenté en fin d'article, fournit quelques détails liés à l'implémentation.Différentes descriptions pour les noms prédicatifs avaient été proposées pour la grammaire FTAG, par (Abeillé, 1991), mais elles comportent selon nous deux inconvénients majeurs. D'une part, ces descriptions n'ont pas été généralisées par l'utilisation d'une métagrammaire, et n'ont pas été incluses dans la version actuelle de la gram- maire, d'autre part, elles placent sur pied d'égalité, verbe support et nom prédicatif, puisque les phrases à verbe support y sont représentées comme des arbres élémentaires à têtes multiples -la tête lexicale comprenant le verbe, le nom prédicatif (et éventuellement le déterminant et des propositions). De telles descriptions correspondent davantage selon nous à des expressions figées. Nous proposons donc de renseigner la tête lexicale par le nom prédicatif seul ; le verbe support sera quant à lui substitué. Le choix du verbe support est déterminé par le nom prédicatif lui-même, de par l'utilisation d'un trait ou d'un métatrait (si plusieurs supports peuvent être utilisés). Les arbres concernant les syntagmes nominaux sont des arbres initiaux et non auxiliaires, comme c'est le cas généralement, puisque le nom est tête. Nous rendons compte de la double analyse, même si celle-ci s'avère peu satisfaisante dans la mesure où elle ne fait pas référence à une ambiguïté de la langue. Le même phénomène se produit lors de la passivation : avec un verbe ordinaire, on ne peut avoir *Un crime a été raconté par Max contre Luc, mais avec un verbe support il est tout à fait possible d'obtenir Un crime a été commis par Max contre Luc. 5 D'autres compilateurs alternatifs existent cependant, et on pourra se tourner vers (Gaiffe et al., 2002) pour davantage de détails. Pour plus de clarté, nous ne renseignons pas ici les traits associés aux différents noeuds. Cela alourdirait inutilement notre exposé. De plus, il n'est fait aucun travail d'unification sur les traits par le compilateur. Il faut encoder un ordre entre réalisation de fonctions et non un ordre entre fonctions. 8 Le terme de modèle minimal apparaît pour la première fois dans(Gerdes, 2002), qui le définit comme "modèle d'une description avec un nombre minimal de noeuds", ie qu'il s'agit des référents minimaux de la description où le nombre de noeuds serait minimal. Ces derniers ont d'ailleurs été considérés à tort par l'auteur comme clairement équivalents au modèle référent de(Candito, 1999).9 Mary gave a book to Peter vs * Mary gave to Peter a book. ANNEXE : Brève description des dimensionsLes nouvelles familles que nous définissons reprennent les conventions de notation déjà adoptées par(Candito, 1999)La grammaire FTAG établit une distinction entre complémenteur et pronom relatif. Cette distinction tient au fait que les complémenteurs pour les relatives introduisent aussi des complétives Une grammaire lexicalisée d'arbres adjoints pour le français. Abeillé A, N. & BARRIER S.Université ParisPhD thesisLa grammaire FTAG. Documentation interneABEILLÉ A. (1991). Une grammaire lexicalisée d'arbres adjoints pour le français. PhD thesis, Université Paris 7. ABEILLÉ A., BARRIER N. & BARRIER S. (2001). La grammaire FTAG. Documentation interne. Une métagrammaire pour les adjectifs du français. Abeillé A Rambow O, Tree Adjoining Grammars. USA: CSLI PublicationsABEILLÉ A. & RAMBOW O. (2000). Tree Adjoining Grammars. USA: CSLI Publications. BARRIER N. (2002). Une métagrammaire pour les adjectifs du français. In TALN 2002. Le prédicat adjectival en ftag. Boonen D, Université Paris 7Master's thesisBOONEN D. (2001). Le prédicat adjectival en ftag. Master's thesis, Université Paris 7. Représentation modulaire et paramétrable de grammaires électroniques lexicalisées. Application au français et à l'italien. Candito M.-H , Université Paris 7PhD thesisCANDITO M.-H. (1999). Représentation modulaire et paramétrable de grammaires électroniques lexicalisées. Application au français et à l'italien. PhD thesis, Université Paris 7. Support verb constructions. Danlos L, Journal of French Linguistic Study. DANLOS L. (1992). Support verb constructions. In Journal of French Linguistic Study. GTAG: un formalisme lexicalisé pour la génération inspiré de TAG. Danlos L, TAL. DANLOS L. (1998). GTAG: un formalisme lexicalisé pour la génération inspiré de TAG. TAL, 39-2. A new metagrammar compiler. Gaiffe B, Crabbé B. &amp; Roussanaly A, Proceedings of TAG+6. TAG+6GAIFFE B., CRABBÉ B. & ROUSSANALY A. (2002). A new metagrammar compiler. In Proceedings of TAG+6. Topologie et grammaires formelles de l'allemand. Gerdes K. ; Giry-Schneider J , Droz. GIRY-SCHNEIDER J.DrozGenève-Paris; Genève-ParisUniversité ParisPhD thesisLes prédicats nominaux en françaisGERDES K. (2002). Topologie et grammaires formelles de l'allemand. PhD thesis, Université Paris 7. GIRY-SCHNEIDER J. (1978). Les nominalisations en français. Genève-Paris: Droz. GIRY-SCHNEIDER J. (1987). Les prédicats nominaux en français. Genève-Paris: Droz. Les bases empiriques de la notion de prédicat sémantique. Gross M, Langages, 63. HARRIS Z. S.Wiley-InterscienceMathematical Structure of LanguagesGROSS M. (1981). Les bases empiriques de la notion de prédicat sémantique. Langages, 63. HARRIS Z. S. (1968). Mathematical Structure of Languages. Wiley-Interscience. Structure sharing in lexicalized tree adjoining grammar. Vijay-Shanker K, Schabes Y, Proceedings of COLING-92. COLING-92VIJAY-SHANKER K. & SCHABES Y. (1992). Structure sharing in lexicalized tree adjoining grammar. In Proceedings of COLING-92. Avoir, prendre, perdre : constructions à verbe support et extensions aspectuelles. Vivès R, Université Paris 8PhD thesisVIVÈS R. (1983). Avoir, prendre, perdre : constructions à verbe support et extensions aspectuelles. PhD thesis, Université Paris 8.

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A Comparative Study of Open Domain and Opinion Question Answering Systems for Factual and Opinionated Queries

The development of the Web 2.0 led to the birth of new textual genres such as blogs, reviews or forum entries. The increasing number of such texts and the highly diverse topics they discuss make blogs a rich source for analysis. This paper presents a comparative study on open domain and opinion QA systems. A collection of opinion and mixed fact-opinion questions in English is defined and two Question Answering systems are employed to retrieve the answers to these queries. The first one is generic, while the second is specific for emotions. We comparatively evaluate and analyze the systems' results, concluding that opinion Question Answering requires the use of specific resources and methods.

A Comparative Study of Open Domain and Opinion Question Answering Systems for Factual and Opinionated Queries 2009 Alexandra Balahur abalahur@dlsi.ua.es Department of Software and Computing Systems. Apartado de Correos 99 Natural Language Processing and Information Systems Group 03080Alicante Ester Boldrini eboldrini@dlsi.ua.es Department of Software and Computing Systems. Apartado de Correos 99 Natural Language Processing and Information Systems Group 03080Alicante Andrés Montoyo montoyo@dlsi.ua.es Department of Software and Computing Systems. Apartado de Correos 99 Natural Language Processing and Information Systems Group 03080Alicante Patricio Martínez-Barco patricio@dlsi.ua.es Department of Software and Computing Systems. Apartado de Correos 99 Natural Language Processing and Information Systems Group 03080Alicante A Comparative Study of Open Domain and Opinion Question Answering Systems for Factual and Opinionated Queries Borovets, Bulgaria2009Question AnsweringMulti-perspective Question AnsweringOpinion AnnotationOpinion MiningNon- Traditional Textual Genres The development of the Web 2.0 led to the birth of new textual genres such as blogs, reviews or forum entries. The increasing number of such texts and the highly diverse topics they discuss make blogs a rich source for analysis. This paper presents a comparative study on open domain and opinion QA systems. A collection of opinion and mixed fact-opinion questions in English is defined and two Question Answering systems are employed to retrieve the answers to these queries. The first one is generic, while the second is specific for emotions. We comparatively evaluate and analyze the systems' results, concluding that opinion Question Answering requires the use of specific resources and methods. Introduction Recent years' statistics show that the number of blogs has been increasing at an exponential rate. A research of the Pew Institute [1] shows that 2-7% of Internet users created a blog and that 11% usually read them. Moreover, researches in different fields proved that this new textual genre is a valuable resource for large community behavior analysis, since blogs address a great variety of topics from a high diversity of social spheres. A common belief is that they are written in a colloquial style, but [2] shows that the language of these texts is not restricted to the more informal levels of expression and a large number of different genres are involved. As a consequence, free expressions, literary prose and newspaper writing coexist without a clear predominance. When using this textual genre, people tend to express themselves freely, using colloquial expressions employed only in day-by-day conversations. Moreover, they can introduce quotes from newspaper articles, news or other sources of information to support their arguments, make references to previous posts or the opinion expressed by others in the discussion thread. Users intervening in debates over one specific topic are from different geographical regions and belong to diverse cultures. All the abovementioned features make blogs a valuable source of information that can be exploited for different purposes. However, due to their language being heterogeneous, it is complex to understand and formalize in order to create effective Natural Language Processing (NLP) tools. At the same time, due to the high volume of data contained in blogs, automatic NLP systems are needed to manage the language understanding and generation. Analyzing emotions and/ or opinions expressed in blog posts could also be useful to predict people's opinion or preferences about a product or an event. One of the other possible applications is an effective Question Answering (QA) system, able to recognize different queries and give the correct answer to both factoid and opinion questions. Related work QA is the task in which, given a set of questions and a collection of documents where the answers can be found, an automatic NLP system is employed to retrieve the answer to these queries in Natural Language. The main difference between QA and Information Retrieval (IR) is that in the first one, the system is supposed to output the exact answer snippet, whereas in the second task whole paragraphs or even documents are retrieved. Research in building factoid QA systems has a long tradition; however, it is only recently that studies have started to focus on the creation and development of opinion QA systems. Recent years have seen the growth of interest in this field, both by the research and publishing of studies on the requirements and peculiarities of opinion QA systems [4] as well as the organization of international conferences that promote the creation of effective QA systems both for general and subjective texts, such as the Text Analysis Conference (TAC) 1 . Last year's TAC 2008 Opinion QA track proposed a mixed setting of factoid and opinion questions (so called "rigid list" and "squishy list"), to which the traditional systems had to be adapted. Participating systems employed different resources, techniques and methods to overcome the newly introduced difficulties related to opinion mining and polarity classification. The Alyssa system [5], which performed better in the "squishy list" questions than in the "rigid list" questions, had additional components implemented for classifying the polarity of the question and of the extracted answer snippet, using a Support Vector Machines (SVM) classifier trained on the MPQA corpus [6], English NTCIR 2 data and rules based on the subjectivity lexicon [7]. Another system introducing new modules to tackle opinion is [8]. They perform query analysis to detect the polarity of the question using defined rules. They filter opinion from fact retrieved snippets using a classifier based on Naïve Bayes with unigram features, assigning for each sentence a score that is a linear combination between the opinion and the polarity scores. The PolyU [9] system determines the sentiment orientation of the sentence and it uses the Kullback-Leibler divergence measure with the two estimated language models for the positive versus negative categories. The UOFL system [10] generates a non-redundant summary of the query for the opinion questions, to take into consideration all the information present in the question, and not only the separated words. Motivation and contribution Opinion Mining is the task of extracting, given a collection of texts, the opinion expressed on a given target within the documents. It has been proven that performing this task, several other subtasks of NLP can be improved: Information Extraction (where opinion mining techniques can be used as a preprocessing step to separate among factual and subjective information), Authorship Determination (as subjective language can be considered as a personality mark), Word Sense Disambiguation, multisource (multi-perspective) summarization and more informative Answer Retrieval for definition questions [16] (as it can constitute a measure for credibility, sentiment and contradictions). Related work presented research in determining the differences in the characteristics of the fact versus opinion queries and their corresponding answers [11]. However, certain types of questions, which are factual in nature, require the use of Opinion Mining resources and techniques in order to retrieve the correct answers. Our first contribution relies in the analysis and definition of the criteria for the discrimination among different types of factual versus opinionated questions. Furthermore, we created and annotated a set of questions and answers over a multilingual blog collection for English and Spanish. Thus, we also analyze the effect of the textual genre characteristics on the properties of the opinion answers retrieved/missed. A further contribution lies in the evaluation of two different approaches to QA; one is fact oriented (based on Named Entities -NEs-) and the other is specifically designed for opinion QA scenarios. We analyze their different elements, specifications, behavior, evaluated 2 http://research.nii.ac.jp/ntcir/ performance and present conclusions on the needs and requirements of systems designed for the presented categories of questions. Last, but not least, using the annotated answers and their corresponding corpus, we analyze possible methods for keyword expansion in an opinion versus fact setting. We present some possible solutions to the shortcomings of direct keyword expansion for opinion QA, employing "polarity-based" expansion using our corpus annotations. Corpus collection and analysis The corpus we employed for our evaluation is composed of blog posts extracted form the Web. It has been collected taking into account the requirements of coherence, authenticity, equilibrium and quality. Our main purpose was to collect a corpus in which the blog posts were about a topic, forming a coherent discussion. Moreover, our collection had to provide a real example of this textual genre, it had to be of the same length for each topic and language, and originated from reliable Web sites. We selected three topics: the Kyoto Protocol, the 2008 Zimbabwe and the USA elections. After having collected the three corpora, we analyzed the characteristic of this textual genre also looking for the subjective expressions and for the way they are formulated in NL. The following step of our research consisted in building up the initial version of EmotiBlog [18], an annotation scheme focused on emotions detection in non-traditional textual genres. The annotation scheme is briefly presented in the following section. Annotation scheme As we mentioned in the previous section, EmotiBlog [12] is an annotation scheme for detecting opinion in nontraditional textual genres. It is the first version of a finegrained and multilingual annotation model that could be useful for an exhaustive comprehension of NL. The first version has been created for English, Italian and Spanish; however, it could be easily adapted for the annotation of other languages. Firstly, we detect the overall sentiment of the blogs and subsequently a distinction between objective and subjective sentences is done. Moreover, for each element, we annotate the source, the target and also a wide range of attributes for the elements (sentiment type, its intensity and polarity, for example). Sentiments are grouped according to [13], who created an alternative dimensional structure of the semantic space for emotions grouping emotions between obstructive and conductive, and finally, between high power and low power control. The annotation task has been carried out by two non-native speakers with extensive knowledge of Spanish and English. The labeling of the 100 texts took approximately one month and a half, working in a part-time schedule. Finally, the last step consisted in labeling the answers to our list of questions to create a gold standard for detecting the mistakes of the QA systems presented in the next section. The list of questions is composed by 20 factual and opinionated queries. Table 1 shows the list of questions. As we can see in Table 1, we have a list of opinionated and factoid queries. Factual need a name, date, time, etc as answer, while opinionated ones something more complex. The system should be able firstly to recognize the subjective expressions and after that, discriminate them in order to retrieve the correct answer. In this case the answer can be expressed by an idiom, a saying, or by a sentence and as a consequence it is not a simple name or a date. It is complex because it could be everything; there are no fixed categories of answer types for opinionated questions. As a consequence, we formulated the opinion questions explicitly in order not to increase the difficulty level of the analysis. 6. Evaluation Open QA system With the purpose of evaluating the performance of a general QA system in a mixed fact and opinion setting, we used the QA system of the University of Alicante [14] [15]. It is an open domain QA system employed to deal with factual questions both for English and Spanish. The queries this system can support are location, person, organization, date-time and number. Furthermore, its architecture is divided into three modules. The first one is the Question Analysis in which the language object of the study is determined using dictionaries with the criterion of selecting the language for which more words are found. Therefore, the question type is selected using a set of regular expressions and the keywords of each question are obtained with morphological and dependencies analysis. For that purpose, MINIPAR 3 for Spanish and Freeling 4 for English are used. The second module is the IR in which the system, originally, relied on the Internet search engines. However, in order to look for information among the Web Log collection, an alternative approach has been developed. A simple keyword-based document retrieval method has been implemented in order to get relevant documents given the question keywords. The last module is called Answer Extraction (AE). The potential answers are selected using a NE recognizer for each retrieved document. LingPipe 5 and Freeling have been used for English and Spanish respectively. Furthermore, NE of the obtained question type and question keywords are marked up in the text. Once selected they are scored and ranked using answer-keywords distances approach. Finally, when all relevant documents have been explored, the system carries out an answer clustering process which groups all answers that are equal or contained by others to the most scored. Specific QA system For the opinion specific QA system, our approach was similar to [16]. Given an opinion question, we try to determine its polarity, the focus, its keywords (by eliminating stopwords) and the expected answer type (EAT) (while also marking the NE appearing in it); once this information is extracted from the question, blog texts are split into sentences and NE are marked. Finally, sentences in the blogs are sought which have the highest similarity score with the question keywords, whose polarity is the same as the determined question polarity and which contains a NE of the EAT. As the traditional QA system outputs 50 answers, we also take the 50 most similar sentences and extract the NEs they contain. In the future, when training examples will be available, we plan to set a threshold for similarity, thus not limiting the number of output answers, but setting a border to the similarity score (this is related to the observation in [4] that opinion questions have a highly variable number of answers. In order to extract the topic and determine the question polarity, we define question patterns. These patterns take into consideration the interrogation formula and extract the opinion words (nouns, verbs, adverbs, adjectives and their determiners). They are then classified to determine the polarity of the question, using the WordNet Affect emotion lists, the emotion triggers resource [17], a list of four attitudes containing the verbs, nouns, adjectives and adverbs for the categories of criticism, support, admiration and rejection and a list of positive and negative opinion words taken from the system in [18]. On the other hand, we preprocessed the blog texts in order to prepare the answer retrieval. Starting from the focus, keywords and topic of the question, we sought sentences in the blog collection (which was split into sentences and where Named Entity Recognition was performed using LingPipe) that could constitute possible answers to the questions, according to their similarity to the latter. The similarity score was computed with Pedersen's Text Similarity Package 6 . The condition we subsequently set was that the polarity of the retrieved snipped be the same as the one of the question and, in the case of questions with EAT PERSON, ORGANIZATION or LOCATION, that a Named Entity of the appropriate type was present in the retrieved snippets. In case retrieved snippets containing Named Entities in the question were found, their score was boosted to the score of the most similar snippet retrieved. In case more than 50 snippets were retrieved, we only considered for evaluation the first 50 in the order of their polarity score (which proved to be a good indicator of the snippet's importance [22]. Evaluation process We evaluate the performance of the two QA systems in terms of the number of found answers within the top 1, 5, 10 and 50 output answers (TQA is the indicator for the traditional QA system and OQA is the indicator for the opinion QA system). In Table 2 we present the results of the evaluations in the case of each of the 20 questions (the table also contains the type of each questions -F (factual) and O (opinion)). The first observation we can make is the fact that the traditional QA system was able to answer only 8 of the 20 questions we formulated. We will thus compare the performance of the systems at the level of these 8 questions they both answered and separately analyze the faults and strong points, as well as the difficulties of each individual question separately). As we can observe in Table 2, as expected, the questions for which the traditional QA system performed better were the pure factual ones (1, 3, 4, 8, 10 and 14), although in some cases (like the one of question number 14) the OQA system retrieved more correct answers. At the same time, purely opinion questions, although revolving around NEs, were not answered by the traditional QA system, but were satisfactorily answered by the opinion QA system (2,5,6,7,11,12), taking into consideration that a purely wordoverlap approach was taken. Questions 18 and 20 were not correctly answered by any of the two systems. We believe this is due to the fact that question 18 was ambiguous as far as polarity of the opinions expressed in the answer snippets ("improvement" does not translate to either "positive" or "negative") and question 20 referred to the title of a project proposal that was not annotated by any of the tools used. Thus, as part of the future work in our OQA system, we must add a component for the identification of quotes and titles, as well as explore a wider range of polarity/opinion scales. Questions 15,16,18,19 and 20 contain both factual as well as opinion aspects and the OQA system performed better than the TQA, although in some cases, answers were lost due to the artificial boosting of the queries containing NEs of the EAT. Therefore, it is obvious that an extra method for answer ranking should be used, as Answer Validation techniques using Textual Entailment. Issues and discussion There are many problems involved when trying to perform opinion QA. Explanations for this fact include ambiguity of the questions (What is the nation that brings most criticism to the Kyoto Protocol? -the answer can be explicitly stated in one of the blog sentences, or a system might have to infer them; therefore, the answer is highly contextual and depends on the texts one is analyzing, the need for extra knowledge on the NEs (i.e. Al Gore is an American politician -should we first look for people that are in favor of environmental measures and test which one is an American politician?) and the fact that, as opposed to purely factoid questions, most of the opinion questions have answers longer than a single sentence. In many of the cases, the opinion mining system missed on the answers due to erroneous sentence splitting. Another source of problems was the fact that we gave a high weight to the presence of the NE of the sought type within the retrieved snippet and in some cases the NER performed by LingPipe either attributed the wrong category to an entity, failed to annotate it or wrongfully annotated words as being NEs when that was not the case. As we could notice, problems of temporal expressions and the coreference need to be taken into account in order to retrieve the correct answer. In most of the time, the QA system need to understand the temporal context of the questions and also of the sentences that compose the corpus, because the present President the USA is different from two years ago, for example. At the other hand, an effective coreference resolution system is indispensable to understand some retrieved answers. Conclusions and future work In this article, we first presented EmotiBlog, an annotation scheme for opinion annotation in blogs and the blog posts collection we gathered to label with our scheme. Subsequently, we presented the collection of mixed opinion and fact questions we created, whose answers we annotated in our corpus. We finally evaluated and discussed on the results of two different QA systems, one that is fact oriented and one that is designed for opinion question answering. Some conclusions that we draw from this analysis are that, even when using specialized resources, the task of opinion QA is still difficult and extra techniques and methods have to be investigated in order to solve the problems we found, parallel to a deeper analysis of the issues involved in this type of QA. In many cases, opinion QA can benefit from a snippet retrieval at a paragraph level, since usually the answers were not mere parts of sentences, but consisted in two or more consecutive sentences. On the other hand, however, we have seen cases in which each of three different consecutive sentences was a separate answer to a question. Future work includes the study of the impact anaphora resolution has on the task of opinion QA, as well as the possibility to use Answer Validation techniques in order to increase the system's performance by answer reranking. ://www.d.umn.edu/~tpederse/text-similarity. Table 1 : 1Example of questionsNUM TYPE QUESTION 1 F What international organization do people criticize for its policy on carbon emissions? 2 O What motivates people's negative opinions on the Kyoto Protocol? 3 F What country do people praise for not signing the Kyoto Protocol? 4 F What is the nation that brings most criticism to the Kyoto Protocol? 5 O What are the reasons for the success of the Kyoto Protocol? 6 O What arguments do people bring for their criticism of media as far as the Kyoto Protocol is concerned? 7 O Why do people criticize Richard Branson? 8 F What president is criticized worldwide for his reaction to the Kyoto Protocol? Table 2 : 2The QA systems' performanceNumber of found answers Question Type Number of answers @1 @5 @10 @ 50 TQA OQA TQA OQA TQA OQA TQA OQA http://www.nist.gov/tac/ http://www.cs.ualberta.ca/~lindek/minipar.htm http://garraf.epsevg.upc.es/freeling/ 5 http://alias-i.com/lingpipe/ AcknowledgmentsThe authors would like to thank Paloma Moreda, Hector Llorens, Estela Saquete and Manuel Palomar for evaluating the questions on their QA system. This research has been partially funded by the Spanish Government under the project TEXT-MESS (TIN 2006-15265-C06-01), by the European project QALL-ME (FP6 IST 033860) and by the University of Alicante, through its doctoral scholarship. Content Creation Online, Pew Internet & American Life Project. A Lenhart, J Horrigan, D Fallow, A. Lenhart, J. Horrigan, D. Fallow, Content Creation Online, Pew Internet & American Life Project. Available at www.pewinternet.org/pdfs/PIP_Content_Creation_Report.pdf Opinion mining and sentiment analysis. 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Kazalski, S. Hunsicker, J.L. Leidner, and D. Klakow. The Alyssa system at TREC QA 2007: Do we need Blog06? In Proceedings of The Sixteenth Text Retrieval Conference (TREC 2007), Gaithersburg, MD, USA, 2007. Annotating expressions of opinions and emotions in language. Language Resources and Evaluation. J Wiebe, T Wilson, C Cardie, 39J. Wiebe, T. Wilson, and C. Cardie Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, volume 39, issue 2-3, pp. 165-210, 2005. Recognising Contextual Polarity in Phrase-level sentiment Analysis. T Wilson, J Wiebe, P Hoffmann, Proceedings of Human language Technologies Conference/Conference on Empirical methods in Natural Language Processing (HLT/EMNLP). Human language Technologies Conference/Conference on Empirical methods in Natural Language Processing (HLT/EMNLP)Vancouver, BC, CanadaT. Wilson, J.Wiebe, and P. Hoffmann. Recognising Contextual Polarity in Phrase-level sentiment Analysis. 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CIMA: A Large Open Access Dialogue Dataset for Tutoring

One-to-one tutoring is often an effective means to help students learn, and recent experiments with neural conversation systems are promising. However, large open datasets of tutoring conversations are lacking. To remedy this, we propose a novel asynchronous method for collecting tutoring dialogue via crowdworkers that is both amenable to the needs of deep learning algorithms and reflective of pedagogical concerns. In this approach, extended conversations are obtained between crowdworkers role-playing as both students and tutors. The CIMA collection, which we make publicly available, is novel in that students are exposed to overlapping grounded concepts between exercises and multiple relevant tutoring responses are collected for the same input.CIMA contains several compelling properties from an educational perspective: student roleplayers complete exercises in fewer turns during the course of the conversation and tutor players adopt strategies that conform with some educational conversational norms, such as providing hints versus asking questions in appropriate contexts. The dataset enables a model to be trained to generate the next tutoring utterance in a conversation, conditioned on a provided action strategy.

CIMA: A Large Open Access Dialogue Dataset for Tutoring July 10, 2020 Katherine Stasaski katiestasaski@berkeley.edu UC Berkeley Berkeley Kimberly Kao Facebook UC Berkeley Berkeley Marti A Hearst hearst@berkeley.edu UC Berkeley Berkeley CIMA: A Large Open Access Dialogue Dataset for Tutoring Proceedings of the 15 th Workshop on Innovative Use of NLP for Building Educational Applications the 15 th Workshop on Innovative Use of NLP for Building Educational ApplicationsJuly 10, 202052 One-to-one tutoring is often an effective means to help students learn, and recent experiments with neural conversation systems are promising. However, large open datasets of tutoring conversations are lacking. To remedy this, we propose a novel asynchronous method for collecting tutoring dialogue via crowdworkers that is both amenable to the needs of deep learning algorithms and reflective of pedagogical concerns. In this approach, extended conversations are obtained between crowdworkers role-playing as both students and tutors. The CIMA collection, which we make publicly available, is novel in that students are exposed to overlapping grounded concepts between exercises and multiple relevant tutoring responses are collected for the same input.CIMA contains several compelling properties from an educational perspective: student roleplayers complete exercises in fewer turns during the course of the conversation and tutor players adopt strategies that conform with some educational conversational norms, such as providing hints versus asking questions in appropriate contexts. The dataset enables a model to be trained to generate the next tutoring utterance in a conversation, conditioned on a provided action strategy. Introduction There is a pressing societal need to help students of all ages learn new subjects. One-on-one tutoring is one of the most effective techniques for producing learning gains, and many studies support the efficacy of conversational tutors as educational aids (VanLehn et al., 2007;Nye et al., 2014;Graesser, 2015;Ruan et al., 2019). Tutoring dialogues should exhibit a number of important properties that are not present in exist- * Research performed while at UC Berkeley. ing open datasets. The conversation should be grounded around common concepts that both the student and the tutor recognize are the topics to be learned (Graesser et al., 2009). The conversation should be extended, that is, long enough for the student to be exposed to new concepts, givin students the opportunity to recall them in future interactions. The collection should contain varied responses, to reflect the fact that there is more than one valid way for a tutor to respond to a student at any given point in the conversation. And lastly, the dialogue should not contain personally identifiable information so it can be available as open access data. We propose a novel method for creating a tutoring dialogue collection that exhibits many of the properties needed for training a conversational tutor. In this approach, extended conversations are obtained between crowdworkers role-playing as both students and tutors. Students work through an exercise, which involves translating a phrase from English to Italian. The workers do not converse directly, but rather are served utterances from prior rounds of interaction asynchronously in order to obtain multiple tutoring responses for the same conversational input. Special aspects of the approach are: • Each exercise is grounded with both an image and a concept representation. • The exercises are linked by subsets of shared concepts, thus allowing the student to potentially transfer what they learn from one exercise to the next. • Each student conversational turn is assigned three responses from distinct tutors. • The exercises are organized into two datasets, one more complex (Prepositional Phrase) than the other (Shape). • Each line of dialogue is manually labeled with a set of action types. We report on an analysis of the Conversa-tional Instruction with Multi-responses and Actions (CIMA) dataset, 1 including the difference in language observed among the two datasets, how many turns a student requires to complete an exercise, actions tutors choose to take in response to students, and agreement among the three tutors on which actions to take. We also report results of a neural dialogue model trained on the resulting data, measuring both quality of the model responses and whether the model can reliably generate text conditioned on a desired set of tutoring actions. 2 Prior Work Tutoring Dialogue Corpus Creation Past work in creation of large publicly-available datasets of human-to-human tutoring interactions has been limited. Relevant past work which utilizes tutoring dialogue datasets draws from proprietary data collections (Chen et al., 2019;Rus et al., 2015a) or dialogues gathered from a student's interactions with an automated tutor (Niraula et al., 2014;Forbes-Riley and Litman, 2013). Open-access human-to-human tutoring data has been released in limited contexts. In particular, we draw inspiration from the BURCHAK work (Yu et al., 2017b), which is a corpus of humans tutoring each other with the names of colored shapes in a made-up foreign language. In each session, an image is given to help scaffold the dialogue. The corpus contains 177 conversations with 2454 turns in total. This corpus has been utilized to ground deep learning model representations of visual attributes (colors and shapes) in dialogue via interacting with a simulated tutor (Ling and Fidler, 2017;Yu et al., 2017b). Follow-up work has used this data to model a student learning names and colors of shapes using a reinforcement learning framework (Yu et al., 2016(Yu et al., , 2017a. Our approach differs from that of Yu et al. (2017b) in several ways, including that we tie the colored shape tutoring interactions to the more complex domain of prepositional phrases. Additionally, by using a real foreign language (Italian) we are able to leverage words with similar morphological properties in addition to well-defined grammar rules. 1 Cima is Italian for "top" and a target word in the dataset. The collection is available at: https://github.com/kstats/CIMA Learning Tutoring Dialogue Systems Modern work in dialogue falls into two categories: chit-chat models and goal-oriented models. Chit-chat models aim to creating interesting, diversely-worded utterances which further a conversation and keep users engaged. These models have the advantage of leveraging large indirectly-collected datasets, such as the Cornell Movie Script Dataset which includes 300,000 utterances (Danescu-Niculescu-Mizil and Lee, 2011). By contrast, goal oriented dialogue systems have a specific task to complete, such as restaurant (Wen et al., 2017) and movie (Yu et al., 2017c) recommendations as well as restaurant reservations (Bordes et al., 2017). Neural goal-oriented dialogue systems require large amounts of data to train. Bordes et al. (2017) include 6 restaurant reservation tasks, with 1,000 training dialogues in each dataset. Multidomain datasets such as MultiWOZ include 10k dialogues spanning multiple tasks (Budzianowski et al., 2018). For longer-term interactions, a dataset involving medical diagnosis has approximately 200 conversations per disease (Wei et al., 2018). By contrast, prior work in the field of intelligent tutoring dialogues has widely relied on large rule-based systems injected with human-crafted domain knowledge (Anderson et al., 1995;Aleven et al., 2001;Graesser et al., 2001;VanLehn et al., 2002;Rus et al., 2015b). Many of these systems involve students answering multiple choice or fillin-the-blank questions and being presented with a hint or explanation when they answer incorrectly. However, curating this domain knowledge is timeexpensive, rule-based systems can be rigid, and the typical system does not include multiple rephrasings of the same concept or response. Some recent work has brought modern techniques into dialogue-based intelligent tutoring, but has relied on hand-crafted rules to both map a student's dialogue utterance onto a template and generate the dialogue utterance to reply to the student (Dzikovska et al., 2014). A limitation of this is the assumption that there is a single "correct" response to show a student in a situation. Crowdwork Dialogue Role-Playing Prior work has shown that crowdworkers are effective at role-playing. Self-dialogue, where a single crowdworker role-plays both sides of a conversation, has been used to collect chit-chat data (Krause (Coetzee et al., 2015); multiple crowdworkers can confirm lexical information within a dialogue (Ono et al., 2017). Tutoring Dataset Creation We create two dialogue datasets within CIMA: Shapes and Prepositional Phrases with colored objects. Stimuli We constructed stimuli for the two tasks at different levels of complexity. The Shape task follows the BURCHAK (Yu et al., 2017b) format of learning the words for adjective-noun modifiers when viewing shapes of different colors (see Figure 1, left). The Prepositional Phrase stimuli involves pairs of objects in relation to one another, with the task of learning the words for the prepositional phrase and its object, where the object is a noun with a color modifier and a determiner (see Figure 1, right). Each stimulus consists of an image, a set of information points, and a question and answer pair. Importantly, the stimuli across the two tasks are linked by shared color terms. Intentionally including a set of common vocabulary words across datasets can potentially aid with transfer learning experiments (both human and machine). Initial tests were all done with English speakers learning the words in Italian. However, other language pairs can easily be associated with the image stimuli. Vocabulary for the Shape task includes six colors (red, blue, green, purple, pink, and yellow) and five shapes (square, triangle, circle, star, and heart). There is only one grammar rule associated with the questions: that adjectives follow nouns in Italian. The Prepositional Phrase task includes 6 prepositional phrases (on top of, under, inside of, next to, behind, and in front of) with 10 objects (cat, dog, bunny, plant, tree, ball, table, box, bag, and bed). Additionally, the same six colors as the Shape dataset modify the objects. Students are not asked to produce the subjects or the verbs, only the prepositional phrases. The full list of grammar rules (e.g. "l"' ("the") is prepended to the following word when it begins with a vowel) appears in Appendix A, and the full distribution of prepositional phrases, objects, and colors is in Appendix B. Dialogue Collection with Crowdworkers We hired crowdworkers on Amazon Mechanical Turk to role-play both the the student and the tutor. (Throughout this paper we will refer to them as students and tutors; this should be read as people taking on these roles.) In order to collect multiple tutoring responses at each point in a student conversation in a controllable way, student and tutor responses are gathered asynchronously. A diagram of this process can be seen in Figure 2. We collect several student conversations from crowdworkers with a fixed collection of hand-crafted and crowdworkergenerated tutor responses. Afterwards, we show those student conversations to tutors to collect multiple appropriate crowdworker-generated responses. We then feed the newly-collected responses into the fixed collection of tutor responses for the next Figure 2: Progression of data collection process. In the first round of data gathering (1A), the student is exposed to 20 conversational responses from a hand-curated set of templates (shown in blue). After gathering data from 20-40 students, each student conversation is subsequently sent to three tutors to gather responses (1B). These responses (shown in pink) are placed into the pool of tutor responses for subsequent rounds (ex: 2A). Figure 3: Example student exercise progression, showing shared features across stimuli. In this case, the student sees images for the words for bed and on top of twice within one session. round of student data collection. Tutors are asked to construct a response to the prior conversation with two outputs: the text of an utterance continuing the conversation and a discrete classification of the action(s) associated with the utterance. A summary of these actions for both the student and tutor can be seen in Table 1. Similarly, students produce utterances which they label with actions as they work through exercises (defined as a question and corresponding answer, see (A) in Figure 1). Students complete as many exercises as possible in a HIT, defined as the crowdworking task consisting of a fixed number of turns. Each turn is defined as a pair consisting of the student's utterance and the most recent tutor response it is replying to. A conversation is defined as the set of utterances that comprise completion of an exercise. Each participant can complete a maximum of 100 combined responses as a tutor or student for each task, to ensure diversity of responses. For the Shape task, students generate 5 responses per HIT. For the Prepositional Phrase task, however, we increase this to 20 responses per HIT due to the more complex domain. To ensure response quality, crowdworkers were required to have 95% approval over at least 1,000 HITs. A subset of responses from each crowdworker were manually checked. We prohibited workers from copying from the prior conversation or writing a blank response. Crowdworkers were paid the equivalent of $8/hour and were required not to know Italian to participate as a student. Figure 3 shows an example student interaction progression, in which students converse with the system to complete multiple exercises. Because the data collection process is asynchronous, when a student converses with the system, we serve a tutor response from a static collection to respond to them instantly. There are four rounds of data collection; in each phase, the pool of tutor responses Student Actions Action Label Description Example Guess The Student Role The student attempts to answer the question "Is it 'il gatto e vicino alla scatola rosa'?" Clarification Question The student asks a question to the tutor, ranging from directly asking for a translated word to asking why their prior guess was incorrect. "How would I say 'pink' in Italian?" Affirmation When the student affirms something previously said by the tutor. "Oh, I understand now!" Other We allow students to define a category if they do not believe their utterance fits into the predefined categories. "Which I just said." Tutor Actions Action Label Description Example Hint The tutor provides knowledge to the student via a hint. "Here's a hint -"tree" is "l'albero" because l' ("the") is prepended to the following word when it begins with a vowel." Open-Ended Question The tutor asks a question of the student, which can attempt to determine a student's understanding or continue the conversation. "Are you sure you have all the words in the right order?" Correction The tutor corrects a mistake or addresses a misconception a student has. "Very close. Everything is correct, expect you flipped 'viola' and 'coniglio'." Confirmation The tutor confirms a student's answer or understanding is correct. "Great! Now say the whole sentence, starting with the dog..." Other We allow tutors to define a category if they do not believe their response fits into the predefined categories. "Correct! Although try to think of the complete word as 'la scatola.' I find that the easiest way to remember what gender everything is -I just think of the 'the' as part of the noun." is augmented with the student and tutor responses from the prior round. For the Shape task, we gather responses from 20 students at each round; we increase this to 40 for Prepositional Phrase collection. The conversation is always started by the tutor, with a pre-defined statement. For subsequent turns, we choose a tutor response conditioned on the student's most recent action, a keyword match of a student's most recent text response, and a log of what the student has been exposed to in the current conversation (details are in Appendix C). As tutor responses are gathered from crowdworkers in subsequent rounds, we add them to the collection. Strategy for Student Exercise Selection A student session is constrained to have 5 or 20 turns, depending on the task. At the start of the session, the system selects a list of stimuli for the student to work through that contains overlapping concepts (prepositions, colors, objects, shapes). From this list, one is chosen at random to show first to the student. After the student completes the exercise, if another exercise exists in the list which overlaps with at least one concept shown in the prior exercise, it is chosen next. If there is not a question with overlap, an exercise is selected at random. This process continues until the student reaches the required number of turns. An example of a resulting image chain can be seen in Figure 3. Mitigating Effects of Potentially Erroneous Responses We adopted two strategies to reduce the cost of potential errors that may arise from automatically selecting tutoring responses to show to students: (i) Student crowdworkers can explicitly indicate if the tutor response they were served does not make sense. (ii) Because there is more downside to a nonsensical answer to some kinds of student responses than others (e.g., in response to a student's question vs. to an affirmation), each student action type is assigned a probability of being served a templated vs crowdworker-collected response (details in Appendix D). The Tutor Role Tutors for both the Shape and Prepositional Phrase tasks complete five responses per HIT. Because the data collection is asynchronous, the tutor is responding not to five consecutive utterances from the same student, but rather to five different students' conversations. To ensure good coverage, we inject three different tutors at each utterance within a student's conversation. This allows redundant generation of tutor responses to the same student input. We show the tutor the entire conversation up to that point. 2 To role-play a tutor, crowdworkers were not expected to have any proficiency in Italian. To simulate the knowledge a tutor would have, we show relevant domain information so the tutor could adequately respond to the student (see Figure 1(B)). This includes vocabulary and grammar information which are necessary to answer the question. This domain-specific information can also be used as input knowledge to inform a learning system. In the Prepositional Phrase task, we also showed summaries of prior student conversations, but do not describe this in detail due to space constraints. Dataset Statistics An analysis of the conversations found that the data contains several interesting properties from an educational perspective. This section summarizes overall statistics of the data collected; the subsequent two sections summarize phenomena associated with the student and tutor data. Shape Dataset A total of 182 crowdworkers participated in the Shape data collection process: 111 as tutors and 90 as students. 2,970 tutor responses were collected, responding to 350 student exercises. A student required an average of 3.09 (standard deviation: 0.85) turns to complete an exercise. The average student turn was 5.38 (3.12) words while the average tutor response length was 7.15 (4.53) words. 4.0% of tutor responses shown to students were explicitly 2 If a student conversation is longer than 10 turns, or if any point of the conversation has been marked as not making sense, the conversation is not shown to tutors. flagged by the student as not making sense. Table 2 shows the distribution of action types. Prepositional Phrase Dataset A total of 255 crowdworkers participated in the creation of Prepositional Phrase data: 77 as students who completed a total of 391 exercises, and 209 as tutors who completed 2880 responses. The average number of turns a student requires before answering a question correctly is 3.65 (2.12). Of the tutor responses served to students, 4.2% were manually flagged as not making sense. The average student utterance is 6.82 words (2.90) while the average length of a tutor utterance is 9.99 words (6.99). We analyze the proportion of tutoring responses which include the direct mention of an Italian color word, English translation of a color word, or "color," as this is the domain component which overlaps with the Shapes task. Of the set of tutor responses, 1,292 (40.0%) include a direct mention, indicating substantial overlap with the Shapes task. Student Interactions By examining a student's interactions over the course of a 20-turn HIT, we find that students take fewer turns on average to complete an exercise at the end than at the beginning of a HIT. We examine the number of turns students take before reaching the correct answer, as we hypothesize this will decrease as students have more exposure to domain concepts. We note this could be due to many factors, such as the students becoming more comfortable with the task or system or learning Italian phrases they were exposed to in prior questions. We measure this with the prepositional phrase domain, because the students interacted with the system for 20 turns, compared to the 5-turn interactions with the Shape task. For a given HIT, we compare the number of student turns needed to produce their first correct answer with how many turns were needed for their final correct answer. 3 For each student, we calculate the difference between the number of turns required between their first and final correct answers. The average difference is -0.723, indicating students required fewer turns to achieve their last correct answer than their first. Thus the data set might contain evidence of learning, although it could be as simple as student workers learning how to more efficiently ask questions of the system. Tutor Phenomena We examine several characteristics about the tutor interactions: (i) properties about the language tutors use in their responses, (ii) how tutors respond to different student action types, and (iii) characterizing if and how tutors agree when presented with identical student input. Tutoring Language One feature of our dataset construction is the progression from the relatively simple Shape task to the linguistically richer Prepositional Phrase task. We analyze the resulting tutoring responses to see if more complex tutoring language emerges from the syntactically richer domain. We measure complexity in terms of number of non-vocabulary terms (where vocabulary refers to the words that are needed in the task, such as "rosa" for "pink"). We examine the set of tutoring responses from each domain. For each utterance, we remove Italian vocabulary, English translations, and stop words. We further restrict the utterance to words included in the English language 4 to remove typos and misspellings. We find an average of 2.34 non-domain words per utterance (of average length 9.99 words) in the Prepositional Phrase dataset, compared to 0.40 per utterance (of average length 7.15 words) in the Shape dataset. While accounting for the average difference in length between the two datasets, the Prepositional Phrase dataset results in more nondomain English words than the Shape dataset. This supports our hypothesis that the added domain complexity makes the Prepositional Phrase collection richer in terms of tutoring language than related work such as Yu et al. (2017b). Tutor Response to Student Actions We additionally examine the tutor action distributions conditioned on the student action taken immediately prior for the Prepositional Phrase dataset. We hypothesize if a student utterance is classified as a question, the tutor will be more likely to respond with the answer to the question (classified as a hint), conforming to conversational expectations. This is supported by the distributions, seen in Figure 4. For other student action type responses (e.g., guess, affirmation), we observe that the tutor actions are more evenly distributed. Tutor Action Agreement As there are three tutors responding to each student utterance, we analyze the conditions in which the tutors agree on a unified set of actions to take in response to a student (in the Prepositional Phrase task). In particular, when all three tutors take the same set of action types we measure (i) which action(s) are they agreeing on and (ii) which action(s) the student took in the prior turn. In 212 out of 1174 tutor tasks, all 3 tutors agreed on the same set of actions to take. We show the distribution of these 212 cases over unified tutor action sets in Table 3. There is a particularly high proportion of agreement on giving hints compared to other action sets. While hint was the most common action taken by tutors compared to the nexthighest action by 26.5%, tutor agreement on hint was the most common by 75.4% compared to the next-highest category, a 2.8 times larger difference. Additionally, we examine how a student's most recent action might influence a group of tutor's potential for action agreement. We measure the proportion of tutor agreement on a unified action set per student action set (the analysis is restricted to student action sets with at least 10 examples). Results can be seen in Table 4. We note the highest agreement occurs after a student has made a Question or Question/Affirmation. This is consistent with (i) the high likelihood of a tutor to give a hint in response to a question ( Figure 4) and (ii) the high proportion of tutor agreement on hints (Table 3). On the contrary, there is relatively low agreement when a student makes a Guess, consistent with the more evenly-distributed tutor action distribution (Figure 4). Figure 4: Distribution of tutor action classifications, grouped by the most recent set of student actions. The "All Other" category represents the combination of tutor action sets with fewer than 15 items. Student Action(s) Tutor Agreement Question 36.8% Question/Affirmation 37.5% Affirmation 12.3% Guess 6.4% Guess/Affirmation 5.6% Table 4: For each student action(s), percentage of tutor groups who agree on a unified action set in response. Tutoring Model We claim CIMA is useful to train neural models for tutoring tasks. To explore this, we train a Generation model (GM) aimed at producing a tutoring response conditioned on two past conversation utterances. 5 An example input would be: Hint, Correction, e di fronte al, giallo, coniglio, is in front of the, yellow, bunny, <EOC> Tutor: Well, "bunny" is "coniglio" Student: il gatto e di fronte al coniglio. In this representation, domain information and an intended set of actions to take is separated with a special token <EOC> from two sentences of conversation. Model training details are in Appendix E. We split the data along conversations into 2296 train, 217 development, and 107 test utterances. Generation Quality Results One benefit of CIMA is the ability to compare generated text to multiple distinct reference sentences in order to measure quality. We apply two standard generation quality measures: BLEU (Papineni et al., 2002) and BERT F1 Score (Zhang* et al., 2020), using the maximum score of the model's response compared to each of the three humangenerated tutor responses for a turn in the conversation. We compare the quality of the GM's responses to Round 1A of the same rule-based system used to collect CIMA (see Appendix C). Results can be seen in Table 5. We note the rule-based baseline (which is guaranteed to be grammatical) performs slightly better than GM on BLEU score (which incentivizes exact word overlap) but that GM performs higher on BERT F1 Score (which incentivizes semantic word overlap). Given the comparable BLEU score and the gain on BERT F1 Score, we conclude that using CIMA to train a neural model can produce tutoring utterances of reasonable quality. Action Evaluation Results In addition to quality, we examine whether the Generation model is able to generate utterances consistent with the set of actions it is conditioned on. We train a separate Action Classifier (AC) to predict a set of actions from a tutoring utterance. For example, for the input Tutor: Well, "bunny" is "coniglio." Do you know the word for yellow? the classifier would output Hint Question. Training details appear in Appendix E. To examine the classifier's reliability, we mea- sure the F1 for the test set, both overall and for each of the four top action categories (excluding Other due to the low number of utterances). Results can be seen in Table 6. While the Overall, Hint, and Question F1 are relatively high, we note the lower Correction and Confirmation scores. Using the classifier, we measure the GM's ability to generate utterances consistent with the set of actions it is conditioned on. For each item in the test set, we sample one of the three tutor's responses, identify the action(s) that tutor chose to make, and use GM to generate an utterance conditioned on that action type. To determine if the generated utterance is of the correct action type, we apply the classifier model. The average accuracy over the test set is 89.8%, indicating GM's ability to generate utterances consistent with an action strategy. Discussion Our analysis finds that tutors are more unified in an action strategy when a student asks a question than other actions. This is consistent with the findings that (i) when tutors agree, they are more likely to agree on a hint and (ii) the most likely action in response to a student question is a hint. Overall tutor agreement was low among the dataset (18.1%), indicating the potential capture of divergent tutoring strategies. Future work can leverage this disagreement to explore the multiple potential actions to take when responding to a student. Our preliminary experiments show CIMA can be used to train a model that can generate text conditioned on a desired actions. Future work should explore more complex models utilizing CIMA, as well as exploring the other unique qualities of the collection, such as the shared image representation, multiple tutoring utterances for each conversation, and link between the two domains. Tutoring responses marked as not making sense should be explored, to both improve the process of serving student responses as well as correcting a model when a generated response veers the conversation off track. A benefit to having this explicitly logged is that the collection contains labeled negative examples of tutoring responses, which can be leveraged in training models. Limitations While past work utilized crowdworkers to collect tutoring utterances (Yu et al., 2017b) and for peer learning studies (Coetzee et al., 2015), future work should examine the similarities and differences between the language and actions taken by crowdworkers and actual tutors and students engaged in the learning process. Because we were working with untrained crowdworkers, we were constrained in the complexity of language learning concepts we could include in CIMA. It is possible that the resulting dataset only transfers to novice language learners. Future work should examine how well this generalizes to a real language learning setting and how general tutoring language and strategies that emerge from our domain transfer to more complex ones. The dataset currently does not distinguish the type of hint or correction tutors make. Examples include providing direct corrections versus indirect feedback which states the error and allows the student to self-correct (Chandler, 2003). Future work on CIMA can examine the prevalence of these different types of feedback and potential benefits or shortcomings. Conclusion We present CIMA: a data collection method and resulting collection of tutoring dialogues which captures student interactions and multiple accompanying tutoring responses. Two datasets of differing complexity have direct applicability to building an automatic tutor to assist foreign language learning, as we examine with a preliminary model. CIMA has the potential to train personalized dialogue agents which incorporate longer-term information, have a well-defined goal to have a student learn and recall concepts, and can explore different correct utterances and actions at given times. A Prepositional Phrase Collection Grammar Rules Listed below is the complete collection of Prepositional Phrase grammar rules: • "il" ("the") is used for when the following word is masculine. • "alla" ("to the") is used when the following word is feminine and a singular object. It is a contraction of a ("to") and la ("the"). • "al" ("to the") is used when the following word is masculine and a singular object. It is a contraction of the words a ("to") and il ("the"). • "l"' ("the") is prepended to the following word when it begins with a vowel. • "all" ("to the" is prepended to the following word when it begins with a vowel. This is a contraction of al ("to") and l' ("the"). • "rossa" is the feminine form of red because the noun it modifies is feminine • Adjectives (such as color words) follow the noun they modify in Italian. • Prepositional phrases separate the two noun phrases. Table 7 shows the coverage of Prepositional Phrase exercises over the potential objects, prepositional phrase, and colors. B Phrase Breakdown C Algorithm Algorithmic specifications for data collection can be viewed in Figure 5. In order to serve a tutorcrafted response to a student, we match the current student utterance to a prior-collected student utterance which has been responded to by a tutor. The most similar student utterance is determined by maximizing word overlap of the student's most recent utterance to the prior-collected student utterances, excluding domain vocabulary words. The English and Italian words are replaced with the information relevant to the current exercise in the associated tutor utterance before showing this to the student. D Hand-Crafted Response Probabilities Throughout different rounds of data collection, we balance the probability of a student receiving a premade tutor response with a crowdworker-generated response from a prior round of data collection. As we collect more tutoring responses in subsequent rounds, the probabilities shift from pre-made, safe choices to the crowd-worker generated responses, because with more data, the choices should be more likely to more closely match a student utterance. The probabilities were manually set and can be seen in Table 8. Table 8: Probabilities for serving hand crafted responses instead of tutor-provided responses for the shape and the prepositional phrase task, rounds 1 -4. for Guess, Question, Action, and Other student question types. 64 E Model Training Details For the Generation Model, we use OpenNMT (Klein et al., 2017) to train a 4-layer LSTM of size 1000 with global attention. We use the Adam optimizer (Kingma and Ba, 2015) with a learning rate of 0.001. We allow the model to have a copy mechanism to copy relevant words (such as translation information) from the input (Gu et al., 2016). We use 300-dimensional pre-trained GloVe embeddings (Pennington et al., 2014), which are allowed to be updated throughout training. At decode time, we replace unknown words with the input word with the highest attention. We train the Action Classification model using OpenNMT (Klein et al., 2017). The model is a 4-layer bidirectional LSTM with 1024 hidden state size, general attention, a learning rate of 0.001, and batch size of 16. It utilizes pre-trained 300dimensional GloVe embeddings (Pennington et al., 2014) which can be updated. This model is trained on the same training set as the generation model, taking in the human-created utterances and predicting the corresponding classifications. Figure 1 : 1Example exercises as seen by a tutor (Left: Shape task, Right: Prepositional Phrase task). Shown are (A) the exercise with the correct answer that the student must produce, (B) knowledge in the form of information bullet points, (C) the image stimulus, and (D) the conversation so far. The student view is similar but does not include the information bullet points or the correct answer. et al., 2017). Crowdworkers have been effective participants in peer learning studies Figure 5 : 5Algorithm for serving tutoring responses. Table 1 : 1Descriptions of Student and Tutor Actions that workers self-assign to their utterances. Table 2 : 2Distribution of student and tutor actions across the two datasets; multiple actions can be associated with each utterance. Table 3 : 3Distribution of action sets agreed on by 3-tutor groups. Included are the proportion of individual tutor utterances labeled with each set of actions over the en- tire dataset for comparison. Table 5 : 5Generation quality results comparing a rule- based baseline to the neural Generation model. Table 6 : 6Action Classification model F1 scores for the test set, where the Overall metric is weighted by class. Table 7 : 7Phrase Breakdown of Student conversations if Guess and Correct then Move on to next question else if Guess and Incorrect then Flip a coin with probability = G if Heads then Compile a list of pre-defined responses containing vocabulary missing from the student response Randomly select from this list else Return the most similar past-tutor response from the set of responses of type G. end if end if if Question then Flip a coin with probability = Q if Heads then Attempt to find words from a set list of pre-defined hints associated with each vocabulary word in the question. if Match is Found then Serve that hint else Choose a random hint that the student has not seen and serve that. end if else Return the most similar past-tutor response from the set of responses of type Q. end if end if if Affirmation or Other then Flip a coin with probability = A / O if Heads then Flip a coin with probability = 0.5 if Heads then Ask the student for an attempt at a guess. else Give a pre-defined hint for a vocabulary or grammar concept that the student has not yet seen. end if else Return the most similar past-tutor response from the set of responses of type A/O. end if end if Note the final correct question might not be the final question the student attempted to answer, as the HIT is finished at 20-turns regardless of the state of a student's conversation. 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The Usefulness of Bibles in Low-Resource Machine Translation March 2-3, 2021 Ling Liu ling.liu@colorado.edu University of Colorado Zach Ryan zachary.j.ryan@colorado.edu University of Colorado Mans Hulden mans.hulden@colorado.edu University of Colorado The Usefulness of Bibles in Low-Resource Machine Translation Papers the 4th Workshop on the Use of Computational Methods in the Study of Endangered LanguagesOnline1March 2-3, 202144 Bibles are available in a wide range of languages, which provides valuable parallel text between languages since verses can be aligned accurately between all the different translations. How well can such data be utilized to train good neural machine translation (NMT) models? We are particularly interested in low-resource languages of high morphological complexity, and attempt to answer this question in the current work by training and evaluating Basque-English and Navajo-English MT models with the Transformer architecture. Different tokenization methods are applied, among which syllabification turns out to be most effective for Navajo and it is also good for Basque. Another additional data resource which can be potentially available for endangered languages is a dictionary of either word or phrase translations, thanks to linguists' work on language documentation. Could this data be leveraged to augment Bible data for better performance? We experiment with different ways to utilize dictionary data, and find that word-to-word mapping translation with a word-pair dictionary is more effective than low-resource techniques such as backtranslation or adding dictionary data directly into the training set, though neither backtranslation nor word-to-word mapping translation produce improvements over using Bible data alone in our experiments. Introduction The Bible has been translated into a wide range of languages, including many low-resource ones, and a significant amount of Bible data is publicly available. For example, www.bible.com has a collection of 2,172 Bible versions in 1,482 languages. This provides valuable parallel text between languages for training machine translation models as well as for conducting other cross-lingual studies. Neural machine translation (NMT) models have been pushing forward the state of the art in recent years Vaswani et al., 2017;Barrault et al., 2019). However, millions of parallel tokens are usually required in order to train a NMT model with high quality (Koehn and Knowles, 2017;Sennrich and Zhang, 2019), an amount much larger than the whole Bible. For example, the Holy Bible, New Living Translation on www.bible.com contains around 880,000 tokens. If we use Bible data to train a NMT model between English and a morphologically complex language like Basque or Navajo, what quality could we achieve and what can we do to improve the performance? In the current work, we attempt to evaluate the Transformer architecture trained on Bible data for translating between Basque and English, and between Navajo and English. First, we experiment with different tokenization approaches for preprocessing the morphologically complex languages, including only separating the punctuation from the words, dividing words into syllables, and applying the popular byte-pair encoding (BPE) algorithm (Gage, 1994). A novel result is that we found that data preprocessing with syllabification is promising for morphologically complex languages: preprocessing Navajo with the syllabification method always produces the best performance whether it is translating Navajo into English or the other way around, and it is the method which produces the second highest BLEU score when translating Basque from and into English. Thanks to linguists' work on language documentation, dictionary data are also available for some endangered languages. Could this data be utilized to augment the Bible data in order to achieve better machine translation quality? To answer this question, we experiment with three dif-ferent ways of utilizing dictionary data to train the models. First, we add individual example pairs of sentences or phrases from the dictionary directly to the Bible training dataset. Second, in order to alleviate the cross-domain problem, we also experiment with only adding word pairs to the Bible data for training. Third, inspired by Nag et al. (2020), we experiment with the data augmentation approach of mapping additional English texts into Basque with the word-to-word dictionary data, which are then combined with the Bible data to train the Basque to English NMT model. This word-to-word translation data augmentation approach is compared with the commonly used backtranslation data augmentation method (Sennrich et al., 2016). However, neither the additional dictionary data nor any of the data augmentation method produces improvements in the BLEU score over using only the Bible data in our experiments, though the word-to-word mapping data augmentation method is the best of all the additional data approaches we apply. Experiments Data and data preprocessing The site www.bible.com provides Bible texts in different languages, where the Bible text in each language is organized with book-title:chapterid:verse-id 1 and the identifiers agree with each other between different languages. Therefore, we can create parallel text at the verse level with the identifiers. We experiment with Basque-English and Navajo-English machine translation. For English (eng), we use the Holy Bible, New Living Translation (NLT), for Navajo (nvj), we use the Navajo Bible (NVJOB), and for Basque (eus), we use the Elizen Arteko Biblia (Biblia en Euskara, Traducción Interconfesional) (EAB). This gives us around 31.7k parallel Basque-English verses and around 31.8k parallel Navajo-English verses. More detailed statistics can be found in Table 1. We split the parallel verses into training, development and test sets with a ratio of 7:1:2. The English data is preprocessed by segmenting punctuation from words. Considering that Basque and Navajo have very rich inflection patternsfor verbs in particular-we experiment with different tokenization methods for these two languages. The same tokenization method as En- glish is used as a baseline, which is referred to as tok. In addition to that, we experiment with segmenting Basque or Navajo words by syllables, for which we manually develop finite-state transducers, using foma (Hulden, 2009), to break up words into syllables for each language. We refer to this method as syl. We also experiment with the bytepair encoding (BPE) algorithm with a vocabulary size of 8,000 and 16,000 respectively, which are referred to as bpe-8k and bpe-16k. Syllabifier details As in Agirrezabal et al. (2012) we treat Basque syllabification as following the maximum onset principle and a standard sonority hierarchy. Such syllabifiers can be built with a finite-state transducer (FST) constructed with a single rewrite rule which inserts syllable boundaries after legal syllables following a leftmost-shortest strategy (Hulden, 2005). Leftmost-shortest rewrite rule compilation is implemented in many standard FST toolkits such as foma (Hulden, 2009), the Xerox tools (Beesley and Karttunen, 2003), or Kleene (Beesley, 2012). Navajo has a relatively simple syllable structure, disallowing onsetless syllables (McDonough, 1990), and can thus be divided through a rule that always places the syllable boundaries before any CV sequence. Both languages have digraphs, such as Basque tx (=[ > tS]), while Navajo also features trigraphs for ejectives such as ch' (=[ > tS']), all of which need to be modeled as single consonants in the FST. Figure 1 shows example outputs of our syllabifiers in both languages, and Figure 2 shows the essential parts of the FST construction in foma code. Model specifics and evaluation For the neural machine translation model, we employ the self-attention Transformer architec-batzuetan ba@ @tzu@ @e@ @tan oraindik o@ @rain@ @dik Donostiarako Do@ @nos@ @ti@ @a@ @ra@ @ko kotxearekin ko@ @txe@ @a@ @re@ @kin ahizpa a@ @hiz@ @pa łééchąą'í łéé@ @chąą@ @'í shi'niiłhį shi'@ @niił@ @hį náninichaadísh ná@ @ni@ @ni@ @chaa@ @dísh nahwiilzhooh na@ @hwiil@ @zhooh ch'éénísdzid ch'éé@ @nís@ @dzid Basque Navajo Orthography Syllabified Figure 1: Example outputs of FST syllabifiers for Basque and Navajo. The output (right) of the FST is in BPE-format with double @-signs for word-internal syllables, and single @-signs at the word edge. ture (Vaswani et al., 2017) as implemented in the Fairseq toolkit (Ott et al., 2019 (Papineni et al., 2002) is used as the metric to evaluate the NMT model generation output throughout. Note that in this paper the BLEU score is measured over word overlap, not word-piece overlap. Using dictionary data We experiment with incorporating dictionary data in three different ways. For Basque-English, we use the Elhuyar dictionary, 3 whose phrase examples give us around 14.9k parallel sentences or phrases, and around 80.6k word pairs. For Navajo-English, we extract around 14.8k parallel sentences or phrases from the Young & Morgan dictionary (Young and Morgan, 1987). We omit the word-pair experiments for Navajo. The first way of incorporating the dictionary data is to add the sentence or phrase pairs from the dictionary to the Bible training set to train the NMT model. We refer to this method as +dict. This experiment is conducted for both Basque-English and Navajo-English. Considering that the nature of the text used in the dictionary example data may be different from the Bible data, which can cause a crossdomain problem, we experiment with adding only word pairs from the dictionary for Basque-English translation, referred to as +w2w. However, it's common in dictionaries that a word in one language is mapped to multiple words in another language or different word in one language is mapped to the same word in another language. For example, we get two English translations polite and kind for the Basque word adeitsu in the dictionary, and Basque words adabatu, adaba and adabatzen are all translated as to patch in English without keeping their inflection information. The dictionary lists these different stems to inform the reader what stem alternations occur in different verb inflections. To avoid such ambiguity, we experiment with randomly picking one word pair when multiple mappings are found in the dictionary data. 4 This is referred to as +w2w-rnd as opposed to +w2w which keeps the multiple mappings. The third way of using the dictionary data is a combination of using dictionary data and monolingual data: We use the word pairs from the dictionary to translate extra monolingual English data (see section 2.5 for details) word by word into Basque, and augment the Bible training set with this translated data. When conducting the wordby-word translation from English to Basque, we randomly pick one translation if the dictionary provides multiple Basque translations for an English word. In cases where an English word does not appear in the dictionary, we copy the English word as Basque translation. Using monolingual data When the task is to translate a low-resource language to a high-resource language, the monolingual data for the high-resource language can be utilized to augment the training data (Przystupa and Abdul-Mageed, 2019; Chen et al., 2020;Edunov et al., 2020). One popular way to do it is through backtranslation (Sennrich et al., 2016). The way backtranslation works is first to train a model with the initial data for translation from the high-resource language to the low-resource language, then to utilize the model from the previous step to translate the monolingual data for the def MainSyll Cons* Vow+ Cons* @-> ... "^" || _ Cons Vow ; def PostSyll "^" -> "@" " " "@"; high-resource language to the low-resource language, and add the noisy translated data to the original training data to train the model for translation from the low-resource language to the highresource language. We experiment with the backtranslation data augmentation method for Basque to English and Navajo to English translation. We also experiment with the word-to-word translation by utilizing word pairs from a dictionary (Nag et al., 2020) for Basque to English translation, as described in section 2.4. The monolingual data we use is a collection of news data from the English Gigaword archive (5th edition) (Parker et al., 2011) (specifically nyt eng 2010 and wpb eng 2010), from which we randomly pick one (+1 * ) to seven (+7 * ) times the number of the Bible training set verses to compare leveraging different amounts of monolingual data. Table 2 presents the BLEU scores on the test set for different tokenization methods and training data. When the translation is into English, BPE with a vocabulary size of 8,000 produces the best result for Basque, and syllabification tokenization produces the best performance for Navajo. When the translation is from English, only segmenting the punctuation from the words gives us the best performance for Basque, which is slightly higher than syllabification, and syllabification is the tokenization method which achieve the highest BLEU score for Navajo. As regards adding dictionary data to Bible training set, for the translation in both directions for both Basque-English and Navajo-English, adding additional dictionary data did not improve the BLEU score, though adding example sentence or phrase pairs turns out to be less harmful than adding only word pairs. Adding additional dictionary examples lowers the BLEU score, be it word pairs or sentence or phrase pairs, may be be- Table 2: BLEU scores on the test set for translating Basque or Navajo into or from English, with different tokenization methods for Basque and Navajo, and different training data. The English data is always tokenized by separating the word from the punctuation. Results and discussion cause the text used in the dictionary examples are different from Bible text. One reason why adding word pairs harms the performance so much may be related to the complex morphological inflection of Basque words: the additional dictionary forms of Basque words induce the model to tend to produce dictionary forms rather than particular inflected forms required by the context. Figure 3(a) plots the performance of the NMT model for Basque to English translation when we augment the Bible training data by word-to-word translating different amounts of English news data to Basque. It shows that the performance is worse than using the Bible training data alone (Except that for bpe-16k tokenization, adding 1 time word-to-word translated data outperforms using Bible training set alone, though the BLEU score is still lower than using Bible training set alone with bpe-8k tokenization), and the more translated data is added, the worse the performance becomes. One reason for the negative effect of leveraging monolingual data here may be that the news text and Bible text are quite different. Another reason may be that the dictionary data is too limited: around 43% of the English tokens can't be found in the word pairs we extract from the dictionary, which influences the translation quality. Other reasons may be that the word-to-word mapping translation does not inflect the Basque words, which is critical for a morphologically complex language, or that the randomly picked words do not match the context well. Therefore, though data augmentation by word-to-word mapping translation of monolingual data has been shown to be helpful in the literature (Nag et al., 2020), our experiments indicate that the helpfulness may depend on the size and quality of the word-to-word dictionary and the morphological complexity as well as the word ambiguity of the languages involved. Our backtranslation results, shown in Figure3 (b) and (c), support the widely recognized fact about backtranslation that the quality of highresource language to low-resource language translation model is positively related to backtranslation quality (Currey et al., 2017). When the high-resource to low-resource language translation model is too poor, adding backtranslated data actually creates more noise, and thus hurts performance. We see in our results that adding backtranslated data does not improve BLEU scores for Basque or Navajo over using Bible data alone, and we observe that the Basque-English results with backtranslation is even worse than adding the word-to-word translated data, indicating the poor quality of the backtranslated data. Conclusion We have performed a systematic evaluation on using Bible data for machine translation in two highly morphologically complex languages. The overarching result is that-while even unsupervised MT has been shown possible in some cases (Artetxe et al., 2018;Lample et al., 2017)-using only the Bible, together with possibly a phrase or word dictionary and standard tools of the trade such as backtranslation even with state-of-the-art sequence-to-sequence models is unlikely to pro- duce very useful MT quality. The best BLEU score is only over 11 when the translation is into English and lower than 9 when the translation is out of English. Future work is needed to improve the machine translation quality in this scenario. An important finding in this paper is that syllabification as opposed to BPE or other sub-word tokenization methods may be helpful for morphologically complex languages like Basque and Navajo. We also experimented with leveraging dictionary data to increase the training data for translation in both directions or augmenting the training data for low-to high-resource language translation by utilizing monolingual data for the high-resource language, though we did not achieve improvements in the BLEU score over using Bible data alone. Data augmentation by word-to-word mapping translation of monolingual data with word pairs obtained from dictionaries outperforms backtranslation, but still did not achieve better performance than using only Bible training set. The word-toword mapping augmentation approach can be improved by converting the mapped words to their correct inflected forms or selecting more contextappropriate candidates when multiple mappings are possible, which can be explored in future work. Figure 2 : 2Foma code example for the syllabifier. Some repetitive parts of vowel, consonant and diphthong specifications are omitted for compactness. Figure 3 : 3Comparison of * → English translation after adding different amounts of monolingual data. Xaxis indicates the augmentation rate: 0 means using only Bible training set, 1 means adding 1 time translated data that of the Bible training set verses, etc. ).2 The Transformer model we use has 4 encoding layers and 4 attention heads with an embedding dimension of 256 and hidden layer size of 1024. Its decoding layer, attention head and dimensions have the same setting. The model is trained with a batch size of 16 for 120k maximum number of updates. Beam search with a width of 5 is used for generation. More details on the hyperparameters and training heuristics are provided in Appendix A.1. The BLEU score def Syllabifier MainSyll .o. PostSyll; def Vow [a|e|i|o|u|A|E|I|O|U|ai|ei|oi|ui|(rest of vowels)]; def Cons [b|c|d|t z|t x|t s|f|g|h|j|k|l|(rest of consonants)]; def PreSyll a i -> ai , e i -> ei, (rest of diphthongs) || [Cons|Vow] _ [Cons|Vow];def Syll [(Cons|[t|p] r) Vow (Cons)]; def MainSyll Syll @> ... "^" || _ Syll ; def PostSyll "^" -> "@" " " "@"; def Syllabifier PreSyll .o. MainSyll .o. PostSyll; Navajo syllabifier skeleton Basque syllabifier skeleton Each verse may or may not be one sentence, depending on the translation. https://fairseq.readthedocs.io/en/ latest/ 3 https://hiztegiak.elhuyar.eus/ The way to choose words in such cases can be improved, for example, by conducting morphological analysis of the words since some of the multiple mappings are due to different inflected forms of the morphologically complex language. A.2 Data augmentation resultsHere are the results for adding monolingual data with word-to-word mapping translation by dictionary pairs and with backtranslation. 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Table 4: BLEU scores on the test sets for Basque and Navajo when models are trained on Bible training set plus backtranslated English news data. Table 4: BLEU scores on the test sets for Basque and Navajo when models are trained on Bible training set plus backtranslated English news data.

Annotation Tools Based on the Annotation Graph API

Annotation graphs provide an efficient and expressive data model for linguistic annotations of time-series data. This paper reports progress on a complete open-source software infrastructure supporting the rapid development of tools for transcribing and annotating time-series data.This generalpurpose infrastructure uses annotation graphs as the underlying model, and allows developers to quickly create special-purpose annotation tools using common components. An application programming interface, an I/O library, and graphical user interfaces are described. Our experience has shown us that it is a straightforward task to create new special-purpose annotation tools based on this general-purpose infrastructure.

Annotation Tools Based on the Annotation Graph API Steven Bird Linguistic Data Consortium University of Pennsylvania 3615 Market Street, Suite 20019104-2608PhiladelphiaPAUSA Kazuaki Maeda maeda@ldc.upenn.edu Linguistic Data Consortium University of Pennsylvania 3615 Market Street, Suite 20019104-2608PhiladelphiaPAUSA Xiaoyi Ma Linguistic Data Consortium University of Pennsylvania 3615 Market Street, Suite 20019104-2608PhiladelphiaPAUSA Haejoong Lee haejoongg@ldc.upenn.edu Linguistic Data Consortium University of Pennsylvania 3615 Market Street, Suite 20019104-2608PhiladelphiaPAUSA Annotation Tools Based on the Annotation Graph API Annotation graphs provide an efficient and expressive data model for linguistic annotations of time-series data. This paper reports progress on a complete open-source software infrastructure supporting the rapid development of tools for transcribing and annotating time-series data.This generalpurpose infrastructure uses annotation graphs as the underlying model, and allows developers to quickly create special-purpose annotation tools using common components. An application programming interface, an I/O library, and graphical user interfaces are described. Our experience has shown us that it is a straightforward task to create new special-purpose annotation tools based on this general-purpose infrastructure. Introduction In the past, standardized file formats and coding practices have greatly facilitated data sharing and software reuse. Yet it has so far proved impossible to work out universally agreed formats and codes for linguistic annotation. We contend that this is a vain hope, and that the interests of sharing and reuse are better served by agreeing on the data models and interfaces. Annotation graphs (AGs) provide an efficient and expressive data model for linguistic annotations of time-series data (Bird and Liberman, Recently, the LDC has been developing a complete software infrastructure supporting the rapid development of tools for transcribing and annotating time-series data, in cooperation with NIST and MITRE as part of the ATLAS project, and with the developers of other widely used annotation systems, Transcriber and Emu (Bird et al., 2000;Barras et al., 2001;Cassidy and Harrington, 2001). The infrastructure is being used in the development of a series of annotation tools at the Linguistic Data Consortium. Two tools are shown in the paper: one for dialogue annotation and one for interlinear transcription. In both cases, the transcriptions are time-aligned to a digital audio signal. This paper will cover the following points: the application programming interfaces for manipulating annotation graph data and importing data from other formats; the model of inter-component communication which permits easy reuse of software components; and the design of the graphical user interfaces. 2 Architecture 2.1 General architecture Figure 1 shows the architecture of the tools currently being developed. Annotation tools, such as the ones discussed below, must provide graphical user interface components for signal visualization and annotation. The communication between components is handled through an extensible event language. An application programming interface for annotation graphs has been developed to support well-formed operations on annotation graphs. This permits applications to abstract away from file format issues, and deal with annotations purely at the logical level. The annotation graph API The application programming interface provides access to internal objects (signals, anchors, annotations etc) using identifiers, represented as formatted strings. For example, an AG identifier is qualified with an AGSet identifier: AGSetId:AGId. Annotations and anchors are doubly qualified: AGSetId:AGId:AnnotationId, AGSetId:AGId:AnchorId. Thus, the identifier encodes the unique membership of an object in the containing objects. We demonstrate the behavior of the API with a series of simple examples. Suppose we have already constructed an AG and now wish to create a new anchor. We might have the following API call: CreateAnchor("agSet12:ag5", 15.234, "sec"); This call would construct a new anchor object and return its identifier: agSet12:ag5:anchor34. Alternatively, if we already have an anchor identifier that we wish to use for the new anchor (e.g. because we are reading previously created annotation data from a file and do not wish to assign new identifiers), then we could have the following API call: CreateAnchor("agset12:ag5:anchor34", 15.234, "sec"); This call will return agset12:ag5:anchor34. Once a pair of anchors have been created it is possible to create an annotation which spans them: CreateAnnotation("agSet12:ag5", "agSet12:ag5:anchor34", "agSet12:ag5:anchor35", "phonetic" ); This call will construct an annotation object and return an identifier for it, e.g. agSet12:ag5:annotation41. We can now add features to this annotation: SetFeature("agSet12:ag5:annotation41", "date", "1999-07-02" ); The implementation maintains indexes on all the features, and also on the temporal information and graph structure, permitting efficient search using a family of functions such as: GetAnnotationSetByFeature( "agSet12:ag5", "date", "1999-07-02"); A file I/O library A file I/O library (AG-FIO) supports input and output of AG data to existing formats. Formats currently supported by the AG-FIO library include the TIMIT, BU, Treebank, AIF (ATLAS Interchange Format), Switchboard and BAS Partitur formats. In time, the library will handle all widely-used signal annotation formats. Figure 2 shows the structure of an annotation tool in terms of components and their communication. Inter-component communication The main program is typically a small script which sets up the widgets and provides callback functions to handle widget events. In this example there are four other components which Both GUI components and the main program support a common API for transmitting and receiving events. For example, GUI components have a notion of a "current region" -the timespan which is currently in focus. A waveform component can change an annotation component's idea of the current region by sending a SetRegion event (Figure 3). The same event can also be used in the reverse direction. The main program routes the events between GUI components, calling the annotation graph API to update the internal representation as needed. With this communication mechanism, it is straightforward to add new commands, specific to the annotation task. Reuse of software components The architecture described in this paper allows rapid development of special-purpose annotation tools using common components. In particular, our model of inter-component communication facilitates reuse of software components. The annotation tools described in the next section are not intended for general purpose annotation/transcription tasks; the goal is not to create an "emacs for linguistic annotation". Instead, they are special-purpose tools based on the general purpose infrastructure. These GUI Graphical User Interfaces A spreadsheet component Dialogue annotation typically consists of assigning a field-structured record to each utterance in each speaker turn. A key challenge is to handle overlapping turns and back-channel cues without disrupting the structure of individual speaker contributions. The tool side-steps these problems by permitting utterances to be independently aligned to a (multi-channel) recording. The records are displayed in a spreadsheet; clicking on a row of the spreadsheet causes the corresponding extent of audio signal to be highlighted. As an extended recording is played back, annotated sections are highlighted, in both the waveform and spreadsheet displays. Figure 4 shows the tool with a section of the TRAINS/DAMSL corpus (Jurafsky et al., 1997). Note that the highlighted segment in the audio channel corresponds to the highlighted annotation in the spreadsheet. An interlinear transcription component Interlinear text is a kind of text in which each word is annotated with phonological, morphological and syntactic information (displayed under the word) and each sentence is annotated with a free translation. Our tool Figure 5: Interlinear Transcription Tool permits interlinear transcription aligned to a primary audio signal, for greater accuracy and accountability. Whole words and sub-parts of words can be easily aligned with the audio. Clicking on a piece of the annotation causes the corresponding extent of audio signal to be highlighted. As an extended recording is played back, annotated sections are highlighted (both waveform and interlinear text displays). The screenshot in Figure 5 shows the tool with some interlinear text from Mawu (a Manding language of the Ivory Coast, West Africa). A waveform display component The tools described above utilize WaveSurfer and Snack (Sjölander, 2000;Sjölander and Beskow, 2000). We have developed a plug-in for WaveSurfer to support the inter-component communication described in this paper. Available Software and Future Work The Annotation Graph Toolkit, version 1.0, contains a complete implementation of the annotation graph model, import filters for several formats, loading/storing data to an annotation server (MySQL), application programming interfaces in C++ and Tcl/tk, and example annotation tools for dialogue, ethology and interlinear text. The supported formats are: xlabel, TIMIT, BAS Partitur, Penn Treebank, Switchboard, LDC Callhome, CSV and AIF level 0. All software is distributed under an open source license, and is available from http://www.ldc.upenn.edu/AG/. Future work will provide Python and Perl interfaces, more supported formats, a query language and interpreter, a multichannel transcription tool, and a client/server model. Conclusion This paper has described a comprehensive infrastructure for developing annotation tools based on annotation graphs. Our experience has shown us that it is a simple matter to construct new specialpurpose annotation tools using high-level software components. The tools can be quickly created and deployed, and replaced by new versions as annotation tasks evolve. Figure 1 : 1Architecture for Annotation Systems 2001). Figure 2 : 2The Structure of an Annotation Tool Figure 3 : 3Inter-component Communication are reused by several annotation tools. The AG and AG-FIO components have already been described. The waveform display component (of which there may be multiple instances) receives instructions to pan and zoom, to play a segment of audio data, and so on. The transcription editor is an annotation component which is specialized for a particular coding task. Most tool customization is accomplished by substituting for this component. Figure 4 : 4Dialogue Annotation Tool for the TRAINS/DAMSL Corpus components can be modified or replaced when building new special-purpose tools. AcknowledgementsThis material is based upon work supported by the National Science Foundation under Grant Nos. 9978056, 9980009, and 9983258. Transcriber: development and use of a tool for assisting speech corpora production. Claude Barras, Edouard Geoffrois, Zhibiao Wu, Mark Liberman, Speech Communication. 33Claude Barras, Edouard Geoffrois, Zhibiao Wu, and Mark Liberman. 2001. Transcriber: development and use of a tool for assisting speech corpora production. Speech Communication, 33:5-22. A formal framework for linguistic annotation. Steven Bird, Mark Liberman, Speech Communication. 33Steven Bird and Mark Liberman. 2001. A formal framework for linguistic annotation. Speech Communication, 33:23-60. ATLAS: A flexible and extensible architecture for linguistic annotation. Steven Bird, David Day, John Garofolo, John Henderson, Chris Laprun, Mark Liberman, Proceedings of the Second International Conference on Language Resources and Evaluation. the Second International Conference on Language Resources and EvaluationParisEuropean Language Resources AssociationSteven Bird, David Day, John Garofolo, John Henderson, Chris Laprun, and Mark Liberman. 2000. ATLAS: A flexible and extensible architecture for linguistic annotation. In Proceedings of the Second International Conference on Language Resources and Evaluation. Paris: European Language Resources Association. Multi-level annotation of speech: An overview of the emu speech database management system. Steve Cassidy, Jonathan Harrington, Speech Communication. 33Steve Cassidy and Jonathan Harrington. 2001. Multi-level annotation of speech: An overview of the emu speech database management system. Speech Communication, 33:61-77. Switchboard SWBD-DAMSL Labeling Project Coder's Manual, Draft 13. Daniel Jurafsky, Elizabeth Shriberg, Debra Biasca, 97-02University of Colorado Institute of Cognitive ScienceTechnical Reportstripe.colorado.edu/˜jurafsky/manual.august1.htmlDaniel Jurafsky, Elizabeth Shriberg, and Debra Biasca. 1997. Switchboard SWBD-DAMSL Labeling Project Coder's Manual, Draft 13. Technical Report 97-02, University of Colorado Institute of Cognitive Science. [stripe.colorado.edu/˜jurafsky/manual.august1.html]. Wavesurfer -an open source speech tool. Kåre Sjölander, Jonas Beskow, Proceedings of the 6th International Conference on Spoken Language Processing. the 6th International Conference on Spoken Language ProcessingKåre Sjölander and Jonas Beskow. 2000. Wavesurfer -an open source speech tool. In Proceedings of the 6th International Conference on Spoken Language Processing. http://www.speech.kth.se/wavesurfer/. The Snack sound toolkit. Kåre Sjölander, Kåre Sjölander. 2000. The Snack sound toolkit. http://www.speech.kth.se/snack/.

Classification of Lexical Collocation Errors in the Writings of Learners of Spanish

It is generally acknowledged that collocations in the sense of idiosyncratic word cooccurrences are a challenge in the context of second language learning. Advanced miscollocation correction is thus highly desirable. However, state-of-the-art "collocation checkers" are merely able to detect a possible miscollocation and then offer as correction suggestion a list of collocations of the given keyword retrieved automatically from a corpus. No more targeted correction is possible since state-ofthe-art collocation checkers are not able to identify the type of the miscollocation. We suggest a classification of the main types of lexical miscollocations by US American learners of Spanish and demonstrate its performance.

Classification of Lexical Collocation Errors in the Writings of Learners of Spanish Sep 7-9 2015 Sara Rodríguez-Fernández sara.rodriguez.fernandez@upf.edu ICREA and Universitat Pompeu Fabra Universitat Pompeu Fabra Universitat Pompeu Fabra Roberto Carlini roberto.carlini@upf.edu ICREA and Universitat Pompeu Fabra Universitat Pompeu Fabra Universitat Pompeu Fabra Leo Wanner leo.wanner@upf.edu ICREA and Universitat Pompeu Fabra Universitat Pompeu Fabra Universitat Pompeu Fabra Classification of Lexical Collocation Errors in the Writings of Learners of Spanish Proceedings of Recent Advances in Natural Language Processing Recent Advances in Natural Language ProcessingHissar, BulgariaSep 7-9 2015 It is generally acknowledged that collocations in the sense of idiosyncratic word cooccurrences are a challenge in the context of second language learning. Advanced miscollocation correction is thus highly desirable. However, state-of-the-art "collocation checkers" are merely able to detect a possible miscollocation and then offer as correction suggestion a list of collocations of the given keyword retrieved automatically from a corpus. No more targeted correction is possible since state-ofthe-art collocation checkers are not able to identify the type of the miscollocation. We suggest a classification of the main types of lexical miscollocations by US American learners of Spanish and demonstrate its performance. Introduction In the second language learning literature, it is generally acknowledged that it is in particular idiosyncratic word co-occurrences of the kind take [a] walk, make [a] proposal, pass [an] exam, weak performance, hard blow, etc. that make language learning a challenge (Granger, 1998;Lewis, 2000;Nesselhauf, 2004;Nesselhauf, 2005;Lesniewska, 2006;Alonso Ramos et al., 2010). Such cooccurrences (in lexicography known as "collocations") are language-specific. For instance, in Spanish, you 'give a walk ' (dar [un] paseo), while in French and German you 'make' it (faire [une] promenade / [einen] Spaziergang machen). In English you take a step, while in German you 'make' it ([einen] Schritt machen) and in Spanish you 'give' it (dar [un] paso). In English, you can hold or give [a] lecture, in Spanish you 'give' (dar [una] clase), but you do not 'hold' it, and in German you 'hold' it ([eine] Vorlesung halten), but do not 'give' it. And so on. Several proposals have been put forward for how to verify automatically whether a collocation as used by a language learner is correct or not and, in the case that it is not, display a list of potential collocations of the keyword (walk, step, and lecture above) of the assumingly incorrect collocation. For instance, a Spanish learner of English may use *approve [an] exam instead of pass [an] exam. When this miscollocation is entered, e.g., into the MUST collocation checker 1 for verification, the program suggests (in this order) pass exam, sit exam, take exam, fail exam, and do exam as possible corrections. That is, the checker offers all possible <verb> + exam collocations found in a reference corpus or dictionary. However, the display of a mere list of correct collocations of a given keyword is unsatisfactory for learners since they are left alone with the problem of picking the right one among several (potentially rather similar) choices. On the other hand, no further restriction of the list of correction candidates or any meaningful reordering is possible because the collocation checker has no knowledge about the type of the error of the miscollocation. In order to improve the state of affairs, and be able to propose a more targeted correction, we must be able to identify the type of error of the collocation proposed by the learner (and thus also the meaning the learner intended to express by the miscollocation). While this seems hardly feasible with isolated collocations submitted by a learner for verification (as above), error type recognition in the writings of learners is more promising. Such an error type recognition procedure is taken for granted in grammar checkers, but is still absolutely unexplored in collocation checkers. In what follows, we outline how some of the most prominent errors in collocations identified in the writings of US American students learning Spanish can be classified with respect to a given collocation error typology. Background on Collocations and Collocation Errors Given that the notion of collocation has been discussed and interpreted in lexicology from different angles, we first clarify our usage of the term. Then, we outline the miscollocation typology that underlies our classification. On the Nature of Collocations The term "collocation" as introduced by Firth (1957) and cast into a definition by Halliday (1961) encompasses the statistical distribution of lexical items in context: lexical items that form high probability associations are considered collocations. It is this interpretation that underlies most works on automatic identification of collocations in corpora; see, e.g., (Choueka, 1988;Church and Hanks, 1989;Pecina, 2008;Evert, 2007;Bouma, 2010). However, in contemporary lexicography and lexicology, an interpretation that stresses the idiosyncratic nature of collocations prevails. According to Hausmann (1984), Cowie (1994), Mel'čuk (1995) and others, a collocation is a binary idiosyncratic co-occurrence of lexical items between which a direct syntactic dependency holds and where the occurrence of one of the items (the base) is subject of the free choice of the speaker, while the occurrence of the other item (the collocate) is restricted by the base. Thus, in the case of take [a] walk, walk is the base and take the collocate, in the case of high speed, speed is the base and high the collocate, etc. It is this understanding of the term "collocation" that we find reflected in general public collocation dictionaries and that we follow in our work since it seems most useful in the context of second language acquisition. However, this is not to say that the two main interpretations of the term "collocation", the distributional and the idiosyncratic one, are disjoint, i.e., necessarily lead to a different judgement with respect to the collocation status of a word combination. On the contrary: two lexical items that form an idiosyncratic co-occurrence are likely to occur together in a corpus with a high value of Pointwise Mutual Information (P M I) (Church and Hanks, 1989): P M I = log P (a∩b) P (a)P (b) = log P (a|b) P (a) = log P (b|a) P (b) (1) The P M I indicates that if two variables a and b are independent, the probability of their intersection is the product of their probabilities. A P M I equal to 0 means that the variables are independent; a positive P M I implies a correlation beyond independence; and a negative PMI signals that the co-occurrence of the variables is lower than the average. Two lexemes are thus considered to form a collocation when they have a positive P M I, i.e., they are found together more often that this would happen if they would be independent variables. P M I has been a standard collocation measure throughout the literature since Church and Hank's proposal in 1989. However, a mere use of P M I or any similar measure neglects that the lexical dependencies between the base and the collocate are not symmetric (recall that P M I is commutative, i.e., P M I(a, b) = P M I(b, a)). Only a few studies take into consideration the asymmetry of collocations; see, e.g., Gries (2013) (2) Typology of Collocation Errors Alonso Ramos et al. (2010) proposed a detailed three-dimensional typology of collocation errors. The first dimension defines which element of the collocation (the base or the collocate) is erroneous or whether it is the collocation as a whole. The second (descriptive) dimension details the type of error that was produced. Three different global types are distinguished: register, lexical, and grammatical. The third dimension, finally, details the possible interpretation of the origin of the error (e.g., calque from the native language of the learner, analogy to another common collocation, etc.). In the experiments presented in this paper, we focus on the lexical branch of the descriptive dimension. Lexical errors are divided into five different types; the first two affect either the base or the collocate; the other three the collocation as a whole: 2 1. Substitution errors: Errors resulting from an inappropriate choice of a lexical unit that exists in the language as either base or collocate. This is the case, e.g., with *realizar una meta 'to reach a goal', lit. 'to make, to carry out a goal', where both the base and the collocate are existing lexical units in Spanish, but the correct collocate alcanzar, lit. 'to achieve' has been substituted by realizar. 2. Creation errors: Errors resulting from the use of a non-existing (i.e., "created" or invented) lexical unit as the base or as the collocate. An example of this type of error is *estallar confrontamientos, instead of estallar confrontaciones, lit. '(make) explode a confrontation', where the learner has used the non-existing form confrontamientos. Synthesis errors: Errors resulting from the use of a non-existing lexical unit instead of a collocation, as, for instance, *escaparatear, instead of ir de escaparates 'to go windowshopping'. 4. Analysis errors: Errors that are inverse to synthesis errors, i.e., that result from the use of an invented collocation instead of a single lexical unit expression. An example of this type of error is *sitio de acampar 'camping site', which in Spanish would be better expressed by the lexical unit camping. 5. Different sense errors: Errors resulting from the use of a correct collocation, but with meaning different from the intended one. An example of this type of error is *el próximo día, instead of el día siguiente 'the next day'. Our studies show that 'Substitution', 'Creation' and 'Different sense' errors are the most common types of miscollocations. In contrast, learners tend to make rather few 'Synthesis' and 'Analysis' errors. Therefore, given that 'Synthesis' errors are not comparable to any other error class, we decided not to consider them at this stage of our work. 'Analysis' errors show in their appearance a high similarity to 'Substitution' errors, such that they could be merged with them without any major ized judgement whether a given collocation is a miscollocation or a correct collocation has in all cases been made by a team of lexicographers who are native speakers of Peninsular Spanish. distortion of the typology. Therefore, we deal below with miscollocation classification with respect to three lexical error classes: 1. 'Extended Substitution', 2. 'Creation', and 3. 'Different Sense'. Towards Automatic Collocation Error Classification In corpus-based linguistic phenomenon classification, it is common to choose a supervised machine learning method that is then used to assign any identified phenomenon to one of the available classes. In the light of the diversity of the linguistic nature of the collocation errors and the widely diverging frequency of the different error types, this procedure seems not optimal for miscollocation classification. A round of preliminary experiments confirmed this assessment. It is more promising to target the identification of each collocation error type separately, using for each of them the identification method that suits its characteristics best. Furthermore and as a matter of fact, it cannot be excluded that a miscollocation may contain more than one type of error. Thus, it may contain an error in the base and another error in the collocate, or it might have a lexical and a grammatical error or two lexical errors (one per element) at the same time. An example of a collocation containing two lexical errors is afecto malo 'bad effect', where both the base and the collocate are incorrect. Afecto 'affect' is chosen instead of efecto 'effect', and malo 'bad' instead of nocivo 'damaging'. In what follows, we describe the methods that we use to identify miscollocations of the three types that we target. All of these methods perform a binary classification of all identified incorrect collocations as 'of type X' / 'not of type X'. The methods for the identification of 'Extended substitution' and 'Creation' errors receive as input the incorrect collocations (i.e., grammatical, lexical or register-oriented miscollocations) recognized in the writing of a language learner by a collocation error recognition program 3 , together with their sentential contexts. The method for the recognition of 'Different sense' errors receives as input 'different sense' errors along with the correct collocations identified in the writing of the learner. Extended Substitution Error Classification. For the classification of incorrect collocations as 'extended substitution error' / 'not an extended substitution error', we use supervised machine learning. This is because 'extended substitution' is, on the one side, the most common type of error (such that sufficient training material is available), and, on the other side, very variant (such that it is difficult to be captured by a rule-based procedure). After testing various ML-approaches, we have chosen the Support Vector Machine (SMO) implementation from the Weka toolkit (Hall et al., 2009). 4 Two different types of features have been used: lexical features and co-occurrence (or P M Ibased) features. The lexical features consist of the lemma of the collocate and the bigram made up of the lemmas of the base and collocate. The P M Ibased features consist of: N P M I C of the base and the collocate, N P M I C of the hypernym of the base and the collocate, N P M I of the base and its context, and N P M I of the collocate and its context, considering as context the two immediate words to the left and to the right of each element. Hypernyms were taken from the Spanish Word-Net; N P M Is and N P M I C s were calculated on a 7 million sentences reference corpus of Spanish. Creation Error Classification. For the detection of creation errors among all miscollocations, we have designed a rule-based algorithm that uses linguistic (lexical and morphological) information; see Algorithm 1. If both elements of a collocation under examination are found in the reference corpus (RC) with a sufficient frequency (≥50 for our experiments), they are considered valid tokens of Spanish, and therefore 'Not creation' errors. If one of the elements has a low frequency in the RC (<50), the algorithm continues to examine the miscollocation. First, it checks whether a learner used an English word in a Spanish sentence, considering it as a 'transfer Creation error'. If this is not the case, it checks whether the gender suffix is wrong, considering it as a 'gender Creation error', as in, e.g., *hacer regala instead of hacer regalo, lit. 'make present'. This is done by alternating the gender suffix and checking the resulting token in the RC. Algorithm 1: Creation Error Classification Given a collocation 'b + c' that is to be verified do if bL,cL ∈ RC // with 'bL'/'cL' as lemmatized base/collocate and freq('bL') > 50 and freq('cL') > 50 then echo "Not a creation error" else if bL ∨ cL ∈ English dictionary then echo "Creation error (Transfer)" else if check gender(bL) = false then echo "Creation error (Incorrect gender)" else if check affix(br) || check affix(cr) // with 'br'/'cr' as stems of base/collocate then echo: "Creation error (Incorrect derivation)" else if check ortography(bL) || check ortography(cL) then echo "Not a creation error (Ortographic)" else if freq('bL') > 0 or freq('cL') > 0 then echo "Not a creation error" else echo "Creation error (Unidentified)" If no gender-influenced error could be detected, the algorithm checks whether the error is due to an incorrect morphological derivation of either the base or the collocate -which would imply a 'derivation Creation error', as in, e.g. *ataque terrorístico instead of ataque terrorista 'terrorist attack'. For this purpose, the stems of the collocation elements are obtained and expanded by the common nominal / verbal derivation affixes of Spanish to see whether any derivation leads to the form used by the learner. Should this not be the case, the final check is to see whether any of the elements is misspelled and therefore we face a 'Not creation error'. This is done by calculating the edit distance from the given forms to valid tokens in the RC. In the case of an unsuccessful orthography check, we assume a 'Creation' error if the frequency of one of the elements of the miscollocation is '0', and a 'Not creation' error for element frequencies between '0' and '50'. Different Sense Error Classification. Given that 'Different Sense Errors' capture the use of correct collocations in an inappropriate context, the main strategy for their detection is to compare the context of a learner collocation with its prototypical context. The prototypical context is represented by a centroid vector calculated using the lexical contexts of the correct uses of the collocation found in the RC. The vector representing the original context is compared to the centroid vector in terms of cosine similarity; cf. Eq. (3). sim(A, B) = A · B A B(3) A specific similarity threshold must be determined in order to discriminate correct and incorrect uses. In the experiments we carried out so far, 0.02543 was empirically determined as the best fitting threshold. However, further research is needed to design a more generic threshold determination procedure. Experiments In this section, we first describe the experiment set up and present then the results of the experiments. Experiment Setup For our experiments, we use a fragment of the Spanish Learner Corpus CEDEL2 (Lozano, 2009), which is composed of writings of learners of Spanish whose first language is American English. The writings have an average length of 500 words and cover different genres. Opinion essays, descriptive texts, accounts of some past experience, and letters are the most common of them. The levels of the students range from 'lowintermediate' to 'advanced'. In the fragment of CEDEL2 (in total, 517 writings) that we use (our working corpus), both the correct and incorrect collocation occurrences are tagged. 5 As stated above, collocations were annotated and revised, and only those for which a general agreement regarding their status was found, were used for the experiments. Table 1 shows the frequency of the correct collocations and of the five types of lexical miscollocations in our working corpus. The numbers confirm our decision to discard synthesis miscollocations (there are only 9 of them -compared to, e.g., 565 substitution miscollocations) and to merge analysis miscollocations (19 in our corpus) with substitution miscollocations. 6 To be able to take the syntactic structure of collocations into account, we processed 5 The tagging procedure has been carried out manually by several linguists. The first phase of it was already carried out by (Alonso Ramos et al., 2011). We carried on the tagging work by Alonso Ramos et al. to As a reference corpus, we used a seven million sentence corpus, from Peninsular Spanish newspaper material. The reference corpus was also processed with Bohnet (2010)'s syntactic dependency parser. Table 2 shows the performance of the individual collocation error classification methods. In the '+' column of each error type, the accuracy is displayed with which our algorithms correctly detect that a miscollocation belongs to the error type in question; in the '−' column, the accuracy is displayed with which our algorithms correctly detect that a miscollocation does not belong to the corresponding error type. Table 2: Error detection performance. The lower row displays the achieved accuracy. Results of the Experiments To assess the performance of our classification, we use three baselines, one for each type of error. To the best of our knowledge, no other state-ofthe-art figures are available with which we could compare its quality further. For the 'Extended substitution' miscollocation classification, we use as baseline a simplified version of the model, trained only with one of our lexical features, namely bigrams made up of the lemmas of the base and the collocate of the collocation. For 'Creation' miscollocation classification, the baseline is an algorithm that judges a miscollocation to be of the type 'Creation' if either one of the elements (the lemma of the base or of the collocate) or both elements of the miscollocation are not found in the reference corpus. Finally, for the 'Different sense' miscollocation classification, we take as baseline an algorithm that, given a bag of the lexical items that constitute the contexts of the correct uses of a collocation in the RC, judges a collocation to be a miscollocation of the 'Different sense' type, if less than half of the lexical items of the context of this collocation in the writing of the learner is not found in the reference bag. Discussion Before we discuss the outcome of the experiments, let us briefly make some generic remarks on the phenomenon of a collocation in the experiments. The Phenomenon of a Collocation The decision whether a collocation is correct or incorrect is not always straightforward, even for native expert annotators. Firstly, a certain number of collocations was affected by spelling and inflective errors. Consider, e.g., tomamos cervesas 'we drank beer', instead of cervezas; sacque una mala nota 'I got a bad mark', where saqué is the right form, or el dolor disminúe 'the pain decreases', instead of disminuye. In such cases, we assume that these are orthographical or morphological mistakes, rather than collocational ones. Therefore, we consider them to be correct. On the other hand, collocations may also differ in their degree of acceptability. Consider, e.g., asistir a la escuela, tomar una fotografía o mirar la televisión. Collocations that were doubtful to one or several annotators were looked up in th RC. If their frequency was higher than a certain threshold, they were annotated as correct. Otherwise, they were considered incorrect. From the above examples, asistir a la escuela was the only collocation considered as correct after the consultation of the RC. The Outcome of the Experiments The performance figures show that the correct identification of 'Different sense' miscollocations is still a challenge. With an accuracy somewhat below 60% for both the recognition of 'Different sense' miscollocations and recognition of 'Cor-rectly used' collocations, there is room for improvement. Our cosine-measure quite often leads to the classification of correct collocations as 'Different sense' miscollocations (cf., e.g., ir en coche 'go by car' , tener una relación 'have a relationship', tener impacto 'have impact', tener capacidad 'have capacity') or classifies 'Different sense' errors as correctly used collocations, such as gastar el tiempo (intended pasar el tiempo 'spend time' or tener opciones instead of ofrecer posibilidades 'offer possibilities'. This shows the limitations of an exclusive use of lexical contexts for the judgement whether a collocation is appropriately used: on the one hand, lexical contexts can, in fact, be rather variant (such that the learner may use a collocation correctly in a novel context), and, on the other hand, lexical contexts do not capture the situational contexts, which determine even to a major extent the appropriateness of the use of a given expression. Unfortunately, to capture situational contexts remains a big challenge. Conclusions and Future Work We discussed a classification of collocation errors made by American English learners of Spanish with respect to the lexical branch of the miscollocation typology presented in Alonso Ramos et al. (2010). The results are very good for two of the three error types we considered, 'Substitution' and 'Creation'. The third type of miscollocation, 'Different sense', is recognized to a certain extent, but further research is needed to be able to recognize it as well as the other two error types. But already with the provided classification at hand, learners can be offered much more targeted correction aids than this is the case with the state-of-the-art collocation checkers. We are now about to implement such aids, which will also offer the classification and targeted correction of grammatical collocation errors (Rodríguez-Fernández et al., 2015), into the collocation learning workbench HARenES (Wanner et al., 2013;Alonso Ramos et al., 2015). , who proposes an asymmetric association measure, ∆P, and Carlini et al. (2014), who propose an assymmetric normalization of P M I; see Eq. (2). In our work, we use Carlini et al. (2014)'s asymmetric N P M I C . N P M I C = P M I(collocate,base) −log(p(collocate)) http://miscollocation-richtrf. rhcloud.com/ Given that we work on a Spanish learner corpus, the examples of miscollocations are in Spanish. The consensual- Since in our experiments we focus on miscollocation classification, we use as "writings of language learners" a learner corpus in which both correct and incorrect collocations have been annotated manually and revised by different annotators. Only those instances for which complete agreement was found were used for the experiments. Weka is University of Waikato's public machine learning platform that offers a great variety of different classification algorithms for data mining. Processing tools' performance on non-native texts is lower than on texts written by natives. We evaluated the performance of the parser on our learner corpus and obtained the following results: LAS:88.50%, UAS:87.67%, LA:84.54%. 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Large-Scale Acquisition of LCS-Based Lexicons for Foreign Language Tutoring

We focus on the probleln of building large repositories of le.rical coJtceplual structure (LCS) representations for verbs in multiple languages. One of the main results of this work is the definition of a relat, ion between broad semantic classes and LCS meaniug components. Our acquisition program--LEXICALL--takes, as input, the result of previous work on verb classification and thematic grid tagging, and outputs LCS representations for different. languages. These representations have been ported into English, Arabic and Spanish lexicons, each containing approximately 9000 verbs. We are currently using these lexicons in an operational foreign language tutoring and machine translation.

Large-Scale Acquisition of LCS-Based Lexicons for Foreign Language Tutoring Bonnie J Dorr Department of Computer Science University of Maryland College Park 20742MDUSA Large-Scale Acquisition of LCS-Based Lexicons for Foreign Language Tutoring We focus on the probleln of building large repositories of le.rical coJtceplual structure (LCS) representations for verbs in multiple languages. One of the main results of this work is the definition of a relat, ion between broad semantic classes and LCS meaniug components. Our acquisition program--LEXICALL--takes, as input, the result of previous work on verb classification and thematic grid tagging, and outputs LCS representations for different. languages. These representations have been ported into English, Arabic and Spanish lexicons, each containing approximately 9000 verbs. We are currently using these lexicons in an operational foreign language tutoring and machine translation. Introduction A wide range of new capabilities in NLP applications such as foreign language tutoring (FLT) has been made possible by recent advances in lexica.1 semantics (Carrier and Randall, 1993;Dowty, 1991;Fillmore, 1968;Foley and Van Valin, 1984;Grimshaw, 1990;Gruber, 1965;Hale and Keyser, 1993;Jackendoff, 1983;aackendoff, 1990aackendoff, : Jackendoff, 1996Levin, 1993;Levin and Rappaport Hovav, To appear;Pesetsky, 1982;Pinker, 1989). Many of these researchers adopt the hypothesis that verbs can be grouped into broad classes, each of which corresponds to some combination of basic meaning con> ponents. This is the basic premise underlying our approach to multilingual lexicon construction. In particular, we have organized verbs into broad selnantic classes and subsequently designed a set of le,ical conceptual structures (LC, S), for each class. These representations have been ported into English, Arabic, and Spanish lexicons, each containing approximately 9000 verbs. An example of a NLP application for which these lexicons are currently in use is an operational foreign language tutoring (FLT) system called Military Language Tutor (MILT). This system provides a wide range of lessons for use in language training. One of the tutoring lessons, the MicroWorld Lesson (see Figure 1) requires the capability of the languagelearner to state domain-specific actions in a variety of different ways. For example, the language-learner might connnand the agent (pictured at the left in the graphical interface) to take the following action: Walt" to the table and pick up the document. The same action should be taken if the user says: Go to the table and remove document, Retrieve the document from the table, etc. The LCS representation provides the capability to execute various forms of the same command without hardcoding them as part, of the graphical interface. In another tutoring lesson, Question-Answering, the student is asked to answer questions about a foreign language text that they have read. Their answer is converted into an LCS which is matched against a prestored LCS corresponding to an answer typed in by a human instructor (henceforth, called the "author"). The prestored LCS is an idealized form of the answer to a question, which can take one of many forms. Suppose, for example, the question posed to the user is: Where did Jack put the book'? (or Addnde paso Jack el libro? in Spanish). The author's answer, e.g., Jack put the book in the trash, has been stored as an LCS by the tutoring system. If the student types Jack threw the book in the trash, or Jack moved the book from the table into the trash, the system is able to nautch against the prestored LCS and determine that all three of these responses are semantically appropriate. We have developed an acquisition program--LEXICALL--that allows us to construct LCS-based lexicons for the FLT system. This program is designed to be used for multiple languages, and also for other NLP applications (e.g., machine translation). One of the main results of this work is the definition of a relation between broad semantic classes (based on work by Levin (1993)) and LCS meaning components. We build on previous work, where verbs were classified automatically (Doff and .Jones, 1996: Dorr, To appear) and tagged with thematic grid information (Dorr, Garman, and Weinberg, 1995). We use these pre-assigned classes and thematic grids as input to LEXICALL. The output is a set of LCS's corresponding to individual verb entries in our lexicon. Previous research in automatic acquisition focuses primarily on the use of statistical techniques, such as bilingual alignment (Church and Hanks, 1990;Klavans and Tzoukermann, 1995;Wu and Xia, 1995) or extraction of syntactic constructions from online dictionaries and corpora (Brent, 1993). Others have taken a more knowledge-based (interlingual) approach (Lonsdale, Mitamura, and Nyberg, 1995). Still others (Copestake et al.. 1995), use Englishbased grammatical codes for acquisition of lexical representations. Our approach differs from these in that it exploits certain linguistic constraints that govern the relation between a word's surface behavior and its corresponding semantic class. We delnonstrate that-by assigning a LCS representatioll to each semantic class--we can produce verb entries on a broad scale; these, in turn, are ported into multiple languages. We first show how the LCS is used in a FLT system. We then present an overview of the LCS acquisition process. Finally, we describe how LEXICALL constructs entries for specific lexical items. Application of the LCS Representation to FLT One of the types of knowledge that must be captured in FLT is linguistic knowledge at the level of the lexicon, which covers a wide range of information types such as verbal subcategorization for events (e.g., that a transitive verb such as hit occurs with an object noun phrase), featural information (e.g., that the direct object of a verb such as frighlen is animate), thematic information (e.g., that Mary is the agent in Mary hie the ball), and lexical-semantic information (e.g., spatial verbs such as throw are conceptually distinct fi'om verbs of possession such as give). By modularizing the lexicon, we treat each information type separately, thus allowing us to vary the degree of dependence on each level so that we can address the question of how much knowledge is necessary for the success of the particular NLP application. This section describes the use of the LCS representation in a question-answering component of the MILT system (Sains, 1993;Weinberg et al., 1995). As described above, the LCS representation is used as the basis of matching routines for assessing students' answers to free response questions about a short foreign language passage. In order to inform the student whether a question has been answered Jack threw the book in the trash exact match "That's right" Jack put the book in the trash Jack threw the book in the trash missing MANNER "How?" .Jack threw the book in the trash Jack put the book in the trash extra MANNER "You're assuming things" .Jack is friendly Jack put the book in the trash mismatch primitive "Please reread" Jack threw the book Jack put the book in the trash missing argument "Where?" correctly, the author of the lesson must provide the desired response in advance. The system parses and semantically analyzes the author's response into a corresponding LCS representation which is then prestored in a database of possible responses. Once the question answering lesson is activated, each of the student's responses is parsed and semantically analyzed into a LCS representation which is checked for a match against the corresponding prestored LCS representation. The student is then informed as to whether the question has been answered correctly depending on how closely the student's response LCS matches the author's prestored LCS. Consider what happens in a lesson if the author has specified that a correct answer to the question Addnde paso Jack el libro? in Spanish is Jack fir6 el libro a la basura ('Jack threw out the book into the trash'). This answer is processed by the system to produce the following LCS: ( The LCS is stored by the tutor and then later matched against the student's answer. If the student types Jack movio ' el libro de la mesa a la basura ('Jack moved the book froln the table to the trash'), the system must determine if these two match. The student's sentence is processed and the following LCS structure is produced: (2 The matcher compares these two, and produces the following output: Missing: MANNER THROWINGLY Extra: FROM LOC The system identifies the student's response as a match with the prestored answer, but it also recognizes that there is one piece of missing information and one piece of extra information. The "Missing" and "Extra" output is internal to the NLP component of the Tutor, i.e., this is not the final response displayed to the student. The system must convert, this information into meaningful feedback so that the student knows how to repair the answer that was originally given. For example, the instructor can program the tutor to notify the student about the omitted information in the form of a 'How' question, or it can choose to ignore it. The extra information is generally ignored, although it is recorded in case the instructor decides to program the system to notify the student about this as well. The full range of feedback is not presented here. Some possibilities are summarized (in English) in Table 1 (adapted from (Holland, 1994)). Note that. the main advantage of using the LCS is that it allows the author to type in an answer that is general enough to match any number of additional answers. Overview of LCS Acquisition We use Levin's publicly available online index (Levin, 1993) as a starting point for building LCSbased verb entries. 1 While this index provides a unique and extensive catalog of verb classes, it does not define the underlying meaning components of each class. One of the main contributions of our work is that it provides a relation between Levin's classes and meaning components as defined in the LCS representation. Table 2 shows three broad semantic categories and example verbs along with their associated LCS representations. We have band-constructed a database containing 191 LCS templates, i.e., one for each verb class in (Levin, 1993). In addition, we have genera.ted LCS templates for 26 additional classes that are not included in Levin's system. Several of these correspond to verbs that take sentential complements (e.g., coerce). Ko]I [CAUSE (X, [BELo¢ (Y, [ATLo¢ (Y, Z)])], [BY (MANNER)])] [BELo¢ (Y,[ATLo~ (Y, Z)], [BY (MANNER}])] [GOLo~ (Y, [(DIRECTION)Lo¢ (Y, [ATLo¢ (Y, Z)])])] [GOLo¢ (Y, [BY (MANNER)])] [CAUSE (X, [GOIdent (Y, [TOWARDId~t (Y, [ATId¢n~ (Y, [(STATE)Id~nt ([(WITH>po~ (*HEAD*, Z)])])])])])] [CAUSE (X, [GOLo¢ (Y)], [BY (MANNER)])] A full entry in the dal:abase includes a semantic class number with a list of possible verbs, a thematic grid, and a LCS template: (3) Class 47.8: adjoin, intersect., meet, touch .... Thematic Grid: _th_loc LCS Template: (be loc (thing 2) (at loc (thing 2) (thing 11)) ( ! ! -ingly 26) ) The semantic class label 47.8 above is taken from Levin's 1993 book (Verbs of Contiguous Location), i.e., the class to which the verb touch has been assigned. 2 A verb, together with its semantic class uniquely identifies the word sense, or LCS template, to which the verb refers. The thematic grid (_th_loc) indicates that the verb has two obligatory arguments, a theme and a location. 3 The !! in the LCS Template acts as a wildcard; it will be filled by a lexeme (i.e., a root form of the verb). The resulting form is called a constant, i.e., the idiosyncratic part of the meaning that distinguishes among members of a verb class (in the spirit of (Grimshaw, 1993;Levin and Rappaport Hovav, To appear;Pinker, 1989;Talmy, 1985)). 4 Three inputs are required for acquisition of verb entries: a semantic class, a thematic grid, and a lexeme, which we will henceforth abbreviate as "class/grid/lexeme." The output is a Lisp-like expression corresponding to the LCS representation. An example of input/output for our acquisition procedure is shown here: (4) Acquisition of LCS for: touch Input: 47.8: _th_loc; "touch" 2Verbs not occurring in Levin's book are also assigned to classes using techniques described in {Dorr and Jones, 1996; Dorr, To appear). ZAn underscore (_) designates an obligatory role and a comma (,) designates an optional role. 4The ! ! in the Lisp representation corresponds to the angle-bracketed constants ill Table 2, e.g., ! !-ingly corresponds to (MANNER}. Output: (be loc (* thing 2) (at loc (thing 2) (* thing 11)) (touchingly 26) ) Language-specific annotations such as the .-,uarker in the LCS Output are added to the templates by processing the components of thematic grid specifications, as we will see in more detail next. 4 Language-Specific Annotations In our on-going example (4), the thematic grid _th loc indicates that the theme and the location are both obligatory (in English) and should be annotated as such in the instantiated LCS. This is achieved by inserting a *-marker appropriately. Consider the structural divergence between the following English/Spanish equivalents: (5) Structural Divergence: E: John entered the house. S: John entr6 a la casa. 'John entered into the house.' The English sentence differs structurally from the Spanish in that the noun phrase the house corresponds to a prepositional phrase a la casa. This distinction is characterized by different positionings of the *-marker in the lexical entries produced by LEXICALL: (6) Lexical Entries: enter: (go loc (* thing 2) (toward loc (thing 2) (in loc (thing 2) (* thing 6))) (enteringly 26) ) entrar: (go loc (* thing 2) ((* toward 5) loc (thing 2) (in loc (thing 2) (thing 6))) (enteringly 26) ) The lexicon entries for enter and entrar both mean "X (= Thing 2) goes into location Y (= Thing 6)." Variable positions (designated by numbers, such as 2, 5 and 6) are used in place of the ultimate fillers such as john and house. The structural divergence of (,5) is a.ccomnaodated as follows: the *-marked leaf node, i.e., (thing 6) in the enter definition, is filled directly, whereas the .-marked non-leaf node, i.e., ((toward 5) loc ...) in the en¢rar definition, is filled in through unification at the internal toward node. 5 Construction of Lexical Entries C.onsider the construction of a lexical entry for the verb adorn. The LC, S for this verb is in the class of Fill Verbs (9.8): s (7) (cause (thing 1) (go ident (thing 2) (toward ident (thing 2) (at ident (thing 2) (!!-ed 9)))) (with poss (*head*) (thing 16))) This list structure recursively associates logical heads with their arguments and modifiers. The logical head is represented as a primitive/field Colnbination, e.g., GOIdent is represented as (go ident ...). The arguments for CAUSE are (thing 1) and (go ident ...). The substructure GO itself has two arguments (thing 2) and (toward ident ...) and a modifier (with poss ...).6 The ! !-ed constant refers to a resulting state, e.g., adorned for the verb adorn. The LC.S produced by our program for this verb is: (8) (cause (thing 1) (go ident (thing 2) (toward ident (thing 2) (at ident (thing 2) (adorned 9)))) (with poss (*head*) (thing 16))) The variables in the representation map between LCS positions and their corresponding thematic roles. In the LCS framework, thematic roles provide semantic information about properties of the argument and modifier structures. In (7) and (8) above, the numbers 1, 2, 9, and 16 correspond to the roles agent (ag), theme (th), predicate (pred), and possessional modifier (mod-poss), respectively. These numbers enter into the construction of LCS entries: they correspond to argument positions in the LCS template (extracted using the class/grid/lexeme specification), hfformatiou is filled into the LCS template using these numbers, coupled with the thematic grid tag for the particular word being defined. Pundmnentals LEXICALL locates the appropriate template in the LCS database using the class/grid pairing as an in-5Some of the other 9.8 verbs are: anoint, bandage. flood, frame, garland, stud, s~@~se, surround, veil. 6The *head* symbol--used for modifiers--is a placeholder that points to the root (cause) of the overall lex-icaJ entry. dex, and then determines the language-specifc annotations to instantiate for that template. The default position of the .-marker is the left-most occurrence of the LCS node corresponding to a particula.r thematic role. However, if a preposition occurs in the grid, the .-marker may be placed differently. In such a. case, a. primitive representation (e.g., (to loc (at loc))) is extracted from a set of predefined mappings. If this representation corresponds to a subcomponent of the LCS template, the program recognizes this as a match against the grid, and the .-marker is placed in the template at the level where this match occurs (as in the entry for entrar given in (6) above). If a preposition occurs in the grid but there is no matching primitive representation, the preposition is considered to be a. collocation, and it is placed in a special slot--:collocations--which indicates that the LCS already covers the semantics of the verb and the preposition is an idiosyncratic variation (as in learn about, know of, etc.). If a preposition is required but it is not specified (i.e., empty parentheses 0), then the .-marker is positioned at the level dominating the node that corresponds to that role--which indicates that several different prepositions might apply (as in put on, put under, put through, etc.). Examples The input to LEXICALL is a class/grid/lexeme specification, where each piece of information is separated by a hash sign (#): <class>#<grid>#<lexeme># <other semantic information> For example, the input specification for the verb replant (a word not classified by Levin) is: 9.7#_ag_th,mod-poss(with)#replant# !!-ed = planted (manner = again) This input indicates that the class assigned to replant is 9.7 (Levin's Spray/Load verbs) and its grid has a.n obligatory agent (ag), theme (tit), and all optional possessional modifer with preposition with (mod-poss (with) ). The information following the final # is optional; this information was previously hand-added to the assigned thematic grids. In the current example, the !!-ed designates the form of the constant planted which, in this case, is a morphological variant of the lexeme replant, r Also, the rThe constant takes one of several forms, including: ! !-ingly for a manner, ! !-er for an instrument, and !!-ed for resulting states. If this information has not been hand-added to the class/grid/lexeme specification (as is the case with most of the verbs), a default morphological process produces the appropriate form from tile lexeme. manner again is specified as an additional semantic coin ponent. For presentational purposes, the remainder of this section uses English examples. However, as we saw in Section 4, the representations used here carry over to other languages a.s well. In fact, we have used the same acquisition program, without modification, for building our Spanish and Arabic LCS-based lexicons, each of size comparable to our English LCSbased lexicon. I. Thematic Roles without Prepositions (9) Example: The flower decorated the room. Input: 9.8#_mod-poss_th#decorate# Template: (be ident (thing 2) (at ident (thing 2) (!!-ed 9)) (with poss (*head*) (thing 16))) Two thematic roles, th and mod-poss, are specified for the above sense of the English verb decorate. The thematic code numbers--2 and 16, respectively--are .-marked and the constant decorated replaces the wildcard: (10) Output: (be ident (* thing 2) (at ident (thing 2) (decorated 9)) (with poss (*head*) (* thing 16))) II. Thematic Roles with Unspecified Prepositions (11) Example: We parked the car near the store. We parked the car in the garage. Input: 9. l#_ag_th_goal ( ) #park# Template: (cause (thing 1) (go loc (thing 2) (toward loc (thing 2) ([at] loc (thing 2) (thing 6)))) ( ! ! -ingly 26) ) The input for this example indicates that the goal is headed by an unspecifed preposition. The thematic roles ag, th, and goal() correspond to code numbers 1, 2, and 6, respectively. The variable positions for ag and th are .-marked just as in the previous case, whereas goal() requires a different treatment. When a required preposition is left. unspecified, the .-marker is associated with a LCS node dominating a generic [at] position: (12) Output: (cause (* thing 1) (go loc (* thing 2) ((* toward S) loc (thing 2) ([at] loc (thing 2) (thing 6)))) (parkingly 26) ) } III. Thematic roles with Specified Prepositions (13) Example: We decorated the room with flowers. Input: 9.8#_ag_th ,mod-poss (with) #decorate# Template: (cause (thing 1) (go ident (thing 2) (toward ident (thing 2) (at ident (thing 2) (!!-ed 9)))) (with poss (*head*) (thing 16))) Here, the mod-poss role requires the preposition 'w~th in the modifier position: (14) Output: (cause (* thing 1) (go ident (* thing 2) (toward ident (thing 2) (at ident (thing 2) (decorated 9)))) ((* with 15) poss (*head*) (thing 16))) In order to determine the position of the .-marker for a thematic role with a required preposition, LEXICALL consults a set of predefined mappings between prepositions (or postpositions, in a language like Korean) and their corresponding primitive representations, s In the current case, the preposition with is mapped to the following primitive representation: (with poss). Since this matches a sub-component of the LCS template, the program recognizes this as a match against the grid, and the .-marker is placed in the template at the level of with. 6 Limitations and Conclusions We have described techniques for automatic construction of dictionaries for use in large-scale FLT. The dictionaries are based on a languageindependent representation called lexical conceptual structure (LCS). Significant enhancements to LCSbased tutoring could be achieved by combining this representation with a mechanism for handling issues related to discourse and pragmatics. For example, Mthough the LCS processor is capable of determining that the phrase in the trash partially matches the answer to Where did John put the book?, a pragmatic component would be required to determine that this answer is (perhaps) more appropriate than the full answer, He put the book in the trash. Representing conversational context and dynamic context updating (Traum et al., 1996;Haller, 1996;DiEugenio and Webber, 1996) would provide a fl'amework for this type of response "relaxation." Along SWe have defined approximately 100 such mappings per language. For example, the mapping produces the following primitive representations for the English word to: (to loc (at loc)), (to poss (at poss)), (to temp (at temp)), (toward loc (at loc)), (toward poss (at poss)). We have similar mappings defined in Arabic and Spanish. For example, the following primitive representations are produced for the Spanish word a: (at loc), (to loc (at loc)), (to poss (at poss)), (toward loc (at lot)). these same lines, a pragmatic component could provide a mechanism for det, ermining that certain fully matched responses (e.g., John hurled the book inlo the trash) are not. as "realistic sounding" as partially matched alternatives. Initially, LEXICALL was designed to support the development of LCS's for English only; however, the same techniques can be used for nmltilingual acquisition. As the lexicon coverage for other languages expands, it, is expected that our acquisition techniques will help further in the cross-linguistic investigation of the relationship between Levin's verb classes and the basic meaning components in the LCS represent, ation. In addition, it is expected that verbs in the same Levin class may have finer distinctions than what we have specified in the current LCS templates. We view the importation of LCS's from the English LCS database into Arabic and Spanish as a first, approxin~ation to the development of complete lexicons for these languages. The results have been hand-checked by native speakers using the class/grid/lexeme format (which is much easier to check than the flfily expanded LCS's). The lexical verification process took only two weeks by the native speakers. We estimate that, it would take at least 6 months to build such a lexicon from scratch (by human recall and data. entry alone), and in such a case, the potential for error would be a.t least twice as high. One important benefit of using the Levin classification as the basis of our program is that, once the mapping between verb classes and LCS representations has been established, we can acquire the LCS representation for a new verb (i.e., one not in Levin) simply by associating it. with one of the 191 classes. We see our approach as a first step toward compression of lexical entries in that it allows lexicons to be stored in terms of the more condensed class/grid/lexeme specifications; these can expanded online, as needed, during sentence processing in the NLP application. We conclude that, while human intervention is necessary for the acquisition of class/grid information, this intervention is virtually eliminated fi'om the LCS construction process because of our provision of a lnapping between semantic classes and primitive meaning components. Figure 1 : 1MicroWorld 1We focus on building entries for verbs; however, we have approximately 30,000 non-verb entries per language. Table 1 : 1Correspondence Between NLP Output and Tutor FeedbackSystem Prompt: Where did Jack put the book? Student Answer Prestored Answer Matcher Output Feedback Jack threw the book in the trash Table 2 : 2Sample Templates Stored in the LCS DatabaseClass Grid 9.2 ,ag_th,loc() 47.8 thloc 51.2 _th,src 51.3.1 th,src() ,goal() 9.8 _ag th ,raod-poss (with) 9.5 , agth gent Computer-Aided Training and Virtual Envi-ronmeT~t Technology, NASA: Houston, TX. Tahny, Leonard. 1985. Lexicalization Patterns: Semantic Structure in Lexical Forms. In T. 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Federated Chinese Word Segmentation with Global Character Associations

Chinese word segmentation (CWS) is a fundamental task for Chinese information processing, which always suffers from out-ofvocabulary word issues, especially when it is tested on data from different sources. Although one possible solution is to use more training data, in real applications, these data are stored at different locations and thus are invisible and isolated among each other owing to the privacy or legal issues (e.g., clinical reports from different hospitals). To address this issue and benefit from extra data, we propose a neural model for CWS with federated learning (FL) adopted to help CWS deal with data isolation, where a mechanism of global character associations is proposed to enhance FL to learn from different data sources. Experimental results on a simulated environment with five nodes confirm the effectiveness of our approach, where our approach outperforms different baselines including some well-designed FL frameworks. 1

Federated Chinese Word Segmentation with Global Character Associations August 1-6, 2021 Yuanhe Tian ♥yhtian@uw.edu♦chenguimin@foxmail.com University of Washington ♦ QTrade The Chinese University of Hong Kong (Shenzhen) ♥ Shenzhen Research Institute of Big Data Guimin Chen University of Washington ♦ QTrade The Chinese University of Hong Kong (Shenzhen) ♥ Shenzhen Research Institute of Big Data Han Qin ♠hanqin@link.cuhk.edu.cn♠songyan@cuhk.edu.cn University of Washington ♦ QTrade The Chinese University of Hong Kong (Shenzhen) ♥ Shenzhen Research Institute of Big Data Yan Song University of Washington ♦ QTrade The Chinese University of Hong Kong (Shenzhen) ♥ Shenzhen Research Institute of Big Data Federated Chinese Word Segmentation with Global Character Associations Association for Computational Linguistics: ACL-IJCNLP 2021 August 1-6, 20214306 Chinese word segmentation (CWS) is a fundamental task for Chinese information processing, which always suffers from out-ofvocabulary word issues, especially when it is tested on data from different sources. Although one possible solution is to use more training data, in real applications, these data are stored at different locations and thus are invisible and isolated among each other owing to the privacy or legal issues (e.g., clinical reports from different hospitals). To address this issue and benefit from extra data, we propose a neural model for CWS with federated learning (FL) adopted to help CWS deal with data isolation, where a mechanism of global character associations is proposed to enhance FL to learn from different data sources. Experimental results on a simulated environment with five nodes confirm the effectiveness of our approach, where our approach outperforms different baselines including some well-designed FL frameworks. 1 Introduction Chinese word segmentation (CWS) is a preliminary and vital task for natural language processing (NLP). This task aims to segment Chinese character sequence into words and thus is generally performed as a sequence labeling task (Tseng et al., 2005;Levow, 2006;Song et al., 2009a;Sun and Xu, 2011;Xia, 2012, 2013;Mansur et al., 2013). Although recent neural-based CWS systems (Pei et al., 2014;Chen et al., 2017;Ma et al., 2018;Higashiyama et al., 2019;Qiu et al., 2019;Ke et al., 2020;Huang et al., 2020a;Tian et al., 2020e) have achieved very good performance on benchmark datasets, it is still an unsolved task (Fu * Equal contribution. † Corresponding author. 1 The code and models involved in this paper are released at https://github.com/cuhksz-nlp/GCASeg. et al., 2020), because it is challenging to handle out-of-vocabulary words (OOV), especially in real applications where the test data may come from different sources. Although leveraging extra labeled data from other sources or domains could alleviate this issue, in real applications, such data are always located in different nodes and thus are inaccessible to each other because of the privacy or legal concerns (e.g., clinical or financial reports from different hospitals or companies). To address the data isolation issue, federated learning (FL) (Shokri and Shmatikov, 2015;Konečnỳ et al., 2016) is proposed and has shown its great promises for many machine learning tasks (Aono et al., 2017;Sheller et al., 2018;He et al., 2020). In many cases, data in different nodes are encrypted and aggregated to the centralized model, and they are invisible to each other during the training stage. This property allows FL to be an essential technique for real applications with privacy and security requirements. However, conventional FL techniques are more suitable for nodes sharing homogeneous data, which is seldom the case for NLP tasks. Particularly for CWS, the appropriate segmentation is sensitive to the data source, where the text and vocabularies used in different datasets contain various expressing patterns. For example, in real applications such as Input Method Editors (IME, such as pinyin input environment), there are millions of individual users with their data stored in isolated nodes, where the different nodes could have diverse segmentation requirement due to the users' preference. Therefore, the restricted data access of traditional FL approaches could result in inferior performance for CWS since they cannot update the model to facilitate localized prediction. Unfortunately, limited attentions have been paid to address this issue. Most existing approaches (Liu et al., 2019;Huang et al., 2020b;Sui et al., 2020) with FL on NLP (e.g., for language modeling Figure 1: The server-node architecture of our approach. The encrypted information (i.e., encrypted data, word segmentation tags, and loss) communicates between a node and the server, where the locally stored data is inaccessible to other nodes during the training process. (Hard et al., 2018;Chen et al., 2019), named entity recognition (Ge et al., 2020), and text classification (Zhu et al., 2020)) mainly focus on optimizing the learning process and ignore domain diversities. In this paper, we propose a FL-based neural model (GCA-FL) for CWS, which is enhanced by global character association (GCA) mechanism in a distributed environment. The GCA mechanism is designed to capture contextual information (patterns) in a particular input for localized predictions and to handle the difficulties in identifying text sources caused by data inaccessibility. Specifically, GCA is served as a server-side component to associate global character n-grams with different inputs from each node and responds with contextual information to help the backbone segmenter. Experimental results on a simulated environment with isolated data from five domains demonstrate the effectiveness of our approach, where GCA-FL outperforms different baselines including the ones with well designed FL framework. Figure 1 illustrates the overall server-node architecture for applying our approach. The centralized model is stored in the FL server and data from multiple sources (domains) are stored at different nodes (the i-th node is denoted by N i ), respectively. Encrypted information (e.g., data, vectors, and loss) communicates between each node N i and the FL server. In this way, the original data stay in the local node and is not accessible to the other nodes. To encode contextual information (patterns) to facilitate localized prediction, we enhance FL by introducing GCA into the centralized model (Figure 2), which follows the character-based sequence labeling paradigm for CWS. Herein, GCA encodes the contextual information from the encrypted input and uses the resulted information to guide the centralized model to make a localized prediction. In the following, we introduce FL for CWS and then the centralized model with GCA. The approach Federated Learning In the training process of FL, the node N i firstly encrypts the original input character sequence X i into X i = x i,1 · · · x i,j · · · x i,l , where x i,j denotes the j-th character in X i . Next, N i passes X i to the server. Then, the centralized model on the server processes X i and predicts the corresponding label sequence Y i = y i,1 · · · y i,j · · · y i,l by Y i = GCA-FL X i(1) where y i,j ∈ T (T is the label set) is the segmentation label for x i,j . Afterwards, Y i is passed back to N i and compared with the gold label sequence Y * i , after which the loss L i for that training instance is obtained locally. Finally, L i is passed to the server and the parameters in the centralized model are updated accordingly. Centralized Model with GCA In standard FL framework, the backbone centralize model works following the encoding-decoding paradigm, where X i is encoded 2 into a sequence of hidden vectors (h i,j denotes the hidden vector for x i,j ), which are then sent to a decoder (e.g., CRF) to obtain the prediction Y i . However, data from different nodes (sources) always contains heterogeneous vocabularies and expressing patterns, where standard FL may obtain inferior results for localized prediction because it cannot distinguish the contextual information from the isolated data. Therefore, motivated by previous studies that leverage n-grams to capture local contextual information (Song et al., 2009b;Pei et al., 2014;Chen et al., 2017;Higashiyama et al., 2019), we propose GCA to enhance standard FL by exploring the contextual information carried by n-grams in the running text and use it to guide the centralized model for making localized prediction. Specifically, GCA contains three components, namely, a lexicon (D) that contains global character n-grams, an n-gram embedding matrix that maps an n-gram in D to its embedding, and a position embedding matrix that maps a position pattern, i.e., the position (e.g., beginning, ending, and inside) of a character in an n-gram, to its embedding. For each character x i,j , GCA encodes the contextual information and uses it to enhance the centralized model in the following process. First, GCA extracts all m n-grams s i,j,k (1 ≤ k ≤ m) associated with x i,j from D, where s i,j,k satisfies the conditions that it contains x i,j and it is a sub-string of X i . Next, according to the position of x i,j in s i,j,k , GCA finds the position pattern v i,j,k associated with s i,j,k and x i,j based on the rules specified in Table 1. For example, if x i,j ="市" (city) and s i,j,k ="市长" (mayor), the position pattern v i,j,k will be "V B " according to the rules in Table 1, because x i,j is at the beginning of s i,j,k . Third, GCA applies n-gram embedding matrix and position embedding matrix to s i,j,k and v i,j,k , respectively, and obtains the ngram embedding e s i,j,k and the position embedding e v i,j,k . Then, GCA computes the weights p i,j,k for position patterns v i,j,k by where W is a trainable matrix that maps e s i,j,k to the same dimension as h i,j to facilitate the inner production "·". GCA further applies p i,j,k to all position embeddings and obtain the representation of contextual information u i,j for x i,j by Rule v i,j,k x i,j is the beginning of the n-gram s i,j,k V B x i,j is inside the n-gram s i,j,k V I x i,j is the ending of the n-gram s i,j,k V E x i,j is the single-character n-gram s i,j,k V Sp i,j,k = exp(h i,j · W · e s i,j,k ) m k=1 exp(h i,j · W · e s i,j,k )(2)u i,j = m k=1 p i,j,k · e v i,j,k(3) Afterwards, u i,j is added (+) to h i,j to guide the backbone model for localized prediction, where the resulted vector is mapped into the output space by a trainable matrix W o and bias b o by o i,j = W o · (u i,j + h i,j ) + b o(4) Finally, o i,j is fed into a CRF decoder to obtain the predicted segmentation label y i,j for x i,j . Experimental Settings Simulations To test the proposed approach, we follow the convention of recent FL-based NLP studies (Liu et al., 2019;Huang et al., 2020b;Zhu et al., 2020;Sui et al., 2020) to build a simulated environment where isolated data are stored in five nodes. Each node contains one of the five genres (i.e., broadcast conversation (BC), broadcast news (BN), magazine (MZ), newswire (NW), and weblog (WEB)) in CTB7 (LDC2010T07) 3 for CWS. Therefore, data from different genres are distributed to the five nodes without overlapping (i.e., the data sources in our simulation are heterogeneous), which is similar to the simulation setting of aforementioned previous studies. We split each genre into train/dev/test splits following Wang et al. (2011) and report the statistics (in terms of the number of sentences, word tokens, and OOV rate) in Table 2. Baselines and Reference Models To show the effectiveness of our approach with GCA, we compare it with a baseline model that follows the FL framework without using it. In addition, we also run two reference models without both FL and GCA, where all training instances are not isolated and are accessible to each other. Specifically, the first reference model (denoted by Single) is trained and evaluated on the data from a single node (genre). The second (denoted by Union) is trained on the union of training instances from all five nodes (genres) and evaluated on a single node. Herein, the Union reference model can be optimized on a particular local node to achieve the best localized prediction; on the contrary, models under the FL setting is stored on the server and shared by all nodes, so that optimizing the model on a particular node could significantly hurt the performance on others. Therefore, the setting of the Union reference model is the ideal situation which is hard to happen in real-applications and it thus provides a potential upper-boundary of model performance for FL-based approaches. Implementation A good text representation is generally a prerequisite to achieve outstanding model performance (Pennington et al., 2014;Peters et al., 2018). To obtain a high qulity of text representation, in our experiments, we try two types of encoder in the centralized model, i.e., the Chinese version of BERT (Devlin et al., 2019) 4 and the large version of ZEN 2.0 5 , because they are pre-trained language models that have been demonstrated to be effective in many NLP tasks (Nie et al., 2020;Huang et al., 2020a;Fu et al., 2020;Tian et al., 2020aTian et al., ,b,c,d, 2021aQin et al., 2021). For both BERT and ZEN 2.0, we use the default settings (i.e., 12 layers of multi-head attentions with 768 dimensional hidden vectors for BERT and 24 layers of multi-head attentions with 1024 dimensional hidden vectors for ZEN 2.0). We use the vocabulary in Tencent Embedding 6 to initialize our lexicon D and the n-gram embedding matrix, where n-grams whose character-based length higher than five are filtered out 7 . During the training stage, we fix the n-gram embedding matrix and update all other parameters (including BERT). For evaluation, we follow previous studies to use the F1 scores (Chen et al., 2017;Ma et al., 2018;Qiu et al., 2019). For other hyperparameter settings, we report them in Table 3. We test all combinations of them for each model on the development set, where models achieve highest F1 score on the development set is evaluated on the test set (the best hyper-parameter setting in our experiments is highlighted in boldface). Table 4 illustrates the experimental results (i.e., F1 scores) of our GCA-FL models and all the aforementioned baselines (i.e., FL) and reference models (i.e., Single and Union) with BERT (a) and ZEN 2.0 (b) encoders on the test set of BC, BN, MZ, NW, and Web from CTB7. There are several observations from the test set results. First, models under the FL framework (i.e., FL and GCA-FL) outperform the reference model (Single) trained on the single node for both BERT and ZEN 2.0 encoder, which confirms that FL works well to leverage extra isolated data. Second, our GCA-FL model consistently outperforms the FL baseline on all nodes (genres), although the FL baseline with BERT and ZEN 2.0 has already achieved very good performance. This observation demonstrates the effectiveness of the proposed GCA mechanism to leverage contextual information to facilitate localized prediction. Third, it is observed that GCA-FL achieves competitive results compared with the Union reference model in most cases. This observation is promising because the Union model has all training data available without suffering from the data isolation problem, which could provide a potential upper boundary for FLbased models. The results obtained from GCA-FL thus further confirm the effectiveness of GCA. Results and Analysis Overall Results Effect of GCA To analize the effect of GCA to leverage isolated extra data to facilitate localized prediction, especially for OOV, we illustrate the recall of OOV of different models (i.e., Single, Union, FL, and GCA-FL) on five nodes (genres) with BERT encoder in Figure 3. Similar to the experimental results in the main experiments, it is observed that FL and GCA-FL outperform Single model in identifying unseen words (OOV). Further, GCA-FL can outperform the FL baseline on the test data in all nodes, where the highest improvement is observed on the node storing data from newswire. One possible explanation could be that BC and BN contains similar texts to NW. GCA-FL can better learn from the similar data on these nodes and thus improves localized prediction, especially for OOV. Conclusion In this paper, we apply FL to CWS to leverage isolated data stored in different nodes and propose GCA to enhance the CWS model stored in the server. Specifically, our approach encodes the contextual information by associating the input characters with global character n-grams, and uses that information to guide the backbone model to make localized predictions. Experimental results under a simulated environment performed on five isolated nodes on CTB7 demonstrate the effectiveness of the proposed approach. Our approach outperforms the baseline model trained under the FL framework and achieves competitive results compared with the reference model that is trained on the union of the data from all nodes. Further analyses on identifying OOV justify the validity of the GCA mechanism to leverage the data on other nodes to facilitate localized prediction and demonstrate its great potential to be applied to real-world applications. Figure 2 : 2An overview of GCA-FL, where centralized model is illustrated on the top and the example input sentence "南京市长江大桥" (Nanjing Yangtze River Bridge) from N i is shown on the bottom. "/" in the output represents the delimiter for the word boundaries. Figure 3 : 3The recall of out-of-vocabulary words (OOV) of different BERT-based models (i.e., Single, FL, GCA-FL, and Union) on the test set of five nodes (genres). Table 1 : 1The rules for assigning different position patterns to x i,j based on its position in an n-gram s i,j,k . Table 2 : 2The number of sentences, word tokens, and the out-of-vocabulary (OOV) rate (in dev and test sets) with respect to the training set of five genres in CTB7. Table 3 : 3The hyper-parameters tested in tuning our models. The best ones used in our final experiments are highlighted in boldface. Table 4 : 4Experimental results (i.e., F1 scores) of different models with BERT (a) and ZEN 2.0 (b) on the development sets of the five nodes (genres) of CTB7. One can use any encoder, e.g., biLSTM, for this process. https://catalog.ldc.upenn.edu/ LDC2010T07 We use the Chinese base model from https://s3. amazonaws.com/models.huggingface.co/.5 We download the Chinese version of ZEN 2.0 from https://github.com/sinovation/ZEN2. https://ai.tencent.com/ailab/nlp/en/ embedding.html7 We use five as the threshold because most Chinese words contain no more than five characters. 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Cross-Lingual Lexical Triggers in Statistical Language Modeling

We propose new methods to take advantage of text in resource-rich languages to sharpen statistical language models in resource-deficient languages. We achieve this through an extension of the method of lexical triggers to the cross-language problem, and by developing a likelihoodbased adaptation scheme for combining a trigger model with an ¡ -gram model. We describe the application of such language models for automatic speech recognition. By exploiting a side-corpus of contemporaneous English news articles for adapting a static Chinese language model to transcribe Mandarin news stories, we demonstrate significant reductions in both perplexity and recognition errors. We also compare our cross-lingual adaptation scheme to monolingual language model adaptation, and to an alternate method for exploiting cross-lingual cues, via crosslingual information retrieval and machine translation, proposed elsewhere.

Cross-Lingual Lexical Triggers in Statistical Language Modeling Woosung Kim woosung@cs.jhu.edu The Johns Hopkins University 3400 N. Charles StBaltimoreMD Sanjeev Khudanpur khudanpur@jhu.edu The Johns Hopkins University 3400 N. Charles StBaltimoreMD Cross-Lingual Lexical Triggers in Statistical Language Modeling We propose new methods to take advantage of text in resource-rich languages to sharpen statistical language models in resource-deficient languages. We achieve this through an extension of the method of lexical triggers to the cross-language problem, and by developing a likelihoodbased adaptation scheme for combining a trigger model with an ¡ -gram model. We describe the application of such language models for automatic speech recognition. By exploiting a side-corpus of contemporaneous English news articles for adapting a static Chinese language model to transcribe Mandarin news stories, we demonstrate significant reductions in both perplexity and recognition errors. We also compare our cross-lingual adaptation scheme to monolingual language model adaptation, and to an alternate method for exploiting cross-lingual cues, via crosslingual information retrieval and machine translation, proposed elsewhere. Data Sparseness in Language Modeling Statistical techniques have been remarkably successful in automatic speech recognition (ASR) and natural language processing (NLP) over the last two decades. This success, however, depends crucially ¢ This research was supported by the National Science Foundation (via Grant Nō ITR-0225656 and IIS-9982329) and the Office of Naval Research (via Contract Nō N00014-01-1-0685). on the availability of accurate and large amounts of suitably annotated training data and it is difficult to build a usable statistical model in their absence. Most of the success, therefore, has been witnessed in the so called resource-rich languages. More recently, there has been an increasing interest in languages such as Mandarin and Arabic for ASR and NLP, and data resources are being created for them at considerable cost. The data-resource bottleneck, however, is likely to remain for a majority of the world's languages in the foreseeable future. Methods have been proposed to bootstrap acoustic models for ASR in resource deficient languages by reusing acoustic models from resource-rich languages (Schultz and Waibel, 1998;Byrne et al., 2000). Morphological analyzers, noun-phrase chunkers, POS taggers, etc., have also been developed for resource deficient languages by exploiting translated or parallel text (Yarowsky et al., 2001). Khudanpur and Kim (2002) recently proposed using cross-lingual information retrieval (CLIR) and machine translation (MT) to improve a statistical language model (LM) in a resource-deficient language by exploiting copious amounts of text available in resource-rich languages. When transcribing a news story in a resource-deficient language, their core idea is to use the first pass output of a rudimentary ASR system as a query for CLIR, identify a contemporaneous English document on that news topic, followed by MT to provide a rough translation which, even if not fluent, is adequate to update estimates of word frequencies and the LM vocabulary. They report up to a 28% reduction in perplexity on Chinese text from the Hong Kong News corpus. In spite of their considerable success, some shortcomings remain in the method used by Khudanpur and Kim (2002). Specifically, stochastic translation lexicons estimated using the IBM method (Brown et al., 1993) from a fairly large sentence-aligned Chinese-English parallel corpus are used in their approach -a considerable demand for a resourcedeficient language. It is suggested that an easierto-obtain document-aligned comparable corpus may suffice, but no results are reported. Furthermore, for each Mandarin news story, the single best matching English article obtained via CLIR is translated and used for priming the Chinese LM, no matter how good the CLIR similarity, nor are other wellmatching English articles considered. This issue clearly deserves further attention. Finally, ASR results are not reported in their work, though their proposed solution is clearly motivated by an ASR task. We address these three issues in this paper. Section 2 begins, for the sake of completeness, with a review of the cross-lingual story-specific LM proposed by Khudanpur and Kim (2002). A notion of cross-lingual lexical triggers is proposed in Section 3, which overcomes the need for a sentencealigned parallel corpus for obtaining translation lexicons. After a brief detour to describe topicdependent LMs in Section 4, a description of the ASR task is provided in Section 5, and ASR results on Mandarin Broadcast News are presented in Section 6. The issue of how many English articles to retrieve and translate into Chinese is resolved by a likelihood-based scheme proposed in Section 6.1. Cross-Lingual Story-Specific LMs For the sake of illustration, consider the task of sharpening a Chinese language model for transcribing Mandarin news stories by using a large corpus of contemporaneous English newswire text. Mandarin Chinese is, of course, not resource-deficient for language modeling -100s of millions of words are available on-line. However, we choose it for our experiments partly because it is sufficiently different from English to pose a real challenge, and because the availability of large text corpora in fact permits us to simulate controlled resource deficiency. Let does not imply that the English document £ ¥ needs to be an exact translation of the Mandarin story £ ¤ . It is quite adequate, for instance, if the two stories report the same news event. This approach is expected to be helpful even when the English document is merely on the same general topic as the Mandarin story, although the closer the content of a pair of articles the better the proposed methods are likely to work. Assume for the time being that a sufficiently good Chinese-English story alignment is given. Assume further that we have at our disposal a stochastic translation dictionary -a probabilistic model of the form " ! $ # & %' ) ( -which provides the Chinese translation # 1 0 3 2 of each English word ' 4 0 6 5 , where 2 and 5 respectively denote our Chinese and English vocabularies. Computing a Cross-Lingual Unigram LM Let 7 8 ! 9 ' ¥ %£ ( denote the relative frequency of a word ' in the document £ , ' @ 0 A 5 , B @ C E D F C ¡ . It seems plausible that, G H # H 0 I 2 , Q P S R ¥ T S U W V W X Ỳ b a d c b e f ! $ # & %£ ( h g p i q s r t u " ! $ # & %' ) ( 7 v ! 9 ' ¥ %£ ( w § (1) would be a good unigram model for the D -th Mandarin story £ ¤ . We use this cross-lingual unigram statistic to sharpen a statistical Chinese LM used for processing the test story £ ¤ . One way to do this is via linear interpolation P x R y T S XV 9 a c 9 ! $ # W w %# W T ¦ § # W T § £ ( h g (2) P x R y T S U W V X Ỳ b a c b e ! $ # w %£ ( ! b B ( d @ ! $ # W w %# T ¦ § # W T ( of the cross-lingual unigram model (1) with a static trigram model for Chinese, where the interpolation weight may be chosen off-line to maximize the likelihood of some held-out Mandarin stories. The improvement in (2) is expected from the fact that unlike the static text from which the Chinese trigram LM is estimated, £ is semantically close to £ ¤ and even the adjustment of unigram statistics, based on a stochastic translation model, may help. Figure 1 shows the data flow in this cross-lingual LM adaptation approach, where the output of the first pass of an ASR system is used by a CLIR system to find an English document £ , an MT system computes the statistic of (1), and the ASR system uses the LM of (2) in a second pass. Obtaining Matching English Documents To illustrate how one may obtain the English document £ to match a Mandarin story £ ¤ , let us assume that we also have a stochastic reversetranslation lexicon ! 9 ' ¥ %# ( . One obtains from the first pass ASR output, cf. Figure 1, " ! 9 ' ¥ %# ( to compute, G f ' f 0 g 5 , h P x R y T S U W V X Ỳ b a c b e f ! 9 ' ¥ %£ ¤ ( h g i i b r j u " ! 9 ' y %# ( 7 k ! $ # & %£ ¤ ( w §(3) an English bag-of-words representation of the Mandarin story £ ¤ as used in standard vector-based information retrieval. The document with the highest TF-IDF weighted cosine-similarity to £ ¤ is selected: £ g l ) m n ö @ l q p r s s t sim! $ h P x R y T S U V W X Ỳ b a c b e u ! 9 ' ¥ %£ ¤ ( § 7 k ! 9 ' ¥ %£ v ( b ( w © Readers familiar with information retrieval literature will recognize this to be the standard querytranslation approach to CLIR. Obtaining Stochastic Translation Lexicons The translation lexicons u ! $ # & %' w ( and ! 9 ' ¥ %# ( may be created out of an available electronic translation lexicon, with multiple translations of a word being treated as equally likely. Stemming and other morphological analyses may be applied to increase the vocabulary-coverage of the translation lexicons. Alternately, they may also be obtained automatically from a parallel corpus of translated and sentence-aligned Chinese-English text using statistical machine translation techniques, such as the publicly available GIZA++ tools (Och and Ney, 2000), as done by Khudanpur and Kim (2002). Unlike standard MT systems, however, we apply the translation models to entire articles, one word at a time, to get a bag of translated words -cf. (1) and (3). Finally, for truly resource deficient languages, one may obtain a translation lexicon via optical character recognition from a printed bilingual dictionary (cf. Doerman et al (2002)). This task is arguably easier than obtaining a large LM training corpus. Cross-Lingual Lexical Triggers It seems plausible that most of the information one gets from the cross-lingual unigram LM of (1) is in the form of the altered statistics of topic-specific Chinese words conveyed by the statistics of contentbearing English words in the matching story. The translation lexicon used for obtaining the information, however, is an expensive resource. Yet, if one were only interested in the conditional distribution of Chinese words given some English words, there is no reason to require translation as an intermediate step. In a monolingual setting, the mutual information between lexical pairs co-occurring anywhere within a long "window" of each-other has been used to capture statistical dependencies not covered by ¡ -gram LMs (Rosenfeld, 1996;Tillmann and Ney, 1997). We use this inspiration to propose the following notion of cross-lingual lexical triggers. In a monolingual setting, a pair of words ! $ x y § z ( is considered a trigger-pair if, given a word-position in a sentence, the occurrence of x in any of the preceding word-positions significantly alters the (conditional) probability that the following word in the sentence is z : x is said to trigger z . E.g. the occurrence of either significantly increases the probability of or subsequently in the sentence. The set of preceding word-positions is variably defined to include all words from the beginning of the sentence, paragraph or document, or is limited to a fixed num-ber of preceding words, limited of course by the beginning of the sentence, paragraph or document. In the cross-lingual setting, we consider a pair of words ! 9 ' w § # ( , ' g 0 { 5 and # I 0 { 2 , to be a trigger-pair if, given an English-Chinese pair of aligned documents, the occurrence of ' in the English document significantly alters the (conditional) probability that the word # appears in the Chinese document: ' is said to trigger # . It is plausible that translation-pairs will be natural candidates for trigger-pairs. It is, however, not necessary for a trigger-pair to also be a translation-pair. E.g., the occurrence of Belgrade in the English document may trigger the Chinese transliterations of Serbia and Kosovo, and possibly the translations of China, embassy and bomb! By infering trigger-pairs from a documentaligned corpus of Chinese-English articles, we expect to be able to discover semantically-or topicallyrelated pairs in addition to translation equivalences. Identification of Cross-Lingual Triggers Average mutual information, which measures how much knowing the value of one random variable reduces the uncertainty of about another, has been used to identify trigger-pairs. We compute the average mutual information for every English-Chinese word pair ! 9 ' w § # ( as follows. Let | £ § £ ¤ } , D g B ) § © © © § ¡ ,@ ! 9 ' & § # ( g £ y ! 9 ' w § # ( ¡ l w @ ! 9 ' w § # q ( h g £ ! 9 ' & § # q ( ¡ ©! 9 ' & # ( h g @ ! 9 ' w § # ( w n Q i q Q i 6 @ ! 9 ' w § # q ( w n Q d i s q Q d i d @ ! ' § # ( w n Q i s q Q i 6 @ ! ' w § # q ( w n Q d i s q Q d i © We propose to select word pairs with high mutual information as cross-lingual lexical triggers. There are %5 % %2 " % possible English-Chinese word pairs which may be prohibitively large to search for the pairs with the highest mutual information. We filter out infrequent words in each language, say, words appearing less than 5 times, then measure ! 9 ' w # ( for all possible pairs from the remaining words, sort them by ! 9 ' w # ( , and select, say, the top 1 million pairs. Estimating Trigger LM Probabilities Once we have chosen a set of trigger-pairs, the next step is to estimate a probability w a (1) X Ỳ ! $ # & %' w ( in lieu of the translation probability u " ! $ # & %' ) ( in' in £ , namely w a X Ỳ ! $ # & %' ) ( g w r s q ¡ r ¡ ! $ # ( i d ¢ r j w r s q ¡ r s ¡ ! $ # £ ( ©(4) As an ad hoc alternative to (4), we also use w a X Ỳ ! $ # & %' w ( h g ! 9 ' w # ( i 9 ¢ r j ! 9 ' & # s £ ¤ ( § (5) where we set ! 9 ' w # ( v g ¦ ¥ whenever ! 9 ' w § # ( is not a trigger-pair, and find it to be somewhat more effective (cf. Section 6.2). Thus (5) is used henceforth in this paper. Analogous to (1), we set w a X Ỳ T S U W V W X Ỳ b a d c b e u ! $ # & %£ ( Q g i q r q t w a d XY ¥ ! $ # w %' w ( 7 k ! 9 ' y %£ ( w §(6) and, again, we build the interpolated model w a X Ỳ T S XV 9 a d c d 9 ! $ # w %# W T ¦ § # T § £ ( h g (7) w a X Ỳ T S U W V W X Ỳ b a d c b e ! $ # W w %£ ( § ! b B ( d @ ! $ # W w %# W T ¦ § # W T ( © Topic-Dependent Language Models The linear interpolation of the story-dependent unigram models (1) and (6) with a story-independent trigram model, as described above, is very reminiscent of monolingual topic-dependent language models (cf. e.g. (Iyer and Ostendorf, 1999)). This motivates us to construct topic-dependent LMs and contrast their performance with these models. To this end, we represent each Chinese article in the training corpus by a bag-of-words vector, and cluster the vectors using a standard K-means algorithm. We use random initialization to seed the algorithm, and a standard TF-IDF weighted cosinesimilarity as the "metric" for clustering. We perform a few iterations of the K-means algorithm, and deem the resulting clusters as representing different topics. We then use a bag-of-words centroid created from all the articles in a cluster to represent each topic. w W X b T S a X Ỳ b a c b e u ! $ # w %# W T ¦ § # T § b ¬ ( h g (8) ® ! $ # S %# W T ¦ § # T ( ! b B F ( d 8 ! $ # w %# W T ¦ § # T ( and used in a second pass of recognition. Alternatives to topic-dependent LMs for exploiting long-range dependencies include cache LMs and monolingual lexical triggers; both unlikely to be as effective in the presence of significant ASR errors. ASR Training and Test Corpora We investigate the use of the techniques described above for improving ASR performance on Mandarin news broadcasts using English newswire texts. We have chosen the experimental ASR setup created in the 2000 Johns Hopkins Summer Workshop to study Mandarin pronunciation modeling, extensive details about which are available in Fung et al (2000). The acoustic training data (¯10 hours) for their ASR system was obtained from the 1997 Mandarin Broadcast News distribution, and contextdependent state-clustered models were estimated using initials and finals as subword units. Two Chinese text corpora and an English corpus are used to estimate LMs in our experiments. A vocabulary 2 of 51K Chinese words, used in the ASR system, is also used to segment the training text. This vocabulary gives an OOV rate of 5% on the test data. XINHUA: We use the Xinhua News corpus of about 13 million words to represent the scenario when the amount of available LM training text borders on adequate, and estimate a baseline trigram LM for one set of experiments. HUB-4NE: We also estimate a trigram model from only the 96K words in the transcriptions used for training acoustic models in our ASR system. This corpus represents the scenario when little or no additional text is available to train LMs. NAB-TDT: English text contemporaneous with the test data is often easily available. For our test set, described below, we select (from the North American News Text corpus) articles published in 1997 in The Los Angeles Times and The Washington Post, and articles from 1998 in the New York Times and the Associated Press news service (from TDT-2 corpus). This amounts to a collection of roughly 45,000 articles containing about 30-million words of English text; a modest collection by CLIR standards. Our ASR test set is a subset (Fung et al (2000)) of the NIST 1997 and 1998 HUB-4NE benchmark tests, containing Mandarin news broadcasts from three sources for a total of about 9800 words. We generate two sets of lattices using the baseline acoustic models and bigram LMs estimated from XINHUA and HUB-4NE. All our LMs are evaluated by rescoring°)¥ w ¥ -best lists extracted from these two sets of lattices. The°)¥ w ¥ -best lists from the XINHUA bigram LM are used in all XINHUA experiments, and those from the HUB-4NE bigram LM in all HUB-4NE experiments. We report both word error rates (WER) and character error rates (CER), the latter being independent of any difference in segmentation of the ASR output and reference transcriptions. ASR Performance of Cross-Lingual LMs We begin by rescoring the°)¥ w ¥ -best lists from the bigram lattices with trigram models. For each test story £ ¤ , we perform CLIR using the first pass ASR output to choose the most similar English document £ from NAB-TDT. Then we create the cross-lingual unigram model of (1). We also find the interpolation weight which maximizes the likelihood of the 1-best hypotheses of all test utterances from the first ASR pass. All ± -values reported in this paper are based on the standard NIST MAPSSWE test (Pallett et al., 1990), and indicate the statistical significance of a WER improvement over the corresponding trigram baseline, unless otherwise specified. Evidently, the improvement brought by CLinterpolated LM is not statistically significant on XINHUA. On HUB-4NE however, where Chinese text is scarce, the CL-interpolated LM delivers considerable benefits via the large English corpus. Likelihood-Based Story-Specific Selection of Interpolation Weights and the Number of English Documents per Mandarin Story The experiments above naïvely used the one most similar English document for each Mandarin story, and a global in (2), no matter how similar the best matching English document is to a given Mandarin news story. Rather than choosing one most similar English document from NAB-TDT, it stands to reason that choosing more than one English document may be helpful if many have a high similarity score, and perhaps not using even the best matching document may be fruitful if the match is sufficiently poor. It may also help to have a greater interpolation weight for stories with good matches, and a smaller for others. For experiments in this subsection, we select a different for each test story, again based on maximizing the likelihood of the B best output given a CL-Unigram model. The other issue then is the choice and the number of English documents to translate. ³ -best documents: One could choose a predetermined number ¡ of the best matching English doc-uments for each Mandarin story. We experimented with values of B , B ¥ ,°)¥ ,)¥ , µ ) ¥ and B ¥ w ¥ , and found that ¡ g ¶° ) ¥ gave us the best LM performance, but only marginally better than ¡ g · B as described above. Details are omitted, as they are uninteresting. All documents above a similarity threshold: The argument against always taking a predetermined number of the best matching documents may be that it ignores the goodness of the match. An alternative is to take all English documents whose similarity to a Mandarin story exceeds a certain predetermined threshold. As this threshold is lowered, starting from a high value, the order in which English documents are selected for a particular Mandarin story is the same as the order when choosing the ¡ -best documents, but the number of documents selected now varies from story to story. It is possible that for some stories, even the best matching English document falls below the threshold at which other stories have found more than one good match. We experimented with various thresholds, and found that while a threshold of ¥ x © B gives us the lowest perplexity on the test set, the reduction is insignificant. This points to the need for a story-specific strategy for choosing the number of English documents, instead of a global threshold. Likelihood-based selection of the number of English documents: Figure 2 shows the perplexity of the reference transcriptions of one typical test story under the LM (2) as a function of the number of English documents chosen for creating (1). For each choice of the number of English documents, the interpolation weight in (2) is chosen to maximize the likelihood (also shown) of the first pass output. This suggests that choosing the number of English documents to maximize the likelihood of the first pass ASR output is a good strategy. For each Mandarin test story, we choose the 1000-best-matching English documents and divide the dynamic range of their similarity scores evenly into 10 intervals. Next, we choose the documents in the top ¦ ¦ ¹ -th of the range of similarity scores, not necessarily the top- B ¥ w ¥ documents, compute P x R y T S U V W X Ỳ b a c b e ! $ # & %£ ( , determine the in (2) that maximizes the likelihood of the first pass output of only the utterances in that story, and record this likelihood. We repeat this with documents in the top ¦ ¹ -th of the range of similarity scores, the top º ¦ ¹ -th, etc., and obtain the likelihood as a function of the similarity threshold. We choose the threshold that maximizes the likelihood of the first pass output. Thus the number of English documents £ in (1), as well as the interpolation weight in (2), are chosen dynamically for each Mandarin story to maximize the likelihood of the ASR output. Table 2 shows ASR results for this likelihood-based story-specific adaptation scheme. Note that significant WER improvements are obtained from the CL-interpolated LM using likelihood-based story-specific adaptation even for the case of the XINHUA LM. Furthermore, the performance of the CL-interpolated LM is even better than the topic-dependent LM. This is remarkable, since the CL-interpolated LM is based on unigram statistics from English documents, while the topictrigram LM is based on trigram statistics. We believe that the contemporaneous and story-specific nature of the English document leads to its relatively higher effectiveness. Our conjecture, that the contemporaneous cross-lingual statistics and static topic-trigram statistics are complementary, is supported by the significant further improvement in WER obtained by the interpolation of the two LMs, as shown on the last line for XINHUA. The significant gain in ASR performance in the resource deficient HUB-4NE case are obvious. The small size of the HUB-4NE corpus makes topicmodels ineffective. Comparison of Cross-Lingual Triggers with Stochastic Translation Dictionaries Once we select cross-lingual trigger-pairs as described in Section 3, ! $ # & %' w ( in (1) is replaced by w a X Ỳ ! $ # & %' ) ( of (5), and u " ! 9 ' y %# ( in (3) by w a d X Ỳ ! 9 ' ¥ %# ( . Therefore, given a set of cross-lingual trigger-pairs, the trigger-based models are free from requiring a translation lexicon. Furthermore, a documentaligned comparable corpus is all that is required to construct the set of trigger-pairs. We otherwise follow the same experimental procedure as above. As Table 2 shows, the trigger-based model (Triginterpolated) performs only slightly worse than the CL-interpolated model. One explanation for this degradation is that the CL-interpolated model is trained from the sentence-aligned corpus while the trigger-based model is from the document-aligned corpus. There are two steps which could be affected by this difference, one being CLIR and the other being the translation of the £ 's into Chinese. Some errors in CLIR may however be masked by our likelihood-based story-specific adaptation scheme, since it finds optimal retrieval settings, dynamically adjusting the number of English documents as well as the interpolation weight, even if CLIR performs somewhat suboptimally. Furthermore, a documentaligned corpus is much easier to build. Thus a much bigger and more reliable comparable corpus may be used, and eventually more accurate trigger-pairs will be acquired. We note with some satisfaction that even simple trigger-pairs selected on the basis of mutual information are able to achieve perplexity and WER reductions comparable to a stochastic translation lexicon: the smallest ± -value at which the difference between the WERs of the CL-interpolated LM and the Trig-interpolated LM in Table 2 would be significant is ¥ x © » for XINHUA and ¥ x © ½ ¼ for HUB-4NE. Triggers (4) vs (5): We compare the alternative w a X Ỳ ! ¾ d % ½ ¾ ¿ ( definitions (4) and (5) for replacing ! ¾ d % ½ ¾ ¿ ( in (1). The resulting CL-interpolated LM (2) yields a perplexity of 370 on the XINHUA test set using (4), compared to 367 using (5). Similarly, on the HUB-4NE test set, using (4) yields 736, while (5) yields 727. Therefore, (5) has been used throughout. Conclusions and Future Work We have demonstrated a statistically significant improvement in ASR WER (1.4% absolute) and in perplexity (23%) by exploiting cross-lingual sideinformation even when nontrivial amount of training data is available, as seen on the XINHUA corpus. Our methods are even more effective when LM training text is hard to come by in the language of interest: 47% reduction in perplexity and 1.3% absolute in WER as seen on the HUB-4NE corpus. Most of these gains come from the optimal choice of adaptation parameters. The ASR test data we used in our experiments is derived from a different source than the corpus on which the translation and trigger models are trained, and the techniques work even when the bilingual corpus is only document-aligned, which is a realistic reflection of the situation in a resource-deficient language. Figure 1 : 1Story-Specific Cross-Lingual Adaptation of a Chinese Language Model using English Text. Figure 2 : 2Perplexity of the Reference Transcription and the Likelihood of the ASR Output v/s Number of £ for a Typical Test Story. now be a documentaligned training corpus of English-Chinese article pairs. Let denote the document frequency, i.e., the number of aligned article-pairs, in which ' occurs in the English article and£ y ! 9 ' w § # ( # in the Chinese. Let £ ! 9 ' & § # q ( denote the number of aligned article- pairs in which ' occurs in the English articles but # does not occur in the Chinese article. Let Table 1 1shows the perplexity and WER for XINHUA and HUB-4NE.Language model Perp WER ± -value XINHUA trigram 426 49.9% - CL-interpolated 375 49.5% 0.208 HUB-4NE trigram 1195 60.1% - CL-interpolated 750 59.3% ² 0.001 Table 1: Word-Perplexity and ASR WER of LMs based on single English document and global . 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Annotation sémantique hors-source à l'aide de vecteurs conceptuels Mots-clefs -Keywords Fabien Jalabert jalabert@lirmm.fr LIRMM (CNRS -Université Montpellier Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier 161, rue AdaF -34392, Cedex 5Montpellier Annotation sémantique hors-source à l'aide de vecteurs conceptuels Mots-clefs -Keywords annotation sémantique, désambiguisation sémantique lexicale WSD , word sens disambiguation, word sens tagging, annotationRésumé -AbstractDans le cadre de la recherche en sémantique lexicale, nous utilisons le modèle des vecteurs conceptuels pour représenter les sens de termes. La base vectorielle est construite à partir de définitions provenant de diverses sources lexicales, ce qui permet statistiquement de tempérer les diverses incohérences locales. Pour désigner le sens obtenu après un regroupement des définitions, nous utilisons un identificateur qui entraîne certaines contraintes. En particulier, un "cluster" de définition est désigné par une référence vers différentes définitions de la multisource. D'autre part, le contrôle de la qualité d'une classification ou désambiguisation de sens impose de faire référence en permanence au lexique source. Nous proposons donc de nommer un sens à l'aide d'un autre terme du lexique. L'annotation est un outil léger et efficace qui est essentiellement une association d'idées que l'on peut extraire de toute base de connaissance linguistique. Les annotations obtenues peuvent finalement constituer une nouvelle source d'apprentissage pour la base de vecteurs conceptuels.In the framework of research in meaning representation in NLP, we focus our attention on thematic aspects and conceptual vectors. This vectorial base is built by a morphosyntaxic analysis of several lexical resources to reduce isolated problems. Also a meaning is a cluster of definitions that are pointed by an Id number. To check the results of an automatic clustering or WSD, we must refer continously to the source dictionnary. We describe in this article a method for naming a word sens by a term of vocabulary. This kind of annotation is a light and efficient method the uses meanings associations someone or something can extract from any lexical knowledge base. Finally, the annotations should become a new lexical learning resource to improve the vectorial base. Introduction Dans le cadre de la recherche en sémantique lexicale, l'équipe TAL du LIRMM développe actuellement un système d'analyse des aspects thématiques des textes et de désambiguisation lexicale basé sur les vecteurs conceptuels. Les vecteurs représentent les idées associées à tout segment textuel (mots, expressions, textes, . . . ) via l'activation de concepts. Pour la construction des vecteurs, nous avons pris deux hypothèses principales : l'automatisation de la création de la base lexicale vectorielle par apprentissage à partir d'informations extraites de diverses sources (dictionnaires à usage humain, liste de synonymes, . . . ), et un apprentissage multisource afin de palier au bruit définitoire (par exemple les problèmes dûs au métalangage comme dans la définition d' aboyer ¡ , crier en parlant du chien). Chaque dictionnnaire proposant un découpage des sens pour une entrée, nous avons mis en place une procédure qui associe à un sens un ensemble définitions. Ce groupe de définitions est désigné par un identifiant numérique utilisé lors d'un processus de désambiguïsation. L'utilisateur, humain ou machine, doit donc connaître le codage pour pouvoir réassocier à un terme et un identifiant le sens correspondant. Chaque utilisateur doit dès lors posséder les sources lexicales du ou des désambiguïseurs auxquels ils font appel. Superviser une désambiguïsation manuellement demande systématiquement de consulter la source pour chaque terme polysémique. Nous proposons dans cet article de nommer un sens par un terme de la langue. Tout agent possédant une compétence linguistique doit être capable de retrouver le sens uniquement à partir du terme annoté (couple (terme, annotation)). Une telle procédure offre l'avantage d'une bonne interopérabilité, différents désambiguïseur peuvent proposer leurs résultats à différents clients (traducteur, indexeur, . . . ) et ces derniers peuvent faire appel à de multiples désambiguïseurs pour pallier aux lacunes de certains. Nous présentons, dans un premier temps, une formalisation de l'annotation puis le modèle des vecteurs conceptuels. Nous détaillerons ensuite la procédure d'annotation que nous proposons et qui repose à la fois sur les vecteurs conceptuels et sur les sources lexicales. Annotation sémantique, nommage de sens : définition Les systèmes actuels associent à chaque mot polysémique une annotation numériques, par exemple : "Le chat/I.2/ mange/1/ la souris/II.1/". L'usage que nous proposons diffère des précédents, nous souhaitons retrouver des associations d'idées dans la langue et les utiliser pour désigner un sens. Nous associons à chaque terme polysémique d'un texte un annoteur qui est lui même terme du lexique. Par exemple : Chaussé/porter/ de ses bottes/chaussure/ il revenait/déplacer/ vers la grange et apercevait/voir/ les bottes/amas/ de foin/paille/. Le lexicographe qui possède ainsi un annotateur et un terme sera alors capable d'identifier plus facilement le sens désigné. Il ne s'agit plus de donner une définition comme annotateur (Wilks, Stevenson, 1997) £ ¢ " ! ¤ § ¥ # $ £ ¤ § ¥ # % © & ' ¥ ¥ ) ( ¢ telle que : © ¥ 0 2 1 3 ¤ § ¥ 5 4 ! 6 8 7 9 2 1 3 ¤ @ 7 A 4 ! B ' C¨ E D ! ' ¥ G F H I 7 Ï Q P R § S ¥ ! I 7 C¨ T P Quel que soit le terme £ ¢ , on partitionne le lexique pour obtenir pour chaque sens ¤ ¥ U V £ un ensemble (non vide) d'annotateurs W ¥ . Cette fonction doit permettre de réassocier à tout couple 1 3 X ! £ 4 où X est un élément de Y ¥ le sens ¤ § ¥ correspondant. Cette fonction admet donc une bijection réciproque à c b : c b £ ¢ " ! © ¥ ) ( ¢ " ! 0 X d ¥ 1 3 X ! £ 4 I % © & ¤ e ¥ On souhaite ainsi insérer dans un texte des balises contenant un terme discréminant pour chaque terme polysémique désambiguïsé. La fonction d'annotation précédente propose pour un sens donné d'un terme un ensemble de candidats. Il importe donc d'évaluer les différents candidats et de les classer par ordre d'intéret tout en accordant une souplesse suivant l'utilisation qui sera faite de l'annotation (usage humain, interopérabilité, . . . ). Propriétés remarquables 1.2.1 Indépendance aux dictionnaires Dans le cas où on ne souhaite pas diffuser le dictionnaire source (droits d'auteur, volume trop important,. . . ) une annotation par des termes de la langue peut permettre au client de retrouver le bon sens sans la source. Si dans plusieurs dictionnaires un terme est associé à un autre, il est fortement probable de retrouver la même relation à l'aide d'autre sources. Différents annotateurs n'offriront pas la même indépendance aux sources, il est donc important d'évaluer cette propriété et de classer les annotateurs en conséquence. Une interface homme-machine L'annotation est un outil précieux pour le lexicographe qui supervise une désambigïsation. Elle met en évidence de façon simple le sens qu'il faut attribuer à un terme, sans devoir constament se référer à un dictionnaire donné et en assimiler la définition. Le coût cognitif est défini comme l'effort que doit faire l'agent humain pour transformer une perception, une information en une connaissance exploitable (Prince, 1996). Dans le cas de l'annotation sémantique à usage humain, il est déterminant de minimiser ce coût et pour cela de prendre en compte d'autre critères. On favorisera ainsi des candidats qui ont un usage proche en utilisant des notions de fréquence, de cooccurence terme/annotation, mais aussi des informations fournies par les dictionnaires comme la morphologie ou l'usage (contexte ou domaine, sens figuré ou soutenu, . . . ). Les vecteurs conceptuels Le modèle vectoriel a été introduit par (Salton, 1968) en recherche d'information. C'est à partir de (Chauché, 1990) que l'on a une formalisation de la projection de la notion linguistique de champs sémantiques dans un espace vectoriel. A partir d'un ensemble de notions élémentaires dont nous faisons l'hypothèse, les concepts, il est possibles de construire des vecteurs (dits conceptuels) et de les associer à des items lexicaux. Dans notre expérimentation sur le Français nous utilisons (Thésaurus Larousse, 1992) . Cette dernière mesure représente intuitivement une notion de distance thématique entre deux mots, elle respecte la symétrie, la réflexivité et l'inégalité triangulaire. Voici un exemple avec le terme amour où les termes les plus proches sont éprendre (0.17), Cupidon (0.25), Aphrodite (0.27), amitié (0.308), tendresse (0.3084). Les concepts les plus activés sont amour (11879), passion (4137), inimitié (3511), courtoisie (3348),. . . La construction de la base vectorielle est automatisée à partir de définitions issues de dictionnaires à usage humain (Schwab & al., 2002), . La procédure d'annotation Notre procédure d'annotation repose sur une utilisation mixte des ressources lexicales et des vecteurs conceptuels. Nous l'avons vu, nous souhaitons annoter avec un terme du lexique. Pour des raisons pratiques (coût calculatoire élevé), nous utilisons dans un premier temps les sources lexicales pour extraire les candidats. Une procédure de validation permet de retirer tous ceux qui ne satisfont pas aux conditions de bijectivité (cf. 1.1). Enfin, pour obtenir une pertinence et un coût cognitif optimaux, des évaluateurs ordonnent le résultat suivant divers critères (fréquence, cooccurence, morphologie, . . . ). Extraction de candidats On distingue plusieurs types d'extracteurs qui sont caractérisés par leur source et la méthode employée. Les premiers s'appliquent à un ensemble de dictionnaires traditionnels, analysant les différentes définitions et proposant tous les mots contenus comme candidats. Nous utilisons pour cela SYGMART 1 , un analyseur morphosyntaxique calculé à partir d'une phrase donnée un arbre morphosyntaxique donnée. On obtient alors des informations complête sur la morphologies des termes de la phrase ainsi que leur fonction. On filtre articles, pronoms, métalangage (famille de, du latin,...) qui n'apportent pas d'information sémantique à la définition. D'autre extracteurs ont une méthode identique mais s'appliquent à des sources comme une liste de synonymes (Ploux, Victorri, 1998) , concepts du thésaurus, de dictionnaires de cooccurence,... Enfin, les derniers extracteurs s'appliquent à des dictionnaires dont on a pu retirer une information structurée grâce à une analyse morphosyntaxique ou à un format semi-structuré comme XML. On peut alors retrouver des relations comme synonymie, antonymie, hyperonymie, règles d'usage, exemples caractéristiques,... Dans le cas des noms propres, on détermine qu'il s'agit d'une ville, d'un fleuve d'un pays précis, etc. Ces extracteurs travailleront ainsi sur tous les cas particuliers qu'on peut extraire des définitions et s'avèrent particulièrement efficaces sur les noms propres. Validation d'un candidat Après avoir extrait de nombreux candidats, il est nécessaire de les évaluer. Cependant, avant de classer des candidats, un filtre va valider ou non des candidats. Comme nous l'avons expliqué précédemment, nous souhaitons obtenir une fonction admettant une réciproque pour associer à un couple 1 3 X t ¥ ! £ 4 un sens ¤ e ¥ £ . L'objectif de la procédure de validation est simplement d'ôter tout candidat qui ne satisfait pas cette condition. Elle se formalise de la façon suivante : un annotateur Evaluation d'un candidat Une note d'extraction des candidats Durant la phase d'extraction des candidats, il est déjà indispensable de les évaluer suivant : (1) la source lexicale (différents dictionnaires n'offrent pas la même qualité de résultat), (2) le type d'extraction (les concepts les plus activés ou les domaines et usages (qui ne sont pas le plus souvant les meilleurs) et enfin (3) l'arbre morphosyntaxique fournit des informations comme la position et la fonction du terme dans la phrase qui sont particulièrement précieuses. En effet, les premiers termes d'une définition contiennent très souvent un excellent annotateur du fait qu'elles sont exprimées en genre et différence. SYGMART nous permet de tenir compte particulièrement du sujet ou du complément d'objet dans la définition et de résoudre les difficultés engendrées par l'apposition de compléments. La distance angulaire et l'annotation Pour assurer la reciprocité de l'annotation, il est indispensable que le candidat soit le plus proche possible du sens annoté tout en maximisant sa distance avec tout autre sens. La notion de distance angulaire permet d'évaluer les candidats potentiels grâce à trois mesures : La marge de désambiguïsation absolue. Elle représente l'intervalle dans lequel la fonction réciproque n'associe pas l'annotateur à un mauvais sens. Plus cette marge est importante, plus les probabilités sont bonnes de ne pas réassocier un mauvais sens dans une autre base lexicale. Soit X b l'annotateur de ¤ b et ¤ e C¨ T ¤ b le second sens le plus proche de X b . Alors la marge absolue est : (3), une erreur que ferait la procédure réciproque serait bien plus importante si elle associait (1) à (2) dans un texte que si elle associait (3). Cependant, on peut souhaiter dans le contexte où les sens (2) et (3) seraient très présents et le sens (1) absent utiliser l'annotation (1), l'objectif étant de discréminer au mieux les sens (2) et (3). £ Q Y d 6 e g f h U 1 i X b ! ¤ b 4 Ü k j ¢ 1 3 X b ! ¤ e 4 % ¢ 1 3 X b ! ¤ b 4 j Autres évaluateurs d'usage Pour selectionner un terme réellement associé, il est déterminant de tenir compte de l'usage de l'annotateur et de l'annoté. Pour cela, plusieurs évaluateurs sont présents dans notre procédure : (1) La fréquence d'usage de l'annotateur : supposons que mouche ¡ reçoive comme candidat mouche/drosophyle/ (pour le sens de l'insecte). Un annotateur très rare risque de ne pas être connu du client, qu'il soit humain ou automatique, un annotateur trop fréquent risque de ne pas faire sens (cuisine/faire/). De même, suivant l'utilisation, on peut souhaiter dans l'ordre de préférence un hyperonyme, un hyponyme ou un co-hyponyme. La fréquence est un indice : un terme bien moins fréquent qu'un autre a plus de chance d'être un hyponyme qu'un hyperonyme. (2) La catégorie grammaticale : le choix d'utiliser des termes de même nature grammaticale réduit le coût cognitif, technique qu'utilisent depuis longtemps les dictionnaires pour définir un terme ( (3) L'usage : deux utilisateurs différents n'auront pas les mêmes associations d'idées entre différents mots. Par exemple, le terme police ¡ peut signifier le contrat d'assurance ou l'autorité judiciaire. Dans ce deuxième sens, l'annotation agent ¡ ou poulet ¡ n'induira pas le même comportement chez le lecteur. Celui-ci risque de ne pas retrouver une association pourtant évidente chez un autre. La cooccurence permet de confirmer l'association d'idée entre deux termes. De plus, un étude en contexte et en usage offrira de meilleurs résultats. Un site personnel et un livre ne s'adresseront pas aux même lecteurs, tout comme un article de presse et un article scientifique. mais d'extraire un représentant. Par exemple, nous proposons d'annoter le terme botte ¡ par botte/paille/, botte/chaussure/, botte/escrime/ qui sont des formes plus intuitives et compréhensibles. On peut alors considérer que cette annotation est hors-source, elle fait sens sans source lexicale spécifique. Si le destinataire a une compétence linguistique, il lui sera possible de réassocier la botte et la chaussure, ou la botte et la réunion de végétaux.L'annotation correspond à une fonction bijective qui, à un terme et un sens, associe un couple (terme, annotation). Soit1.1 Définition formelle ¢ le dictionnaire, £ un terme du dictionnaire, ¤ ¦ ¥ un sens de £ tel que ¤ § ¥ © £ et ¥ un ensemble d'annotateurs pour ¤ ¥ et soit la fonction d'annotation : bâtiment de guerre à trois mâts ¡ et (3) Bâtiment d'escorte anti-sous-marin ¡ . Soient les deux candidats voilier possédant une marge de désambiguisation identique qui annotent (2). Voilier ¡ a pour sens le plus proche (1) tandis que guerre ¡ l'est avec (3). Alors on doit tenir compte de la distance entre les sens ambigus. Ainsi la distance entre (1) et (2) étant bien plus importante qu'entre (2) etLa marge de désambiguïsation relative. Prenons comme exemple deux candidats X b . Les trois sens de ce mot sont (1) Oiseau de mer ¡ , (2) ¡ et guerre ¡ déplanter¡ & enlever ¡ ; rimer ¡ & constituer une rime ¡ ; table ¡ & meuble ¡ ... ). . Leur marge de désambiguisation respectives sont :. La marge absolue favorise ainsi le deuxième candidat. Pourtant, le premier est bien mieux associé au sens annoté. La marge relative prend en compte la distance entre le candidat est le sens nommé de la façon suivante :Le risque de non-sens : Cette dernière mesure possède encore un dernier défaut : elle ne tient pas compte de la distance entre les deux sens les plus ambigus. Prenons l'exemple de frégate Les résultats actuels sont encourrageants, mais de nombreuses voies restent à explorer. Nous projetons ainsi dans un avenir proche d'étudier précisément les comportement de l'utilisateurs et d'expérimenter l'interfaçage entre différents systèmes automatiques dans un cadre multilingue. Enfin, cette analyse des associations d'idées va devenir une nouvelle source lexicale pour la base vectorielle. L'objectif est par cela d'améliorer la base de connaissance. L'annotation telle que nous venons de la présenter est un outil important dans le cadre de nos recherche. C'est une interface homme-machine permettant une évaluation rapide et efficace pour le superviseur d'un processus de désambiguïsation dans le cadre de la traduction, ou dans notre cas, une aide de l'analyse de définitions pour produire un vecteur conceptuel. Elle est, d'autre part, un nouvelle forme d'interfaçage entres de multiples systèmes. et de ce fait notre propre processus. phénomène de la double boucle (SchwabL'annotation telle que nous venons de la présenter est un outil important dans le cadre de nos recherche. C'est une interface homme-machine permettant une évaluation rapide et efficace pour le superviseur d'un processus de désambiguïsation dans le cadre de la traduction, ou dans notre cas, une aide de l'analyse de définitions pour produire un vecteur conceptuel. Elle est, d'autre part, un nouvelle forme d'interfaçage entres de multiples systèmes. Les résultats actuels sont en- courrageants, mais de nombreuses voies restent à explorer. Nous projetons ainsi dans un avenir proche d'étudier précisément les comportement de l'utilisateurs et d'expérimenter l'interfaçage entre différents systèmes automatiques dans un cadre multilingue. Enfin, cette analyse des asso- ciations d'idées va devenir une nouvelle source lexicale pour la base vectorielle. L'objectif est par cela d'améliorer la base de connaissance, et de ce fait notre propre processus (phénomène de la double boucle (Schwab, 2003). Prince pour l'aide précieuse qu'ils m'ont apportée Références Jacques Chauché, Détermination sémantique en analyse structurelle : une expérience basée sur une définition de distance. TAL Information. D Je Remercie, M Schwab, Lafourcade, 31Je remercie D. Schwab, M. Lafourcade et V. Prince pour l'aide précieuse qu'ils m'ont apportée Références Jacques Chauché, Détermination sémantique en analyse structurelle : une expérience basée sur une défi- nition de distance. TAL Information, 31/1, pp 17-24, 1990. . Hachette, Dictionnaire Hachette Encyclopédique, Hachette, 2.01.280477.2Hachette. Dictionnaire Hachette Encyclopédique. Hachette, ISBN 2.01.280477.2, version en ligne: http://www.encyclopedie-hachette.com Schwab Vecteurs conceptuels et structuration émergente de terminologies Revue TAL. M Lafourcade, V Prince, D , 43M. Lafourcade, V. Prince, D. Schwab Vecteurs conceptuels et structuration émergente de terminologies Revue TAL Volume 43 -n 1/2002, pages 43 à 72 Thésaurus Larousse -des idées aux mots, des mots aux idées. Larousse, 1992. S. Ploux, B. Victorri Construction d'espaces sémantiques à l'aide de dictionnaires de synonymes Revue TAL. Larousse, 39Numéro 1Larousse. Thésaurus Larousse -des idées aux mots, des mots aux idées. Larousse, 1992. S. Ploux, B. Victorri Construction d'espaces sémantiques à l'aide de dictionnaires de synonymes Revue TAL Volume 39, 1998, Numéro 1. V Prince, Vers une informatique cognitive dans les organisations -Le rôle central du langage Ed. Masson. V. Prince Vers une informatique cognitive dans les organisations -Le rôle central du langage Ed. Masson 1996. Automatic Information Organisation and Retrieval McGraw-Hill. G Salton, New YorkG. Salton Automatic Information Organisation and Retrieval McGraw-Hill, New York 1968. Prince Vers l'apprentissage automatique, pour et par les vecteurs conceptuels, de fonctions lexicales. D Schwab, M Lafourcade, V , NancyD. Schwab, M. Lafourcade, V. Prince Vers l'apprentissage automatique, pour et par les vecteurs con- ceptuels, de fonctions lexicales. -L'exemple de l'antonymie. TALN'2002 Nancy, Juin 2002. Schwab Société d'agents apprenants et sémantique lexicale : comment construire des vecteurs conceptuels à l'aide de la double boucle. Batz-sur-Mer; JuinSchwab Société d'agents apprenants et sémantique lexicale : comment construire des vecteurs con- ceptuels à l'aide de la double boucle. RECITAL 2003, Batz-sur-Mer, Juin 2003. Stevenson Sense tagging : semantic tagging with a lexicon Proceedings of the SIGLEX Workshop on Tagging Text with Lexical Semantics: Why, What and How?. M Wilks, Washington, D.CWilks, M. Stevenson Sense tagging : semantic tagging with a lexicon Proceedings of the SIGLEX Workshop on Tagging Text with Lexical Semantics: Why, What and How?, Washington, D.C. (1997).

A Bayesian Model for Learning SCFGs with Discontiguous Rules

We describe a nonparametric model and corresponding inference algorithm for learning Synchronous Context Free Grammar derivations for parallel text. The model employs a Pitman-Yor Process prior which uses a novel base distribution over synchronous grammar rules. Through both synthetic grammar induction and statistical machine translation experiments, we show that our model learns complex translational correspondences-including discontiguous, many-to-many alignments-and produces competitive translation results.Further, inference is efficient and we present results on significantly larger corpora than prior work.

A Bayesian Model for Learning SCFGs with Discontiguous Rules Association for Computational LinguisticsCopyright Association for Computational LinguisticsJuly 2012. 2012 Abby Levenberg Dept. of Computer Science School of Computer Science Dept. of Computer Science University of Oxford Carnegie Mellon Univeristy University of Oxford Chris Dyer cdyer@cs.cmu.edu Dept. of Computer Science School of Computer Science Dept. of Computer Science University of Oxford Carnegie Mellon Univeristy University of Oxford Phil Blunsom pblunsom@cs.ox.ac.uk Dept. of Computer Science School of Computer Science Dept. of Computer Science University of Oxford Carnegie Mellon Univeristy University of Oxford A Bayesian Model for Learning SCFGs with Discontiguous Rules Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language LearningJeju Island, KoreaAssociation for Computational LinguisticsJuly 2012. 2012 We describe a nonparametric model and corresponding inference algorithm for learning Synchronous Context Free Grammar derivations for parallel text. The model employs a Pitman-Yor Process prior which uses a novel base distribution over synchronous grammar rules. Through both synthetic grammar induction and statistical machine translation experiments, we show that our model learns complex translational correspondences-including discontiguous, many-to-many alignments-and produces competitive translation results.Further, inference is efficient and we present results on significantly larger corpora than prior work. Introduction In the twenty years since Brown et al. (1992) pioneered the first word-based statistical machine translation (SMT) models substantially more expressive models of translational equivalence have been developed. The prevalence of complex phrasal, discontiguous, and non-monotonic translation phenomena in real-world applications of machine translation has driven the development of hierarchical and syntactic models based on synchronous context-free grammars (SCFGs). Such models are now widely used in translation and represent the state-of-the-art in most language pairs (Galley et al., 2004;Chiang, 2007). However, while the models used for translation have evolved, the way in which they are learnt has not: naïve word-based models are still used to infer translational correspondences from parallel corpora. In this work we bring the learning of the minimal units of translation in step with the representational power of modern translation models. We present a nonparametric Bayesian model of translation based on SCFGs, and we use its posterior distribution to infer synchronous derivations for a parallel corpus using a novel Gibbs sampler. Our model is able to: 1) directly model many-to-many alignments, thereby capturing non-compositional and idiomatic translations; 2) align discontiguous phrases in both the source and target languages; 3) have no restrictions on the length of a rule, the number of nonterminal symbols per rule, or their configuration. Learning synchronous grammars is hard due to the high polynomial complexity of dynamic programming and the exponential space of possible rules. As such most prior work for learning SCFGs has relied on inference algorithms that were heuristically constrained or biased by word-based alignment models and small experiments (Wu, 1997;Zhang et al., 2008;Blunsom et al., 2009;Neubig et al., 2011). In contrast to these previous attempts, our SCFG model scales to large datasets (over 1.3M sentence pairs) without imposing restrictions on the form of the grammar rules or otherwise constraining the set of learnable rules (e.g., with a word alignment). We validate our sampler by demonstrating its ability to recover grammars used to generate synthetic datasets. We then evaluate our model by inducing word alignments for SMT experiments in several typologically diverse language pairs and across a range of corpora sizes. Our results attest to our model's ability to learn synchronous grammars encoding complex translation phenomena. Prior Work The goal of directly inducing phrasal translation models from parallel corpora has received a lot of attention in the NLP and SMT literature. Marcu and Wong (2002) presented an ambitious maximum likelihood model and EM inference algorithm for learning phrasal translation representations. The first issue this model faced was a massive parameter space and intractable inference. However a more subtle issue is that likelihood based models of this form suffer from a degenerate solution, resulting in the model learning whole sentences as phrases rather than minimal units of translation. DeNero et al. (2008) recognised this problem and proposed a nonparametric Bayesian prior for contiguous phrases. This had the dual benefits of biasing the model towards learning minimal translation units, and integrating out the parameters such that a much smaller set of statistics would suffice for inference with a Gibbs sampler. However this work fell short by not evaluating the model independently, instead only presenting results in which it was combined with a standard word-alignment initialisation, thus leaving open the question of its efficacy. The fact that flat phrasal models lack a structured approach to reordering has led many researchers to pursue SCFG induction instead (Wu, 1997;Cherry and Lin, 2007;Zhang et al., 2008;Blunsom et al., 2009). The asymptotic time complexity of the inside algorithm for even the simplest SCFG models is O(|s| 3 |t| 3 ), too high to be practical for most real translation data. A popular solution to this problem is to heuristically restrict inference to derivations which agree with an independent alignment model (Cherry and Lin, 2007;Zhang et al., 2008). However this may have the unintended effect of biasing the model back towards the initial alignments that they attempt to improve upon. More recently Neubig et al. (2011) reported a novel Bayesian model for phrasal alignment and extraction that was able to model phrases of multiple granularities via a synchronous Adaptor Grammar. However this model suffered from the common problem of intractable inference and results were presented for a very small number of samples from a heuristically pruned beam, making interpreting the results difficult. Blunsom et al. (2009) presented an approach similar to ours that implemented a Gibbs sampler for a nonparametric Bayesian model of ITG. While that work managed to scale to a non-trivially sized corpus, like other works it relied on a state-of-the-art word alignment model for initialisation. Our model goes further by allowing discontiguous phrasal translation units. Surprisingly, the freedom that this extra power affords allows the Gibbs sampler we propose to mix more quickly, allowing state-of-the-art results from a simple initialiser. Model We use a nonparametric generative model based on the 2-parameter Pitman-Yor process (PYP) (Pitman and Yor, 1997), a generalisation of the Dirichlet Process, which has been used for various NLP modeling tasks with state-of-the-art results such as language modeling, word segmentation, text compression and part of speech induction (Teh, 2006;Goldwater et al., 2006;Wood et al., 2011;Blunsom and Cohn, 2011). In this section we first provide a brief definition of the SCFG formalism and then describe our PYP prior for them. Synchronous Context-Free Grammar An synchronous context-free grammar (SCFG) is a 5-tuple Σ, ∆, V, S, R that generalises context-free grammar to generate strings concurrently in two languages (Lewis and Stearns, 1968). Σ is a finite set of source language terminal symbols, ∆ is a finite set of target language terminal symbols, V is a set of nonterminal symbols, with a designated start symbol S, and R is a set of synchronous rewrite rules. A string pair is generated by starting with the pair S 1 | S 1 and recursively applying rewrite rules of the form X → s, t, a where the left hand side (LHS) X is a nonterminal in V , s is a string in (Σ ∪ V ) * , t is a string in (∆ ∪ V ) * and a specifies a one-to-one mapping (bijection) between nonterminal symbols in s and t. The following are examples: 1 VP → schlage NP 1 NP 2 vor | suggest NP 2 to NP 1 NP → die Kommission | the commission In a probabilistic SCFG, rules are associated with probabilities such that the probabilities of all rewrites of a particular LHS category sum to 1. Translation with SCFGs is carried out by parsing the source language with the monolingual source language projection of the grammar (using standard monolingual parsing algorithms), which induces a parallel tree structure and translation in the target language (Chiang, 2007). Alignment or synchronous parsing is the process of concurrently parsing both the source and target sentences, uncovering the derivation or derivations that give rise to a string pair (Wu, 1997;Dyer, 2010). Our goal is to infer the most probable SCFG derivations that explain a corpus of parallel sentences, given a nonparametric prior over probabilistic SCFGs. In this work we will consider grammars with a single nonterminal category X. Pitman-Yor Process SCFG Before training we have no way of knowing how many rules will be needed in our grammar to adequately represent the data. By using the Pitman-Yor process as a prior on the parameters of a synchronous grammar we can formulate a model which prefers smaller numbers of rules that are reused often, thereby avoiding degenerate grammars consisting of large, overly specific rules. However, as the data being fit grows, the model can become more complex. The PYP is parameterised by a discount parameter d, a strength parameter θ, and the base distribution G 0 , which gives the prior probability of an event (in our case, events are rules) before any observations have occurred. The discount is subtracted from each positive rule count and dampens the rich get richer effect where frequent rules are given higher probability compared to infrequent ones. The strength parameter controls the variance, or concentration, about the base distribution. In our model, a draw from a PYP is a distribution over SCFG rules with a particular LHS (in fact, it is a distribution over all well-formed rules). From this distribution we can in turn draw individual rules: G X ∼ PY(d, θ, G 0 ), X → s, t, a ∼ G X . Although the PYP has no known analytical form, we can marginalise out the G X 's and reason about Step 1: Generate source side length. Step 2: Generate source side configuration of terminals (and non-terminal placeholders). Step 3: Generate target length. Step 4. Generate target side configuration of terminals (and non-terminal placeholders). Step 5. Generate the words. individual rules directly using the process described by Teh (2006). In this process, at time n a rule r n is generated by stochastically deciding whether to make another copy of a previously generated rule or to draw a new one from the base distribution, G 0 . Let ϕ = (ϕ 1 , ϕ 2 , . . .) be the sequence of draws from G 0 ; thus |ϕ| is the total number of draws from G 0 . A rule r n corresponds to a selection of a ϕ k . Let c k be a counter indicating the number of times ϕ k has been selected. In particular, we set r n to ϕ k with probability X < _ _ _ ||| ? > X < X1 _ X2 ||| ? > X < X1 _ X2 ||| _ _ _ > X < X1 _ X2 ||| _ X1 X2 > X < X1 你 X2 ||| you X1 X2 >c k − d θ + n , and increment c k , or with probability θ + d · |ϕ| θ + n , we draw a new rule from G 0 , append it to ϕ, and use it for r n . Base Distribution The base distribution G 0 for the PYP assigns probability to a rule based our belief about what constitutes a good rule independent of observing any of the data. We describe a novel generative process for all rules X → s, t, a that encodes these beliefs. We describe the generative process generally here in text, and readers may refer to the example in Figure 1. The process begins by generating the source length (total number of terminal and nonterminal symbols, written |s|) by drawing from a Poisson distribution with mean 1: |s| ∼ Poisson(1) . This assigns high probability to shorter rules, but arbitrarily long rules are possible with a low probability. Then, for every position in s, we decide whether it will contain a terminal or nonterminal symbol by repeated, independent draws from a Bernoulli distribution. Since we believe that shorter rules should be relatively more likely to contain terminal symbols than longer rules, we define the probability of a terminal symbol to be φ |s| where 0 < φ < 1 is a hyperparameter. s i ∼ Bernoulli(φ |s| ) ∀ i ∈ [1, |s|] . We next generate the length of the target side of the rule. Let # NT (s) denote the number of nonterminal symbols we generated in s, i.e., the arity of the rule. Our intuition here is that source and target lengths should be similar. However, to ensure that the rule is well-formed, t must contain exactly as many nonterminal symbols as the source does. We therefore draw the number of target terminal symbols from a Poisson whose mean is the number of terminal symbols in the source, plus a small constant λ 0 to ensure that it is greater than zero: |t| − # NT (s) ∼ Poisson (|s| − # NT (s) + λ 0 ) . We then determine whether each position in t is a terminal or nonterminal symbol by drawing uniformly from the bag of # NT (s) source nonterminals and |t| − # NT (s) terminal indicators, without replacement. At this point we have created a rule template which indicates how large the rule is, whether each position contains a terminal or nonterminal symbol, and the reordering of the source nonterminals a. To conclude the process we must select the terminal types from the source and target vocabularies. To do so, we use the following distribution: t)) first generates the source (target) terminals from uniform draws from the vocabulary, then generates the string in the other language according to IBM MODEL 1, marginalizing over the alignments (Brown et al., 1993). P terminals (s, t) = P M 1 ← (s, t) + P M 1 → (s, t) 2 where P M 1 ← (s, t) (P M 1 → (s, Gibbs Sampler In this section we introduce a Gibbs sampler that enables us to perform posterior inference given a corpus of sentence pairs. Our innovation is to represent the synchronous derivation of a sentence pair in a hierarchical 4-dimensional binary alignment grid, with elements z [s,t,u,v] ∈ {0, 1}. The settings of the grid variables completely determine the SCFG rules in the current derivation. A setting of a binary variable z [s,t,u,v] = 1 represents a constituent linking the source span [s, t] and the target span [u, v] in the current derivation; variables with a value of 0 indicate no link between spans [s, t] and [u, v]. 2 This relationship from our grid representation is illustrated in Figure 2a. Our Gibbs sampler operates over the space of all the random variables z [s,t,u,v] , resampling one at a time. Changes to a single variable imply that at most two additional rules must be generated, as illustrated in Figure 2b. The probability of choosing a binary setting of 0 or 1 for a variable is proportional to the probability of generating the two derivations under the model described in the previous section. Note that for a given sentence, most of the bispan variables must be set to 0 otherwise they would violate the strict nesting constraint required for valid SCFG derivations. We discuss below how to exploit this fact to limit the number of binary variables that must be resampled for each sentence. To be valid, a Gibbs sampler must be ergodic and satisfy detailed balance. Ergodicity requires that there is non-zero probability that any state in the sampler be reachable from any other state. Clearly our operator satisfies this since given any configuration of the alignment grid we can use the toggle operator to flatten the derivation to a single rule and then break it back down to reach any derivation. X [0,4,0,3] → X [0,1, Detailed balance requires that the probability of transitioning between two possible adjacent sampler states respects their joint probabilities in the stationary distribution. One way to ensure this is to make the order in which bispan variables are visited deterministic and independent of the variables' current settings. Then, the probability of the sampler targeting any bispan in the grid is equal regardless of the current configuration of the alignment grid. A naive instantiation of this strategy is to visit all |s| 2 |t| 2 bispans in some order. However, since we wish to be able to draw many samples, this is not computationally feasible. A much more efficient approach avoids resampling variables that would result in violations without visiting each of them individually. However, to ensure detailed balanced is maintained, the order that we resample bispans has to match the order we would sample them using any exhaustive approach. We achieve this by always checking a derivation top-down, from largest to smallest bispan. Under this ordering, whether or not a smaller bispan is visited will be independent of how the larger ones were resampled. Furthermore, the set of variables that may be resampled is fixed given this ordering. Therefore, the probability of sampling any possible bispan in the sentence pair is still uniform (ensuring detailed balance), while our sampler remains fast. Evaluation The preceding sections have introduced a model, and accompanying inference technique, designed to induce a posterior distribution over SCFG derivations containing discontiguous and phrasal translation rules. The evaluation that follows aims to determine our models ability to meet these design goals, and to do so in a range of translation scenarios. In order to validate both the model and the sampler's ability to learn an SCFG we first conduct a synthetic experiment in which the true grammar is known. Subsequently we conduct a series of experiments on real parallel corpora of increasing sizes to explore the empirical properties of our model. Synthetic Data Experiments Prior work on SCFG induction for SMT has validated modeling claims by reporting BLEU scores on real translation tasks. However, the combination of noisy data and the complexity of SMT pipelines conspire to obscure whether models actually achieve their design goals, normally stated in terms of an ability to induce SCFGs with particular properties. Here we include a small synthetic data experiment to clearly validate our models ability to learn an SCFG that includes discontiguous and phrasal translation rules with non-monotonic word order. Using the probabilistic SCFG shown in the top half of Table 1 we stochastically generated three thousand parallel sentence pairs as training data for our model. We then ran the Gibbs sampler for fifty iterations through the data. The bottom half of Table 1 lists the five rules with the highest marginal probability estimated by the sampler. Encouragingly our model was able to recover a grammar very close to the original. Even for such a small grammar the space of derivations is enormous and the task of recovering it from a data sample is non-trivial. The divergence from the true probabilities is due to the effect of the prior assigning shorter rules higher probability. With a larger data sample we would expect the influence of the prior in the posterior to diminish. Machine Translation Evaluation Ultimately the efficacy of a model for SCFG induction will be judged on its ability to underpin a stateof-the-art SMT system. Here we evaluate our model by applying it to learning word alignments for parallel corpora from which SMT systems are induced. We train models across a range of corpora sizes and for language pairs that exhibit the type of complex alignment phenomena that we are interested in modeling: Chinese → English (ZH-EN), Urdu → English (UR-EN) and German → English (DE-EN). Data and Baselines The UR-EN corpus is the smallest of those used in our experiments and is taken from the NIST 2009 translation evaluation. 3 The ZH-EN data is of a medium scale and comes from the FBIS corpus. The DE-EN pair constitutes the largest corpus and is taken from Europarl, the proceedings of the European Parliament (Koehn, 2003). Statistics for the data are shown in Table 2. We measure translation quality via the BLEU score (Papineni et al., 2001). All translation systems employ a Hiero translation model during decoding. GRAMMAR RULE TRUE PROBABILITY X→ X 1 a X 2 |X 1 X 2 1 0.2 X→ b c d | 3 2 0.2 X→ b d | 3 0.2 X→ d | 3 0.2 X→ c d | 3 1 0.2 SAMPLED RULE SAMPLED PROBABILITY X→ d | 3 0.25 X→ b d | 3 0.24 X→ c d | 3 1 0.24 X→ b c d | 3 2 0.211 X→ X 1 a X 2 |X 1 X 2 1 0.012 Baseline word alignments were obtained by running GIZA++ in both directions and symmetrizing using the grow-diag-final-and heuristic (Och and Ney, 2003;. Decoding was performed with the cdec decoder with the synchronous grammar extracted using the techniques developed by Lopez (2008). All translation systems include a 5-gram language model built from a five hundred million token subset LANGUAGE TEST Table 3: Results for the SMT experiments in BLEU . The baseline is produced using a full GIZA++ run. The MODEL 1 INITIALISATION column is from the initialisation alignments using MODEL 1 and no sampling. The PYP-SCFG columns show results for the 500th sample for both MODEL 1 and HMM initialisations. of all the English data made available for the NIST 2009 shared task (Graff, 2003). Experimental Setup To obtain the PYP-SCFG word alignments we ran the sampler for five hundred iterations for each of the language pairs and experimental conditions described below. We used the approach of Newman et al. (2007) to distribute the sampler across multiple threads. The strength θ and discount d hyperparameters of the Pitman-Yor Processes, and the terminal penalty φ (Section 3.3), were inferred using slice sampling (Neal, 2000). The Gibbs sampler requires an initial set of derivations from which to commence sampling. In our experiments we investigated both weak and a strong initialisations, the former based on word alignments from IBM Model 1 and the latter on alignments from an HMM model (Vogel et al., 1996). For decoding we used the word alignments implied by the derivations in the final sample to extract a Hiero grammar with the same standard set of relative frequency, length, and language model features used for the baseline. Weak Initialisation Our first translation experiments ascertain the degree to which our proposed Gibbs sampling inference algorithm is able to learn good synchronous derivations for the PYP-SCFG model. A number of prior works on alignment with Gibbs samplers have only evaluated models initialised with the more complex GIZA++ alignment models (Blunsom et al., 2009;DeNero et al., 2008), as a result it can be difficult to separate the performance of the sampler from that of the initialisation. In order to do this, we initialise the sampler PYP-SCFG Table 3. Additionally we build a translation system from the MODEL 1 alignment used to initialise the sampler without using using our PYP-SCFG model or sampling. BLEU scores are shown in the MODEL 1 INITIALISATION column of Table 3. Firstly it is clear MODEL 1 is indeed a weak initialiser as the resulting translation systems achieve uniformly low BLEU scores. In contrast, the models built from the output of the Gibbs sampler for the PYP-SCFG model achieve BLEU scores comparable to those of the MODEL 4 BASELINE. Thus the sampler has moved a good distance from its initialisation, and done so in a direction that results in better synchronous derivations. Strong Initialisation Given we have established that the sampler can produce state-of-the-art translation results from a weak initialisation, it is instructive to investigate whether initialising the model with a strong alignment system, the GIZA++ HMM (Vogel et al., 1996), leads to further improvements. Column HMM INIT. of Table 3 shows the results for initialising with the HMM word alignments and sampling for 500 iterations. Starting with a stronger initial sample results in both quicker mixing and better translation quality for the same number of sampling iterations. Table 4 compares the average lengths of the rules produced by the sampler with both the strong and weak initialisers. As the size of the training corpora increases (UR-EN → ZH-EN → DE-EN) we see that the average size of the rules produced by the weakly initialised sampler also increases, while that of the strongly initialised model stays relatively uniform. Initially both samplers start out with a large number of long rules and as the sampling progresses the rules are broken down into smaller, more generalisable, pieces. As such we conclude from these metrics that after five hundred samples the strongly initialised model has converged to sampling from a mode of the distribution while the weakly initialised model converges more slowly and on the longer corpora is still travelling towards a mode. This suggests that longer sampling runs, and Gibbs operators that make simultaneous updates to multiple parts of a derivation, would enable the weakly initialised model to obtain better translation results. Grammar Analysis The BLEU scores are informative as a measure of translation quality but we also explored some of the differences in the grammars obtained from the PYP-SCFG model compared to the standard approach. In Figures 3 and 4 we show some basic statistics of the grammars our model produces. From Figure 3 we see that the number of unique rules in the PYP-SCFG grammar decreases steadily as the sampler iterates through the data, so the model is finding an increasingly sparser distribution with fewer but better quality rules as sampling progresses. Note that the gradient of the curves appears to be a function of the size of the corpus and suggests that the model built from the large DE-EN corpus would benefit from a longer sampling run. Figure 4 shows the distribution of rules with a given arity as a percentage Table 2). X→ 底 | end of X→ 届 全 | ninth * X→ 运作 X | charter X X→ 信心 | confidence in X→ 中国 政府 X | the chinese government X X→ 都 是 | are X→ 新华社 北京 X | beijing , X * X→ 有关 部门 | departments concerned X→ 新华社 华盛顿 X | washington , X * X→ 鲍威尔 X 1 了 X 2 , | he X 1 X 2 , * Table 5: The five highest ZH-EN probability rules in the Hiero grammar built from the PYP-SCFG that are not in the baseline Hiero grammar (top), and the top five rules in the baseline Hiero grammar that are not in the PYP-SCFG grammar (bottom). An * indicates a bad translation rule. of the full grammar after the final sampling iteration. The model prior biases the results to shorter rules as the vast majority of the model probability mass is on rules with zero, one or two nonterminals. Tables 5 and 6 show the most probable rules in the Hiero translation system obtained using the PYP-SCFG alignments that are not present in the TM from the GIZA++ alignments and visa versa. For both language pairs, four of the top five rules in X→ yh | it is X→ zmyn | the earth X→ yhy X | the same X X→ X 1 nhyN X 2 gy | X 2 not be X 1 X→ X 1 gY kh X 2 | recommend that X 2 X 1 * X→ hwN gY | will X→ Gyr mlky | international * X→ X 1 *rAye kY X 2 | X 2 to X 1 sources * X→ nY X 1 nhyN kyA X 2 | did not X 1 X 2 * X→ xAtwn X 1 ky X 2 | woman X 2 the X 1 Table 6: Five of the top scoring rules in the UR-EN Hiero grammar from sampled PYP-SCFG alignments (top) versus the baseline UR-EN Hiero grammar rules not in the sampled grammar (bottom). An * indicates a bad translation rule. Conclusion and Further Work In this paper we have presented a nonparametric Bayesian model for learning SCFGs directly from parallel corpora. We have also introduced a novel Gibbs sampller that allows for efficient posterior inference. We show state-of-the-art results and learn complex translation phenomena, including discontiguous and many-to-many phrasal alignments, without applying any heuristic restrictions on the model to make learning tractable. Our evaluation shows that we can use a principled approach to induce SCFGs designed specifically to utilize the full power of grammar based SMT instead of relying on complex word alignment heuristics with inherent bias. Future work includes the obvious extension to learning SCFGs that contain multiple nonterminals instead of a single nonterminal grammar. We also expect that expanding our sampler beyond strict binary sampling may allow us to explore the space of hierarchical word alignments more quickly allowing for faster mixing. We expect with these extensions our model of grammar induction may further improve translation output. Figure 1 : 1Example generation of a synchronous grammar rule in our G 0 . example grid representation of a synchronous derivation. The SCFG rules (annotated with their bispans) that correspond to this setting of the grid are: Figure 2 : 2A single operation of the Gibbs sampler for a binary alignment grid. Figure 3 : 3Unique grammar rules for each language pair as a function of the number of samples. The number of rule types decreases monotonically as sampling continues. Rule counts are displayed by normalised corpus size (see Figure 4 : 4The percentage of rules with a given arity in the final grammar of the PYP-SCFG model. the PYP-SCFG grammar that are not in the heuristically extracted grammar are correct and minimal phrasal units of translation, whereas only two of the top probability rules in the GIZA++ grammar are of good translation quality. Table 1 : 1Manually created SCFG used to generate synthetic data, and the five most probable inferred rules by our model. ZH-EN NIST UR-EN NIST DE-EN EUROPARL TRAIN (SRC) 8.6M 1.2M 34M TRAIN (TRG) 9.5M 1.0M 36M DEV (SRC) 22K 18K 26K DEV (TRG) 27K 16K 28K Table 2 : 2Corpora statistics (in words). LANGUAGE PAIR MODEL 1 INIT. HMM INIT.UR-EN 1.93/2.08 1.45/1.58 ZH-EN 3.47/4.28 1.69/2.37 DE-EN 4.05/4.77 1.50/2.04 Table 4 : 4Average source/target rule lengths in the PYP-SCFG models after the 500th sample for the different initialisations. using just the MODEL 1 distribution used in the PYP-SCFG model's base distribution. We denote this a weak initialisation as no alignment models outside of those included in the PYP-SCFG model influence the resulting word alignments. The BLEU scores for translation systems built from the five hundredth sample are show in the WEAK M1 INIT. column of The nonterminal alignment a is indicated through subscripts on the nonterminals. Our grid representation is the synchronous generalisation of the well-known correspondence between CFG derivations and Boolean matrices; seeLee (2002) for an overview. http://www.itl.nist.gov/iad/mig/tests/ mt/2009/ AcknowledgementsThis work was supported by a grant from Google, Inc. and EPRSRC grant no. EP/I010858/1 (Levenberg and Blunsom), the U. S. Army Research Laboratory and U. S. Army Research Office under contract/grant no. W911NF-10-1-0533 (Dyer). A hierarchical pitmanyor process hmm for unsupervised part of speech induction. P Blunsom, T Cohn, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. the 49th Annual Meeting of the Association for Computational Linguistics: Human Language TechnologiesOregon, USAPortland. Association for Computational LinguisticsP. Blunsom and T. Cohn. 2011. A hierarchical pitman- yor process hmm for unsupervised part of speech induction. 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Book Reviews Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition Daniel Jurafsky (University of Colorado Boulder) James H Martin (University of Colorado Boulder) Book Reviews Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition Upper Saddle River, NJ: Prentice Hall (Prentice Hall series in artificial intelligence, edited by Stuart Russell and Peter Norvig), 2000, xxvi+934 pp; hardbound, ISBN 0-13-095069-6, $64.00 Reviewed by Virginia Teller Hunter College and the Graduate Center, The City University of New York Introduction Jurafsky and Martin's long-awaited text sets a new gold standard that will be difficult to surpass, as attested by the flurry of glowing reviews that accompanied its publication early this year. 1 In a nutshell, J&M have successfully integrated knowledge-based and statistical methods with linguistic foundations and computational models in a book that is at once balanced, comprehensive, and, above all, eminently readable. My review will focus primarily on the format and content of Speech and Language Processing (SLP) as well as its use in a course. Format The 21 chapters are grouped into an introduction followed by four parts starting at the level of Words (six chapters), then proceeding to Syntax (six chapters), Semantics (four chapters), and finally Pragmatics (four chapters). Several other writers contributed chapters, principally Andrew Kehler (Chapter 18, "Discourse"), Keith Vander Linden (Chapter 20, "Natural Language Generation") and Nigel Ward (Chapter 21, "Machine Translation"). Supplementary material in four appendices covers regular-expression operators, the Porter stemming algorithm, the CLAWS C5 and C7 tagsets, and the forward-backward algorithm. Instructional aids abound. Margin notes are used liberally, and a series of Methodology Boxes explains techniques for evaluating natural language systems, including such measures as perplexity for n-gram models, the kappa statistic for computing agreement, and precision and recall for parsing and information extraction and retrieval. So-called Key Concepts highlight important points, and each chapter ends with a summary, bibliographic and historical notes, and exercises. In addition there is an extensive bibliography (more than 50 pages) and index (over 30 pages). Pithy epigrams introducing each chapter and sprinkled throughout the text further enliven J&M's engaging writing style. Two of my favorites appear in Chapter 17 ("Word Sense Disambiguation and Information Retrieval"), which opens with Groucho Marx (Oh, are you from Wales? Do you know a fella named Jonah? He used to live in whales for a while.), and Chapter 19 ("Dialogue and Conversational Agents"), which features Abbott and Costello's version of Who's on First. Excerpts are also taken from poems (Frost), musicals (Gershwin, Gilbert and Sullivan), drama (Shakespeare), and literature (Bacon, Melville), and many leading figures in the field--Chomsky, Jespersen, and Jelinek, to name a few--are quoted as well. Content SLP delves into topics as diverse as articulatory phonetics and the Chomsky hierarchy and treats virtually every major application in the field. I can't think of an area that's not covered. Spelling and grammar correction, speech recognition, text-to-speech, tagging, context-free and probabilistic parsing, word sense disambiguation, dialogue and conversational agents, information extraction and retrieval, natural language generation, machine translation, and many more are all included. On the theoretical side, J&M discuss regular expressions, finite-state automata and transducers, first-order predicate calculus, grammar formalisms, and more. The material on each subject is presented in a logical and organized fashion. Theoretical underpinnings are explored in depth, computational modeling strategies are thoroughly motivated, and algorithms are clearly stated and illustrated with detailed examples. Even areas that are touched upon only lightly, such as word segmentation (Chapter 5) and computational approaches to metaphor and metonymy (Chapter 16), are mentioned with sufficient pointers to the literature that they can easily be pursued by interested readers. A particular strength of SLP is the way that central themes are woven into the fabric of specific applications. The noisy-channel model is introduced in Chapter 5 ("Probabilistic Models of Pronunciation and Spelling"), and the simplified version of Bayes's rule as the product of likelihood and prior probability is stated as a Key Concept (p. 149). J&M apply these notions to spelling correction and English pronunciation variation in Chapter 5. The noisy-channel model is then reformulated for speech recognition in Chapter 7, and the revised version of Bayes's rule appears again as a Key Concept (p. 239). Bayesian inference resurfaces briefly in Chapter 17 as the naive Bayes classifier supervised learning method for word sense disambiguation, and Bayes's rule is restated a final time in the context of statistical machine translation (Chapter 21). The dynamic programming paradigm traverses a similar course, beginning in Chapter 5 with the minimum edit-distance, forward, and Viterbi algorithms. The Viterbi algorithm is revisited in Chapter 7 and used, along with A* decoding, for speech recognition. Chapters 10 and 12 describe the Earley and CYK algorithms for contextfree and probabilistic context-free parsing, respectively, and Appendix D sketches the forward-backward (Baum-Welch) algorithm, a special case of the EM algorithm. This threading of themes throughout the text, rather than seeming disjointed, permits J&M to consider applications in their proper context (words, syntax, semantics, pragmatics), and they provide ample explicit links along the way. As is typical with the first printing of any new book, there are numerous typographical and other printing errors--some more serious than others--that readers Computational Linguistics Volume 26, Number 4 may find annoying. J&M have compiled a list of errata, currently about five pages, and posted it on the SLP web site. At this writing (July 2000), a second printing incorporating all of these corrections is expected shortly. Use in a Course In early 2000, I used SLP to teach an introductory graduate course to linguistics and computer science students at the CUNY Graduate Center; linguists outnumbered computer scientists two to one. The computational sophistication of the linguists varied from none to intermediate C++ programming skills, while the other students had minimal, if any, background in linguistics. Copies of the book arrived in the college bookstore just one day before the first class. During the course of the 14-week semester we covered 12 chapters, approximately one chapter per week and roughly three chapters from each of the book's four parts; the last two weeks were devoted to student presentations (see below). Homework assignments took two forms: chapter exercises and Web-based work. Whenever feasible, chapter exercises involving computation, such as computing minimum editdistance or using kappa to determine the agreement between the output of two taggers, were selected so that students with programming skills could obtain solutions by writing programs, while students without this ability could perform the same calculations by hand. The Web assignments allowed students to construct their own corpora and then evaluate the results of processing them using taggers, parsers, and machine translation. Other Web sites gave exposure to natural language generation and to lexical semantics via WordNet. Unlike other texts I have used for this course (see Teller 1995), no supplementary reading is required with SLP. For content, it's one-stop shopping. Perhaps the best indication of SLP's worth came from the research component of the course. Students picked their own topics and developed a project in five steps: annotated bibliography, abstracts of relevant literature, proposal, class presentation, and final written report. This approach gave the students an opportunity to tackle a variety of problems close to their own interests. Almost everyone chose to use corpusbased methods, which entailed building a corpus from Web sources and using it as primary data. Here's a sample of topics: the productivity of two similar Chinese affixes (zi and tou); English adjective usage for Japanese learners; word sense disambiguation in medical text; natural language information retrieval on the Web; unknown words in speech recognition; modifying Brill's tagger for e-mail. The students were uniformly enthusiastic about SLP as a text for the course, and they also credited it as a valuable starting point for their research. ! credit SLP for contributing to the great distance in knowledge that some members of the class were able to travel from the beginning to the end of the semester. In their Preface, J&M suggest four routes through SLP for courses with different foci: NLP (one quarter); NLP (one semester); Speech plus NLP (one semester); and computational linguistics (one quarter). Compared to Allen (1995), SLP conveys a more intuitive grasp of human language, offers superior coverage of statistical methods, and provides a more up-to-date treatment of the symbolic paradigm. Although SLP may not suffice for a course that concentrates on the stochastic approach alone, some instructors may nonetheless find J&M's tutorial level of exposition more suitable than either Charniak's (1993) terse account or Manning and Schiitze's (1999) monumental tome. I plan to use SLP for an advanced undergraduate computer science course at Hunter College in late 2000. Conclusion In the past decade there has been a sea change in the methodology of natural language computing and an explosion in the availability of applications, especially on the Web. Traditional labels such as computational linguistics and natural language processing no longer adequately describe the range of methodologies and applications in the field; today a better term is language technology. SLP is the first text to address the needs of the wide audience in this expanded arena. Readers familiar with linguistics will find an accessible introduction to computational modeling, methods, algorithms, and techniques. Those with a computer science background will gain insight into the linguistic foundations of the field. And people knowledgeable about speech processing can learn more about syntax, semantics, and discourse. The appeal of SLP is by no means limited to academia. Researchers, developers, and practitioners from any related discipline should all find SLP an ideal vehicle for becoming acquainted with the burgeoning field of language technology. See, for example, the book's page at amazon.tom or the book's home page at www.cs.colorado.edu/~martin/slp.html. James Allen, Natural Language Understanding. second editionAllen, James. 1995. Natural Language Understanding, second edition. . / Benjamin, Cummings, Redwood City, CABenjamin/Cummings, Redwood City, CA. Statistical Language Learning. Eugene Charniak, The MIT PressCambridge, MACharniak, Eugene. 1993. Statistical Language Learning. The MIT Press, Cambridge, MA. Christopher D Manning, Hinrich Schfitze, Foundations of Statistical Natural Language Processing. Cambridge, MAThe MIT PressManning, Christopher D. and Hinrich Schfitze. 1999. Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge, MA. Virginia Teller, Review of Natural Language Processing. second editionTeller, Virginia. 1995. Review of Natural Language Processing (second edition) by . James Allen, Computational Linguistics. 214James Allen. Computational Linguistics 21(4):604-606. In recent years her research has focused on machine translation and parameter-based models of first-language syntax acquisition. Teller's address is: Computer Science Department. Hunter College, CUNY, 695 Park Avenue; New York, NYVirginia Teller is Professor of Computer Science at Hunter College, City University of New York, and a member of the doctoral faculties in Linguistics and Computer Science at the CUNY Graduate Center. e-mail: teller@cs.hunter.cuny.eduVirginia Teller is Professor of Computer Science at Hunter College, City University of New York, and a member of the doctoral faculties in Linguistics and Computer Science at the CUNY Graduate Center. In recent years her research has focused on machine translation and parameter-based models of first-language syntax acquisition. Teller's address is: Computer Sci- ence Department, Hunter College, CUNY, 695 Park Avenue, New York, NY 10021; e-mail: teller@cs.hunter.cuny.edu.

DISCOURSE MODELS~ DIALOG MEMORIES~ AND USER MODELS

DISCOURSE MODELS~ DIALOG MEMORIES~ AND USER MODELS Katharina Morik Technical University Berlin West Germany DISCOURSE MODELS~ DIALOG MEMORIES~ AND USER MODELS INTRODUCTION In this paper, we discuss some terminological issues related to the notions of discourse models, dialog memories, and user models. It is not our goal to show how discourse modeling and user modeling should actually interact in a cooperative system, but to show how the notions of discourse model, dialog memory, and user model can be defined and related in order to prevent misunderstandings and confusion. We argue that dialog memory may be subsumed under user model, as well as under discourse model, but that the three concepts should not be identified. Several separating criteria are discussed. We conclude that discourse modeling and user modeling are two lines of research that are orthogonal to each other. DIALOG MEMORY AS PART OF USER MODEL A dialog memory can be viewed as part of a user model, namely the part that represents the dialog-dependent knowledge of the user (Morik 1984). Entries out of the dialog memory may cause entries in the user model, and entries of the user model may support the interpretation of an utterance, the interpretation then being stored in the dialog memory. However, in order to keep technical terms precise, user modeling on the one hand, and building and exploiting a dialog memory on the other hand should not be identified. This would lead to a reduction of what user modeling is about by disregarding all aspects other than dialog-dependent knowledge of the user as known to the system, while in fact there is some information that is to be covered by a user model and that may not be covered by a dialog memory. Let us think, for example, of a visit to the dentist's. The dentist will have some expectations concerning the client before the client said a word---even before he opened his mouth. This is due to the conversational setting, the roles of dentist and client. The same two persons meeting in another environment (e.g., at a political event, a horse race, or the opera) would not rely on the dentist-client expectations but on the information that then belongs to their roles. A user model contains explicit assumptions on the role of the user and the way a particular user plays it. The system exploits the user model systematically for playing its role more cooperatively by adopting to diverse users. To that end it uses rules which are parametrized according to the facets of the user. A user model is built up based on a "naive psychology", which forms a consistent image of the user. Schuster also states that the user model covers entities that do not belong into the dialog memory. In addition to the argument mentioned above (the dialog memory being the part of the user model that represents the dialog-dependent knowledge of the user), she points out that the dialog memory is used for building up parts of the user model and that both, user model and dialog memory, are used for generating an adequate answer. But if this were a valid argument for establishing a subsumption relation, we should also view the grammar as part of the user model, because the grammar is necessary for understanding and producing utterances. All the knowledge sources of a natural language system (hopefully) work together. We separate them conceptually not because of their independence, but because they contain different kinds of knowledge that contribute to the overall task in different ways. A dialog memory contains all beliefs that can be inferred with certainty from utterances, so that they belong to the mutual belief space. For example, the objects and their properties introduced in a dialog are typical entries in a dialog memory. Also, presuppositions that can be inferred from articles or question particles belong into the dialog memory. The linguistic rules that determine the inferences are valid and binding for all conversational settings. General rules establish mutual beliefs on the basis of utterances. The dialog memory is then used for, e.g., determining the appro-Copyright 1988 by the Association for Computational Linguistics. Permission to copy without fee all or part of this material is granted provided that the copies are not made for direct commercial advantage and the CL reference and this copyright notice are included on the first page. To copy otherwise, or to republish, requires a fee and/or specific permission. 0362-613X/88/0100o-o$03.00 priate description (definite/indefinite), anaphoric expression, or characterization. DIALOG MEMORY AS PART OF DISCOURSE MODEL Another notion that is very close to user model as well as to dialog memory is discourse model. Sometimes dialog memory and discourse model are treated as synonyms (e.g., Wahlster 1986). Given the above definition of dialog memories, however, there is a difference between the two notions. As opposed to Schuster, who defines a discourse model as "containing representations of entities, along with their properties and relations they participate in", which corresponds exactly to our dialog memory, I use discourse model according to the framework of Grosz and Sidner (1986), where a discourse model is the syntactic structure of a dialog. One part of it, though, could be identified with the dialog memory, namely the focus space stack. The overall discourse model additionally represents the structure of the dialog with the segments and their relations, which is not part of the user model. Decomposing a dialog into segments and establishing relations between them does not depend on a particular conversational setting. As is the case with dialog memories the overall discourse model, too, is built up by general linguistic rules that need not be parametrized according to a certain user. SEPARATING CRITERIA Previous attempts to separate user models from discourse models have used the short-time/long-time criterion, arguing that entries in the dialog memory can be forgotten after the end of the dialog, whereas entries in the user model are to be remembered. The same argument applies to dialog memories as part of the discourse model. The rationale of this argument is that anaphors are not applicable from one dialog to another and that the structure of a dialog is unlikely to be recalled as the syntactic structure of uttered sentences--just to mention these two phenomena. But does that mean that the entities with all their properties and relations as communicated in the dialog are forgotten? What would be the reason to talk to each other then? How could we learn from each other? Knowledge is to a great extent transferred via dialogs. Second, how could speech acts have social obligations as a consequence that may well hold for a long time? (Think of promises, for example!) Although the speaker may have forgotten the dialog, the hearer has--by very general conventions of language use--the right to insist on the speaker's commitments (Lewis 1975, Searle 1969, Wunderlich 1972. The synthesis of those seemingly conflicting observations is that the content of the dialog memory is integrated into the world knowledge. In other words, the conteilt of the focus space stack is partially incorporated into the world knowledge when it gets popped off the stack. So, the structure is lost, but the content is at least partly saved. Turniing things the other way around, why couldn't properties or character traits of a user be forgotten? What makes entries of a user model more stable? Think, for instance, of a post office clerk. Although he may adapt his behavior to the particular customer during the dialog, he normally forgets the information about her or him immediately after the dialog. As Rich (1979) pointed out, user models may be short term or long term. Thus short-time/long-time or forgettable/unforgettable is no criterion for dividing user models from dialog memories or discourse models. Another criterion could be, whether the knowledge is used for generating the linguistic form (how to say something) or for establishing the content of a system's utterance (what to say). Clearly, dialog memory and overall discourse model deal with the linguistic structure of dialogs, e.g., the reference resolution and the appropriate verbalization of concepts. The user model, on the other hand, also covers information that directs the selection of what to utter. The user's level of expertise determines the particularity of a system utterance, the realization of notes of caution, and the word choice, for instance. The user's wants establish the context of the user utterances and guide the system's problem solving, thus keeping the system behavior directed towards the user goals. This distinction, however, is not clear cut either, for two reasons. First, the line between what to say and how to say it is rather fuzzy. Referencing a concept, for example, also involves choosing the appropriate attributes for characterization--and this is naturally a matter of what to say. Second, this criterion would exclude work as presented by Lehman and Carbonell (1988) from the area of user modeling. There, linguistic rules are specialized for a particular user in a particular conversational setting. This is clearly not a matter of the dialog memory, but of the user model, although it is concerned with the linguistic form. Thus the form/ content distinction does not separate user models from dialog memories and discourse models, either. The difficulty to find criteria separating between discourse models and user models indicates a case of cross-classification. The criteria, namely what is specific to a user and what concerns dialog structure naturally cross. Dialog memory falls into both categories. On the one hand, from what the user utters his beliefs, his level of expertise in a certain domain, his wants, and his language style can be inferred. This knowledge can be used by all the system components: tuning syntactic analysis, resolving reference, determining the input speech act, disambiguating the input, selecting relevant information, organizing the text to be outputted (Paris 1988), choosing the appropriate words, referencing, topicalizing, etc. In order to do so, all the components must include procedures that are parametrized according to the (particular) user model. Currently, the interactive systems do not put all the user facets to good use in all their components. This is not due to principled limits, however, but rather to a shortcoming in the state of the art. On the other hand, the user's utterances can also be analyzed from another viewpoint, namely incorporating them into a coherent discourse model as described by, e.g., Grosz and Sidner (1986). Also, this model can be used during all processing steps from understanding to generating. Both user models and discourse models are built up (at least partially) from the user utterances. Both contribute to a cooperative system behavior. But they do so from different viewpoints with different aims. Adopting to a particular user on the one hand and achieving a coherent, well-formed dialog on the other hand are two aims for a cooperative system which are orthogonal to each other. The terms user model and discourse model denote different aspects of a system. Thus although the notions are intensionally different, the extension of their respective definitions may overlap. Katharine MorikDiscourse Models, Dialog Memories, and User Models Computational Linguistics, Volume 14, Number 3, September 1988 Attention, Intentions, and the Structure of Discourse. B Grosz, C Sidner, In Computational Linguistics. 12Grosz, B. and Sidner, C. 1986 Attention, Intentions, and the Structure of Discourse. In Computational Linguistics 12: 175-204. Learning the User's Language: A Step Towards Automated Creation of User Models. J F Lehman, J G Carbonell, User Models in Dialog Systems. Kobsa, A. and Wahlster, W.Berlin--New YorkSpringer-VerlagLehman, J. F. and Carbonell, J. G. 1988 Learning the User's Lan- guage: A Step Towards Automated Creation of User Models. In Kobsa, A. and Wahlster, W. (eds.), User Models in Dialog Systems. Springer-Verlag, Berlin--New York. Languages and Language. D Lewis, Language, Mind and Knowledge, Minnesota Studies in the Philosophy of Science 7. Gunderson, K.Minneapolis, MNUniversity of Minnesota PressLewis, D. 1975 Languages and Language. In Gunderson, K. (ed.), Language, Mind and Knowledge, Minnesota Studies in the Phi- losophy of Science 7. University of Minnesota Press, Minneapolis, MN. Partnermodellierung und Interessenprofile bei Dialogsystemen der Kfinstlichen Intelligenz. K Morik, Probleme des (Text-) Verstehens: Ansdtze der Kiinstlichen lntelligenz. Niemeyer. Rollinger, C. R.Tfibingen, W. GermanyMorik, K. 1984 Partnermodellierung und Interessenprofile bei Dia- logsystemen der Kfinstlichen Intelligenz. In Rollinger, C. R. (ed.), Probleme des (Text-) Verstehens: Ansdtze der Kiinstlichen lntel- ligenz. Niemeyer, Tfibingen, W. Germany. Tailoring Object Descriptions to a User's Level of Expertise. C L Paris, Kobsa, A. and Wahlster, W.Springer-VerlagBerlin--New YorkUser Models in Dialog SystemsParis, C. L. 1988 Tailoring Object Descriptions to a User's Level of Expertise. In Kobsa, A. and Wahlster, W. (eds.), User Models in Dialog Systems. Springer-Verlag, Berlin--New York. Building and Exploiting User Models. E Rich, Pittsburgh, PADepartment of Computer Science, Carnegie-Mellon UniversityPh.D. thesisRich, E. 1979 Building and Exploiting User Models. Ph.D. thesis, Department of Computer Science, Carnegie-Mellon University, Pittsburgh, PA. J R Searle, Speech Acts. Cambridge, EnglandCambridge University PressSearle, J. R. 1969 Speech Acts. 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TwitterHawk: A Feature Bucket Approach to Sentiment Analysis

This paper describes TwitterHawk, a system for sentiment analysis of tweets which participated in the SemEval-2015 Task 10, Subtasks A through D. The system performed competitively, most notably placing 1 st in topicbased sentiment classification (Subtask C) and ranking 4 th out of 40 in identifying the sentiment of sarcastic tweets. Our submissions in all four subtasks used a supervised learning approach to perform three-way classification to assign positive, negative, or neutral labels. Our system development efforts focused on text pre-processing and feature engineering, with a particular focus on handling negation, integrating sentiment lexicons, parsing hashtags, and handling expressive word modifications and emoticons. Two separate classifiers were developed for phrase-level and tweetlevel sentiment classification. Our success in aforementioned tasks came in part from leveraging the Subtask B data and building a single tweet-level classifier for Subtasks B, C and D.

TwitterHawk: A Feature Bucket Approach to Sentiment Analysis Association for Computational LinguisticsCopyright Association for Computational LinguisticsSemEval 2015. June 4-5, 2015. 2015 William Boag wboag@cs.uml.edu Dept. of Computer Science University of Massachusetts Lowell 198 Riverside St01854LowellMAUSA Peter Potash ppotash@cs.uml.edu Dept. of Computer Science University of Massachusetts Lowell 198 Riverside St01854LowellMAUSA Anna Rumshisky Dept. of Computer Science University of Massachusetts Lowell 198 Riverside St01854LowellMAUSA TwitterHawk: A Feature Bucket Approach to Sentiment Analysis Proceedings of the 9th International Workshop on Semantic Evaluation the 9th International Workshop on Semantic EvaluationDenver, ColoradoAssociation for Computational LinguisticsSemEval 2015. June 4-5, 2015. 2015 This paper describes TwitterHawk, a system for sentiment analysis of tweets which participated in the SemEval-2015 Task 10, Subtasks A through D. The system performed competitively, most notably placing 1 st in topicbased sentiment classification (Subtask C) and ranking 4 th out of 40 in identifying the sentiment of sarcastic tweets. Our submissions in all four subtasks used a supervised learning approach to perform three-way classification to assign positive, negative, or neutral labels. Our system development efforts focused on text pre-processing and feature engineering, with a particular focus on handling negation, integrating sentiment lexicons, parsing hashtags, and handling expressive word modifications and emoticons. Two separate classifiers were developed for phrase-level and tweetlevel sentiment classification. Our success in aforementioned tasks came in part from leveraging the Subtask B data and building a single tweet-level classifier for Subtasks B, C and D. Introduction In recent years, microblogging has developed into a resource for quickly and easily gathering data about how people feel about different topics. Sites such as Twitter allow for real-time communication of sentiment, thus providing unprecedented insight into how well-received products, events, and people are in the public's eye. But working with this new genre is challenging. Twitter imposes a 140-character limit on messages, which causes users to use novel abbreviations and often disregard standard sentence structures. For the past three years, the International Workshop on Semantic Evaluation (SemEval) has been hosting a task dedicated to sentiment analysis of Twitter data. This year, our team participated in four subtasks of the challenge: Contextual Polarity Disambiguation (phrase-level), B: Message Polarity Classification (tweet-level), C: Topic-Based Message Polarity Classification (topic-based), and D: Detecting Trends Towards a Topic (trending sentiment). For a more thorough description of the tasks, see Rosenthal et al. (2015). Our system placed 1 st out of 7 submissions for topic-based sentiment prediction (Subtask C), 3 rd out of 6 submissions for detecting trends toward a topic (Subtask D), 10 th out of 40 submissions for tweet-level sentiment prediction (Subtask B), and 5 th out of 11 for phrase-level prediction (Subtask A). Our system also ranked 4 th out of 40 submissions in identifying the sentiment of sarcastic tweets. Most systems that participated in this task over the past two years have relied on basic machine learning classifiers with a strong focus on developing robust and comprehensive feature set. The top system for Subtask A in both 2013 and 2014 from NRC Canada (Mohammad et al., 2013;Zhu et al., 2014) used a simple linear SVM while putting great effort into creating and incorporating sentiment lexicons as well as carefully handling negation contexts. Other teams addressed imbalances in data distributions, but still mainly focused on feature engineering, including an improved spelling correction, POS tagging, and word sense disambiguation (Miura et al., 2014). The second place submission for the 2014 Task B competition also used a neural network setup to learn sentiment-specific word embedding features along with state-of-the-art hand-crafted features (Tang et al., 2014). Our goal in developing TwitterHawk was to build on the success of feature-driven approaches established as state-of-the-art in the two previous years of SemEval Twitter Sentiment Analysis competitions. We therefore focused on identifying and incorporating the strongest features used by the best systems, most notably, sentiment lexicons that showed good performance in ablation studies. We also performed multiple rounds of pre-processing which included tokenization, spelling correction, hashtag segmentation, wordshape replacement of URLs, as well as handling negated contexts. Our main insight for Task C involved leveraging additional training data, since the provided training data was quite small (489 examples between training and dev). Although not annotated with respect to a particular topic, we found that message-level sentiment data (Subtask B) generalized better to topic-level sentiment tracking than span-level data (Subtask A). We therefore used Subtask B data to train a more robust model for topic-level sentiment detection. The rest of this paper is organized as follows. In Section 2, we discuss text preprocessing and normalization, describe the two classifiers we created for different subtasks, and present the features used by each model. We report system results in Section 3, and discuss system performance and future directions in Section 4. System Description We built a system to compete in four subtasks of Se-mEval Task 10 (Rosenthal et al., 2015). Subtasks A-C were concerned with classification of Twitter data as either positive, negative, or neutral. Subtask A involved phrase-level (usually 1-4 tokens) sentiment analysis. Subtask B dealt with classification of the entire tweet. Subtask C involved classifying a tweet's sentiment towards a given topic. Subtask D summarized the results of Subtask C by analyzing the sentiment expressed towards a topic by a group of tweets (as opposed to the single tweet classification for Subtask C). We trained two classifiers one for phrase-level classification and one for tweet-level sentiment classification. We use the phrase-level classifier for Sub-task A and we use the tweet-level classifier for Subtasks B and C. Subtask D did not require a separate classifier since it effectively just summarized the output of Subtask C. We experimented to determine whether data from Subtasks A or B generalized for C, and we found that the Subtask B model performed best at predicting for Subtask C. Preprocessing and Normalization Prior to feature extraction, we perform several preprocessing steps, including tokenization, spell correction, hashtag segmentation, and normalization. Tokenization and POS-tagging Standard word tokenizers are trained on datasets from the Wall Street Journal, and consequently do not perform well on Twitter data. Some of these issues come from shorter and ill-formed sentences, unintentional misspellings, creative use of language, and abbreviations. We use ARK Tweet NLP toolkit for natural language processing in social media (Owoputi et al., 2013;Gimpel et al., 2011) for tokenization and partof-speech tagging. An additional tokenization pass is used to split compound words that may have been mis-tokenized. This includes splitting hyphenated phrases such as 'first-place' or punctuation that was not detached from its leading text such as 'horay!!!'. Spell Correction Twitter's informal nature and limited character space often cause tweets to contain spelling errors and abbreviations. To address this issue, we developed a spell correction module that corrects errors and expands abbreviations. Spell correction is performed in two passes. The first pass identifies the words with alternative spellings common in social media text. The second pass uses a general-purpose spell correction package from PyEnchant library. 1 If a word w is misspelled, we check if it is one of four special forms we define: 1. non-prose -w is hashtag, URL, user mention, number, emoticon, or proper noun. 2. abbreviation -w is in our custom hand-built list that contains abbreviations as well as some common misspellings. 3. elongated word -w is an elongated word, such as 'heyyyy'. We define 'elongated' as repeating the same character 3 or more times in a row. 4. colloquial -w matches a regex for identifying common online phrases such as 'haha' or 'lol'. We use a regex rather than a closed list for elongated phrases where more than one character is repeated in order. This allows, for 'haha' and 'hahaha', for example, to be normalized to the the same form. Non-prose forms are handled in the tweet normalization phase (see sec 2.1). For abbreviations, we look up the expanded form in our hand-crafted list. For elongated words, we reduce all elongated substrings so that the substring's characters only occur twice. For example, this cuts both 'heeeeyyyy' and 'heeeyyyyyyyyy' down to 'heeyy'. Finally, colloquials are normalized to the shortened form (e.g., 'hahaha' becomes 'haha'). If w is not a special form, we feed it into PyEnchant library's candidate generation tool. We then filter out all candidates whose edit distance is greater than 2, and select the top candidate from PyEnchant. Hashtag Segmentation Hashtags are often used in tweets to summarize the key ideas of the message. For instance, consider the text: We're going bowling #WeLoveBowling. Although the text "We're going bowling" does not carry any sentiment on its own, the positive sentiment of the message is expressed by the hashtag. Similarly to spell correction, we define a general algorithm for hashtag segmentation, as well as several special cases. If hashtag h is not a special form, we reduce all characters to lowercase and then use a greedy segmentation algorithm which scans the hashtag from left to right, identifying the longest matching dictionary word. We split off the first word and repeat the process until the entire string is scanned. The algorithm does not backtrack at a dead end, but rather removes the leading character and continues. We use a trie structure to insure the efficiency of longest-prefix queries. We identify three special cases for a hashtag h: 1. manually segmented -h is in our custom hand-built list of hashtags not handled correctly by the general algorithm; 2. acronym -h is all capitals; 3. camel case -h is written in CamelCase, checked with a regex. For hashtags that are in the manually segmented list, we use the segmentation that we identified as correct. If h is an acronym, we do not segment it. Finally, for CamelCase, we treat the capitalization as indicating the segment boundaries. Normalization and Negation During the normalization phrase, all tokens are lowercased. Next, we replace URLs, user mentions, and numbers with generic URL, USER, and NUMBER tokens, respectively. The remaining tokens are stemmed using NLTKs Snowball stemmer (Bird et al., 2009). We also process negation contexts following the strategy used by Pang et al. (2002). We define a negation context to be a text span that begins with a negation word (such as 'no') and ends with a punctuation mark, hashtag, user mention, or URL. The suffix neg is appended to all words inside of a negation context. We use the list of negation words from Potts (2011). Machine Learning For the phrase-level sentiment classification, we trained a linear Support Vector Machine (SVM) using scikit-learn's LinearSVC (Pedregosa et al., 2011) on the Subtask A training data, which contained 4832 positive examples, 2549 negative, and 384 neutral. The regularization parameter was set to C=0.05, using a grid search over the development data (648 positive, 430 negative, 57 neutral). To account for the imbalance of label distributions, we used sklearn's 'auto' class weight adjustment which applies a weight inversely proportional to a given class's frequency in the training data to the numeric prediction of each class label. The tweet-level model was trained using scikitlearn's SGDClassifier with the hinge loss function and a learning rate of 0.001. The main difference between the learning algorithms of our classifiers was the regularization term of the loss function. While the phrase-level classifier uses the default SVM regularization, the tweet-level classifier uses an 'elasticnet' penalty with l1 ratio of .85. These parameter values were chosen following Gunther (2014) from last year's SemEval and verified in cross validation. We also used the 'auto' class weight for this task because the training label distribution was 3640 positive, 1458 negative, and 4586 neutral. We used scikit-learn's norm mat function to normalize the data matrix so that each column vector is normalized to unit length. Features Our system used two kinds of features: basic text features and lexicon features. We describe the two feature classes below. There was a substantial overlap between the features used for the phrase-level classifier and those used for the tweet-level classifier, with some additional features used at the phrase level. Basic Text Features Basic text features include the features derived from the text representation, including token-level unigram features, hashtag segmentation, character-level analysis, and wordshape normalization. For a given text span, basic text features included • presence or absence of: raw bag-of-words (BOW) unigrams, normalized/stemmed BOW unigrams, stemmed segmented hashtag BOW, user mentions, URLs, hashtags; • number of question marks and number of exclamation points; • number of positive, negative, and neutral emoticons; emoticons were extracted from the training data and manually tagged as positive, negative or neutral; 2 • whether the text span contained an elongated word (see Section 2.1, special form 3). The above features were derived from the annotated text span in both phrase-level and tweet-level analysis. For the phrase-level analysis, these were supplemented with the following: • normalized BOW unigram features derived from 3 tokens preceding the target phrase; • normalized BOW unigram features derived from 3 tokens following the target phrase; • length 2, 3, and 4 character prefixes and suffixes for each token in the target phrase; • whether the phrase was in all caps; • whether phrase contained only stop words; • whether a phrase contained only punctuation; 2 http://text-machine.cs.uml.edu/twitterhawk/emoticons.txt • whether the phrase contained a word whose length is eight or more; • whether the phrase contained an elongated word (cf. Section 2.1). There were a few other differences in the way each classifier handled some of the features. The phraselevel classifier changed the feature value from 1 to 2 for elongated unigrams. In the tweet-level classifier, we ignored unigrams with proper noun and preposition part-of-speech tags. Negation contexts were also handled differently. For the phrase-level classifier, a negated word was treated as a separate feature, whereas for the tweet-level classifier, negation changed the feature value from 1 to -1. Lexicon Features We used several Twitterspecific and general-purpose lexicons. The lexicons fell into one of two categories: those that provided a numeric score (usually, -5 to 5) score and those that sorted phrases into categories. For a given lexicon, categories could correspond to a particular emotion, to a strong or weak positive or negative sentiment, or to automatically derived word clusters. We used the features derived from the following lexicons: AFINN (Nielsen, 2011), Opinion Lexicon (Hu and Liu, 2004), Brown Clusters (Gimpel et al., 2011) son et al., 2005), and General Inquirer (Stone et al., 1966). Features are derived separately for each lexicon. General Inquirer and Hashtag Emotion were excluded from the tweet-level analysis since they did not improve system performance in cross-validation. We also experimented with features derived from WordNet (Fellbaum, 1998), but these failed to improve performance for either task in ablation studies. See Section 3.1 for ablation results. The features for the lexicons that provided a numeric score included: • the average sentiment score for the text span; • the total number of positively scored words in the span; • the maximum score (or zero if no words had a sentiment score); • the score of the last positively scored word; • three most influential (most positive or most negative) scores for the text span; this was only used by the phrase-level system. The features derived from lexicons that provided categories for words and phrases included the number of words that belonged to each category. For phrase-level analysis, the text span used for these features was the target phrase itself. For the tweet-level analysis, the text span covered the whole tweet. Table 1 shows which lexicons we used when building each classifier. Results In this section, we describe the experiments we conducted during system development, as well as the official SemEval Task 10 results. The scores reported throughout this section are calculated as the average of the positive and negative class F-measure (Nakov et al., 2013); the neutral label classification does not directly affect the score. System Development Experiments Both phrase-level and tweet-level systems were tuned in 10-fold cross-validation using the 2013 training, dev, and test data (Nakov et al., 2013). We used fixed data folds in order to compare different runs. Feature ablation studies, parameter tuning, and comparison of different pre-processing steps were performed using this setup. We conducted ablation studies for lexicon features using tweet-level evaluation. Table 2 shows ablation results obtained in 10-fold cross-validation. The figures are bolded if withholding the features derived from a given lexicon produced a higher score. Note that these experiments were conducted using a Linear SVM classifier with a limited subset of basic text features. Our best cross-validation results using the configuration described in sections 2.2 and 2.3 above were 87.12 average F-measure for phrase-level analysis (Subtask A), and 68.50 for tweet-level analysis (Subtask B). For topic-level sentiment detection in Subtask C, we investigated three different approaches: (1) using our phrase-level classifier "as is", (2) training our phrase level classifier only on phrases that resembled topics 3 , and (3) using our tweet-level classifier "as is". We found that our phrase-level classifiers did not perform well (F-scores in the 35-38 range), which could be explained by the fact that the Subtask A data was annotated so that the target phrases actually carried sentiment (e.g., the phrase "good luck"), whereas the Subtask C assumption was that the topic itself had no sentiment and that the topics context determined the expressed sentiment. For example, in the tweet "Gotta go see Flight tomorrow Denzel is the greatest actor ever", positive sentiment is carried by the phrase "the greatest actor ever", rather than the token "Denzel" (corresponding to the topic). It is therefore not surprising that our tweetlevel classifier achieved an F-score of 54.90, since tweet-level analysis is better able to capture longrange dependencies between sentiment-carrying expressions and the target topic. Consequently, we used the tweet-level classifier in our submission for Subtask C. Official Results Our official results for phrase-level and tweet-level tasks on the 2014 progress tests are given in Table 3. The models were trained on the 2013 training data. In the official 2015 ranking, our system performed competitively in each task. For subtask A (phraselevel), we placed 5 th with an F-score of 82.32, compared to the winning teams F-score of 84.79. For subtask B, we placed 10 th out of 40 submissions, with an F-score of 61.99, compared to the top team's 64.84. Our classifier for Subtask C won 1 st place with an F-score of 50.51, leading the second place entry of 45.48 by over 5 points. Finally, for Subtask D, we came in 3 rd place, with an average absolute difference of .214 on a 0 to 1 regression, as compared to the gold standard (Rosenthal et al., 2015). Our system also ranked 4 th out of 40 submissions in identifying the message-level sentiment of sarcastic tweets in 2014 data, with an F-score of 56.02, as compared to the winning team's F-score of 59.11. Discussion Consistent with previous years' results, our system performed better on phrase-level data than on tweetlevel data. We believe this is largely due to the skewed class distributions, as the majority baselines for Subtask A are much higher, and there are very few neutral labels. This is not the case for Subtask B, where the neutral labels outnumber positive labels. Also, the phrase-level text likely carries clearer sentiment, while the tweet-level analysis has to deal with conflicting sentiments across a message. Note that hashtag segmentation strategy can be improved by using a language model to predict which segmentations are more likely, as well as evaluating the hashtag's distributional similarity to the rest of the tweet. A language model could also be used to improve the spell correction. Our system's large margin of success at detecting topic-directed sentiment in Subtask C (over 5 points in F-score better than the 2 nd place team) likely comes from the fact that we leverage the large training data of Subtask B and the tweet-level model is able to capture long-range dependencies between sentiment-carrying expressions and the target topic. We found that the most influential features for detecting sarcasm were normalized BOW unigrams, lexicon-based features, and unigrams from hashtag segmentation. Not surprisingly, lexicon features improved performance for all genres, including SMS, LiveJournal, and non-sarcastic tweets (see rows 2 and 4 in Table 4). The same was true of spelling correction (as shown in Table 4, row 1). Hashtag-based features, on the other hand, only yielded large improvements for the sarcastic tweets, as shown in the gain achieved by adding hashtag features to the normalized BOW unigrams (see rows 2 and 3 in Table 4). Note that the 6.24 point gain is only observed for sarcasm data; other genres showed the average improvement of about 0.67. We believe that hashtags were so effective at predicting sentiment for sarcasm, because sarcastic tweets facetiously emulate literal tweets at first but then express their true sentiment at the end by using a hashtag, e.g. "On the bright side we have school today... Tomorrow and the day after ! #killmenow". , Hashtag Emotion (Mohammad, 2012), Sen-timent140 (Mohammad et al., 2013), Hashtag Sentiment (Mohammad et al., 2013), Subjectivity (Wil Table 1 : 1Which lexicons we used for each classifier.Opinion Hashtag Sentiment140 Subjectivity AFINN Hashtag Brown General Sentiment Emotion Clusters Inquirer Phrase-level Tweet-level Withholding F-score -(Full System) 63.76 Opinion Lexicon 63.70 Hashtag Sentiment 63.49 Sentiment140 63.22 Hashtag Emotion (HE) 63.77 Brown Clusters 63.01 Subjectivity Lexicon 63.49 AFINN Lexicon 63.43 General Inquirer (GI) 63.94 WordNet (WN) 65.49 WN, GI, HE 66.38 Table 2 : 2Ablation results for lexicons features in tweetlevel classification. 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Semi-supervised Method to Cluster Chinese Events on Social Streams

Event clustering on social streams aims to cluster short texts according to event contents. Event clustering models can be divided into unsupervised learning or supervised learning at present. The unsupervised models suffer from poor performance, while the supervised models require lots of labeling data. To address the above issues, this paper proposes a semi-supervised incremental event clustering model SemiEC based on a small-scale annotated dataset. This model encodes the events by LSTM and calculates text similarity by a linear model, and then clusters short texts on social streams. In particular, it uses the samples generated by incremental clustering to retrain the model and redistribute the uncertain samples. Experimental results show that this model SemiEC outperforms the traditional clustering algorithms.

Semi-supervised Method to Cluster Chinese Events on Social Streams 郭 郭 郭恒 恒 恒睿 School of Computer Science and Technology Soochow University SuzhouChina ， ， ，王 王 王中 中 中卿 School of Computer Science and Technology Soochow University SuzhouChina ， ， ，李 李 李培 培 培峰 ， ， ，朱 朱 朱巧 巧 巧明 School of Computer Science and Technology Soochow University SuzhouChina AI Research Institute Soochow University SuzhouChina Hengrui Guo hengruig@outlook.com School of Computer Science and Technology Soochow University SuzhouChina Zhongqing Wang School of Computer Science and Technology Soochow University SuzhouChina Peifeng Li Qiaoming Zhu qmzhu@suda.edu.cn Semi-supervised Method to Cluster Chinese Events on Social Streams 基 基 基于 于 于半 半 半监 监 监督 督 督学 学 学习 习 习的 的 的中 中 中文 文 文社 社 社交 交 交文 文 文本 本 本事 事 事件 件 件聚 聚 聚类 类 类方 方 方法 法 法 * 摘 摘 摘要 要 要 面向社交媒体的事件聚类旨在根据事件特征对短文本聚类。目前，事件聚类模型主要 分为无监督模型和有监督模型。无监督模型聚类效果较差，有监督模型依赖大量标注 数据。基于此，本文提出了一种半监督事件聚类模型(SemiEC)，该模型在小规模标 注数据的基础上，利用LSTM表征事件，利用线性模型计算文本相似度，进行增量聚 类，利用增量聚类产生的标注数据对模型再训练，结束后对不确定样本再聚类。实验 表明，SemiEC的性能相比其他模型均有所提高。 关 关 关键 键 键词 词 词： ： ： 社交媒体事件聚类 ；增量聚类 ；文本相似度event clustering on social mediaincremental clusteringtext similarity * Event clustering on social streams aims to cluster short texts according to event contents. Event clustering models can be divided into unsupervised learning or supervised learning at present. The unsupervised models suffer from poor performance, while the supervised models require lots of labeling data. To address the above issues, this paper proposes a semi-supervised incremental event clustering model SemiEC based on a small-scale annotated dataset. This model encodes the events by LSTM and calculates text similarity by a linear model, and then clusters short texts on social streams. In particular, it uses the samples generated by incremental clustering to retrain the model and redistribute the uncertain samples. Experimental results show that this model SemiEC outperforms the traditional clustering algorithms. 1 引 引 引言 言 言 在如今的网络时代，随着移动互联网的发展，信息交互变得前所未有的简便快捷。QQ、 微信、微博、抖音、快手等社交媒体深入走进了人们的生活，改变了人们的生活习惯。研究表 明，社交媒体对于新事件的反应要比传统媒体更加敏锐 (Petrović et al., 2010) 。因此，对社交媒 体中的文本进行数据分析有着非常重要的意义。其中，事件聚类是社交媒体中事件检测的重要 步骤(Aggarwal and Subbian, 2012)。 事件聚类旨在根据文本事件特征的不同对社交媒体中的文本进行聚类。社交媒体中多为 短文本，且文本内容具有多样性、随意性，包含较多的干扰词。传统的无监督聚类模型难以 准确提取社交文本的事件特征，因而得到的事件聚类结果一般准确度较低。Wang and Zhang (2017) 采用了有监督的深度神经网络模型对社交文本进行聚类，增强了聚类效果，但面对海量 的社交文本，该方法需要大量的文本标注工作。 基 于 此 ， 本 文 提 出 了 一 种 半 监 督 的 中 文 社 交 文 本 增 量 事 件 聚 类 模 型SemiEC(Semi- supervised Chinese incremental Event Clustering model)。SemiEC模型利用LSTM (Hochreiter and Schmidhuber, 1997 (Xu et al., 2017) (Allan et al., 1998) 采用了基于DCNN的深度神经网络学习文本的 深度特征表示。该模型首先通过现有无监督降维方法得到文本的二进制编码，然后将文本通 过Word Embedding输入卷积神经网络，将文本的二进制编码作为模型的训练目标。将卷积层 与输出层之间的中间特征向量作为文本的深度特征表示，是一种基于自训练的无监督模型。 对 于 社 交 媒 体 中 的 流 式 数 据 ， 常 用 的 聚 类 算 法 有singlepass增 量 算 法 和 局 部 敏 感 哈 希(LSH)算法。对于到来的新样本，singlepass聚类算法首先需要计算新来文本与已有事件的 相似度，若相似度超过阈值，则将其加入相似度最大的已有事件，否则将其设为新事件3 半 半 半监 监 监督 督 督的 的 的社 社 社交 交 交媒 媒 媒体 体 体事 事 事件 件 件增 增 增量 量 量事 事 事件 件 件聚 聚 聚类 类 类模 模 模型 型 型( ( (SemiEC) ) ) 有监督聚类算法虽然聚类效果较好，但需要大量的数据标注工作来训练模型提高效果。无 监督方法的性能又往往不能满足实用的需求。基于此，本文提出了一种半监督的中文社交文本 增量事件聚类模型(SemiEC)，相比Wang and Zhang (2017)采用的模型在相同训练的情况下 进一步提高聚类效果。 图 1: 基于数据增强的增量聚类过程 聚类过程如图1，对于输入的社交媒体文本t i ，首先对其进行事件聚类，判断t i 是属于已有 事件还是新生事件，或是无法确定。若t i 属于已有事件，则将其加入该簇；若为新事件，则基 于t i 建立新的簇，否则设t i 为不确定样本，加入Buffer。该过程中使用LSTM提取文本特征，利 用线性模型计算文本相似度。由于增量聚类算法可以得到实时的聚类结果，因此每次对部分数 据进行聚类后，从聚类结果中抽取样本组成训练集对模型进行再训练，使模型进一步学习新的 事件特征，增强聚类效果。在结束后，将聚类结果中所含元素较少的簇中的样本全部设为不确 定样本。然后用经过多次再训练后的模型对不确定样本进行聚类，得到最终的聚类结果。下面 对聚类过程进行详细介绍。 3.1 文 文 文本 本 本表 表 表示 示 示 类似Wang and Zhang (2017)，SemiEC采用LSTM模型提取文本特征，记为M encoder 。在文 本输入前，首先进行分词和去停用词。将分词后的文本用X = {w 1 , w 2 , . . . , w n }表示。其中w i 表 示句子中第i个词在词表中的编号，n表示句子中的词数。使用由百度百科预训练的中文词向量 对句子进行词嵌入，用n维词向量x i 表示词w i ，x i ∈ R n * d 。将文本向量X = (x 1 , x 2 , . . . , x n )通 过LSTM模型得到一个隐藏序列{h 1 , h 2 , . . . , h n }，其中h t 由当前输入向量x t 和前一时刻的输 出h t−1 计算得到，t ∈ (1, n)，即h t = LST M (x t , h t−1 )。训练过程中初始状态的参数均为随机生 成。我们采用低维向量H = h n 来表示文本X。 3.2 文 文 文本 本 本相 相 相似 似 似度 度 度计 计 计算 算 算 对 于 两 个 文 本X i 和X j ， 首 先 通 过M encoder 得 到 两 个 文 本 的 特 征 向 量H i 和H j 。 将 向 量H i 和H j 拼接后，通过一个线性层得到向量H c ，最后通过一维线性层，由sigmoid函数得 到X i 和X j 的相似度P c ，P c ∈ [0, 1]，将相似度计算模型记为M sim ，计算过程如下式。 H c = σ W h c (H i ⊕ H j ) + b h c (1) P c = sigmoid (W c H c + b c ) (2) 其中，⊕代表两个向量的拼接，W h c ，b h c ，W c ，b c 为模型参数。 3.3 聚 聚 聚类 类 类算 算 算法 法 法 SemiEC模型的聚类过程主要分为三个步骤：1)：对社交媒体文本的事件聚类；2)：对模 型的再训练；3)：对不确定样本的重新聚类。 对社交文本的事件聚类(算法1-12行) 对于一个新到来的社交文本t i ，要判断其与已有事件的最大相似度。SemiEC模型用事件 簇中的前N个文本来表示这个簇，作为簇心，若样本数不足N，则将簇中全部样本作为簇心。 将t i 与代表簇心的各个文本的相似度平均值作为t i 与该簇的相似度。设定两个阈值L和H，其 中0 < L < 0.5 < H < 1。若t i 与目前已有簇的最大相似度大于H，则将t i 加入相似度最大的簇； 若t i 与目前已有簇的最大相似度小于L，则以t i 建立一个新的簇；若t i 与目前已有簇的最大相似 度在L和H之间，则认为t i 分配不确定，将t i 加入缓冲区Buf f er，暂时不进行聚类。 传统singlepass算法采用一个阈值判断文本t i 属于已有事件还是属于新生事件，相比较而 言SemiEC模型加入了不确定样本这一分类，可以防止较差样本影响簇心表征和再训练的质量。 对模型的再训练(算法13-17行) 将特征提取模型M encoder 和相似度模型M sim 拼接到一起同时训练。设置一个更新阈值U ， 每当有U 个样本完成聚类，就从U 个样本中抽取D组训练数据组成训练集，对模型进行再训练。 训练集中正例与负例的比为1:1。正例抽取方法为：从U 个样本中随机选取一个样本p，再从U 个 样本中与p同簇的样本集中随机选取一个样本q，样本p与样本q组成一组正例，标签置为1。负例 的抽取方法为：从U 个样本中随机选取一个样本p，再从U 个样本中与p不同簇的样本集中随机 选取一个样本q，若U 中没有与p不同簇的，则从目前所有已聚类且与p不同簇的样本中随机选取 一个样本q，样本p与样本q组成一组负例，标签置为0。若目前所有已聚类的样本中都没有与p不 同簇的，则本次不进行训练。 相比Wang and Zhang (2017) (2017) (2017) 相同的规则搜集了关于40次不同 地震事件的微博，共计10828个。采用30次地震事件作为训练数据，从中随机选取样本组成训练 集。其中同一地震事件中的两个文本为正例，标签为1；不同地震事件中的两个文本为负例，标 签为0。训练集中正例和负例比为1:1，总共抽取了400000组样本训练模型。剩余的10次地震事 件数据作为测试集，共包含2518个文本，用于事件聚类实验。 4.2 实 实 实验 验 验参 参 参数 数 数 聚类算法中N 的取值为25，用事件簇中的前N 个文本来表示该簇。N 越大，对事件簇的代 表性越强，但计算量也会增大。聚类过程中的阈值L和H的的取值范围为0 < L < 0.5 < H < 1，L的取值越接近0，H的取值越接近1，对不确定样本的筛选越严格，但与此同时也会增大计 算量。本次实验中L的取值为0.3，H的取值为0.7。更新阈值U 的取值不能太小，否则训练过于 频繁，使模型容易受一些极端值的影响，产生偏离；U 值过大则会使大量样本仅能使用原始模 型聚类。本次实验中U 值为200。每次训练集样本数D的取值越大，模型对事件特征的学习效 果越明显，但同时也会增大运算量。同时D也受U 的影响，实验中训练集样本数D取值为800， 为U的4倍。训练轮数为5轮。 4.3 模 模 模型 型 型参 参 参数 数 数 模 型 基 于Keras框 架 ， 后 端 为Tensorflow。 特 征 提 取 模 型M encoder 训 练 过 程 中 使 用 了 由 百 度 百 科 预 训 练 的 词 向 量 ， 向 量 维 度 为300， 嵌 入 层 设 置 为 不 可 训 练 。LSTM层 输 出 维 度 为128，dropout=0.1，recurrent dropout=0.1， 不 返 回 序 列 ， 其 余 为 默 认 参 数 。 相 似 度 模 型M sim 中 的 全 连 接 层 输 出 维 度 为128， 激 活 函 数 为relu， 其 中 还 包 含 两 个Dropout层，Dropout=0.1。 神 经 网 络N的 输 出 为y i ， 真 实 标 签 为ȳ i ， 为0或1。 优 化 器 为adam， 采 用 交 叉 熵 损 失 函 数binary crossentropy，计算步骤如下： loss = − 1 N N i=1ȳ i log(y i ) + (1 −ȳ i ) log(1 − y i ) (3) 4.4 模 模 模型 型 型训 训 训练 练 练 将M encoder 和M sim 拼 接 到 一 起 ， 两 个 文 本 通 过M encoder 得 到 特 征 向 量H i 和H j ， 将 其 作 为M sim 的输入，组成一个计算文本相似度的孪生神经网络N，最终得到两个文本的相似度。 将神经网络N在训练集上训练5轮，将训练得到的参数赋予M encoder 和M sim ，从而实现模 型的预训练。模型再训练同样是对网络N进行再训练，训练轮数为5轮，将更新后的参数赋 予M encoder 和M sim ，从而实现模型的再训练。 4.5 评 评 评价 价 价指 指 指标 标 标 我 们 采 用 纯 度Purity， 归 一 化 互 信 息NMI和 调 整 兰 德 系 数ARI作 为 聚 类 效 果 的 评 价 指 标。Purity是正确计算的文本数与文本总数的比值。其定义如下： P urity (Ω, C) = 1 N k max j |w k ∩ c j |(P (w k ∩ c j ) log P (w k ∩ c j ) P (w k ) P (c j ) = k j |w k ∩ c j | N log N |w k ∩ c j | |w k | |c j | (6) H表示熵： H (Ω) = − i w i N log w i N (7) NMI的取值范围为[0, 1]，越接近1，聚类效果越好。 调整兰德系数ARI弥补了兰德系数RI惩罚力度不够的问题，其定义如下： RI = a + b C 2 N (8) ARI = RI − E [RI] max [RI] − E [RI] (9) 其中，a表示实际类簇与聚类预测类簇中都是同类别的元素对数，b表示实际类簇不同 类别，在聚类预测类簇中也是不同类别的元素对数。E [RI]为RI的平均值。ARI的取值范围 为[−1, 1]，越接近1，聚类效果越好。 4.6 聚 聚 聚类 类 类结 结 结果 果 果 我们将提出的事件聚类模型得到的聚类结果与以下聚类方法进行对比。 1) Singlepass： 该 聚 类 方 法 采 用 向 量 空 间 模 型 表 示 文 本 ， 用cosine计 算 文 本 相 似 度 ， 采 用singlepass算法进行聚类，属于无监督聚类算法。模型由自己实现。 2) Kmeans： 该 聚 类 方 法 采 用 向 量 空 间 模 型 表 示 文 本 ， 用cosine计 算 文 本 相 似 度 ， 采 用Kmeans算法进行聚类，属于无监督聚类算法。实验中指定正确的事件个数。模型由自5 分 分 分析 析 析 5.1 训 训 训练 练 练数 数 数据 据 据大 大 大小 小 小对 对 对SemiEC模 模 模型 型 型的 的 的影 影 影响 响 响 有监督聚类算法对训练集的依赖较大，要使模型可以更准确地区分各种事件，其关键在于 训练集中是否有足够充分的事件类型。因此，分别选取10次，15次，20次，25次，30次地震事 件作为训练数据，从中抽取400000组文本对组成训练集对模型进行训练，以4.1节中的10次地震 事件作为测试集，以Wang and Zhang (2017)的模型为baseline，以NMI为参考指标，将测试数 据随机打乱进行10次聚类，对NMI取平均值，得到结果如图2。 图 2: 不同训练集事件数对聚类结果的影响 由图2可以看出在不同训练集事件数的情况下，SemiEC模型相比baseline模型聚类效果有所 提高，但不同训练数据的聚类效果有所差别。当训练事件数为15时，SemiEC的性能已经超过了 使用更多训练数据的baseline模型。这充分说明了本文半监督方法的有效性，可以在少量标注数 据的基础上，通过利用聚类过程中产生的标注数据学习新的事件特征，获得更好的性能。 5.2 参 参 参数 数 数设 设 设置 置 置对 对 对SemiEC模 模 模型 型 型的 的 的影 影 影响 响 响 模型再训练和不确定样本重聚类步骤都需要设置一些额外的参数。其中对SemiEC模 型 聚 类 效 果 影 响 最 大 的 是 更 新 阈 值U， 主 要 用 于 控 制 模 型 再 训 练 的 频 率 。 本 次 实 验 将U分 别 设 置 为10，50，200，500，1000， 每 次 训 练 集 样 本 数D设 置 为U的4倍 ， 分 别 为40，200，800，2000，4000。 以4.1节 中 的10次 地 震 事 件 作 为 测 试 集 ， )提取文本特征，利用线性模型计算两个文本属于同一事件的概率，在此 基础上进行增量聚类。该模型利用增量聚类过程产生的标注样本对模型进行再训练。在无需额 外数据标注工作的同时，帮助模型学习更多的事件信息，提高聚类效果。同时，对于聚类过程 中的不确定样本，暂时不进行聚类，在结束后用再训练后的模型进行重新聚类，可以防止较差 样本影响簇心表征，影响模型再训练，提高对不确定样本的聚类准确度。实验表明，SemiEC模 型与基准模型相比在各项聚类指标上均得到了提高。 2 相 相 相关 关 关工 工 工作 作 作目前，大部分事件聚类研究主要基于词的特征。与长文本不同，短文本聚类存在高维稀 疏的问题(Aggarwal and Subbian, 2012)，因此一些学者考虑引入外部特征。其中Mathioudakis and Koudas (2010)以及Saeed et al. (2019)利用突发性关键词来预测短文本的重要性，并通过这 些重要的短文本来检测事件进行聚类。Nguyen and Jung (2015)通过考虑事件的发布时间、扩 散程度和扩散敏感性，采用时间特征来检测事件，并进行聚类。除此之外，Li et al. (2012)探索 了用户在社交媒体数据中的影响，利用文本内容特征、用户特征和使用特征来检测事件并进行 聚类。Mcminn and Jose (2015)借助了文本的命名实体特征来加强事件检测的效果。 为了解决传统方法高维稀疏的问题，Cai et al. (2005)通过局部保存索引(LPI)将高维文本 投影到低维语意空间，同时使语意相关的文本在低维空间中也彼此接近。Qimin et al. (2015)使 用Kmeans聚类算法对特征词集进行聚类得到特征簇，用特征簇表示句向量，从而解决了向量空 间模型维度爆炸的问题，同时提高了聚类效果。Zhou et al. (2018)以word2vec词向量为基础， 结合时序关系，提出了JS-ID'F顺序来进行文本嵌入。Arora et al. (2019)提出了对文本的SIF Embedding，通过对词向量进行加权平均，再用PCA和SVD对其进行一些修改，得到文本的低 维向量表示。Xu et al. (2015) 。该算法的关键步骤在于计算文本相似度，目前最为常用的是余弦相似度cosine。 局部敏感哈希(LSH)聚类算法主要基于新事件检测模型(FSD)，其思路是通过LSH算法找到新 来文本在已聚类文本中的近邻文本集合，从该集合中找到新来文本的最近邻文本，若两者最 大相似度大于设定阈值，则为已有事件，否则为新事件(Petrović et al., 2010)。在局部敏感 哈希(LSH)聚类算法中，其关键步骤在于通过LSH算法尽快找到新来文本的近邻文本。Wurzer et al. (2015)对寻找最近邻文本的哈希算法做了改进，提高了效率，但准确率与Petrović et al. (2010)相当。Xie et al. (2016)提出了一种基于自训练的深度嵌入式聚类模型。该模型使用深 度神经网络同时学习特征表示和聚类分配，是一种基于划分的聚类模型，不适合处理流式数 ©2020 中国计算语言学大会 根据《Creative Commons Attribution 4.0 International License》许可出版 据。Hadifar et al. (2019)使用SIF Embedding进行句子表征，采用自编码器提取文本的低维特 征表示，采用类似Xie et al. (2016)的聚类算法通过自训练的神经网络模型同时学习文本特征 表示和聚类分配，得到聚类结果。Finley and Joachims (2005)利用有监督的SVM模型判断两个 文本是否相关，通过Bansal et al. (2004)的方法进行文本聚类，该聚类方法将样本分布视为一 个图模型，通过最大化簇内的样本对相似性实现聚类。Haponchyk et al. (2018)将Finley and Joachims (2005)的方法进行改进，用于对话系统中用户问题的聚类，从而分析用户意图。Wang and Zhang (2017)采用了有监督的LSTM模型提取文本特征，计算文本相似度，采用增量聚类算 法进行社交文本聚类，相比此前的聚类算法有所提高，但需要大量的数据对模型进行训练。目 前，在面向社交媒体的事件聚类方法中，还没有采用半监督的方法。 4 ) 4其中N 表示总的样本个数，Ω = {w 1 , w 2 , . . . , w K }表示聚类模型得到的聚类簇划分，C = {c 1 , c 2 , . . . , c J } 表示真实类别划分。Purity取值范围为[0, 1]，越接近1，聚类效果越好。 NMI是一个基于熵的评价指标，其定义如下： N M I (Ω, C) = I (Ω; C) [H (Ω) + H (C)] 己实现。 3 ) 3LSH(Petrović et al., 2010)：这是一种基于局部敏感哈希的聚类算法，采用向量空间模型表 示文本，用cosine计算文本相似度，属于无监督聚类算法。模型由自己实现。 4) Hadif ar(Hadifar et al., 2019)：该聚类模型采用SIF Embedding表示文本，通过自编码器学 习文本的低维特征表示，采用类似Xie et al. (2016)的深度聚类算法进行短文本聚类，属于无 监督聚类算法。该方法在实验中采用不同领域的短文本作为测试集，本次实验中的测试集为 地震领域的不同事件。实验中指定正确的事件个数。模型采用了改论文中提供的代码。 5) W ang(Wang and Zhang, 2017)：该聚类模型利用LSTM提取文本特征，利用线性神经网络 模型计算文本相似度，通过增量聚类算法进行聚类，属于有监督聚类算法。模型由自己实 现。 6) BERT ：该方法将Wang and Zhang for each C i ∈ C do 19: if |C i | < N then 将C i 中样本加入Buf f er，删除C i for each t j ∈ Buf f er do 对于文本t j ，利用M encoder 得到其特征向量X j 利用M sim 计算X j 和C中的每个簇C m 的相似度Sim m 得到与t j 相似度最大的簇C r ，Sim r = M ax (Sim m ) 输出C = {C 1 , C 2 , . . . , C k } 4.1 实 实 实验 验 验数 数 数据 据 据本次实验的数据来自微博。采用与Wang and Zhang的方法，SemiEC增加了模型再训练的步骤，利用增量聚类过 程中产生的标注数据对模型进行再训练，可以使模型学习新事件的特征，进一步提高模型的泛 化能力。 不确定样本进行重新聚类(算法18-32行) 在增量聚类结束后，经常会出现个别样本数特别少的簇，这是增量聚类模型经常容易出 现的问题。若聚类结束后，出现包含样本数少于N 的事件簇，则删除这些簇，并将这些簇中 的样本加入缓冲区Buf f er，Buf f er中包含聚类过程中的不确定样本。增量聚类结束后，再 对Buf f er中的样本进行重新聚类。计算不确定样本与已有事件的最大相似度，若样本与已有事 件簇的最大相似度大于0.5，则将其加入该簇，否则以该样本建立新的簇。 相比Wang and Zhang (2017)的方法，SemiEC增加了不确定样本重新聚类的步骤，一方 面，可以防止不确定样本加入簇心影响事件簇的表征以及进入训练集对模型进行错误的训练； 另一方面，在聚类结束后，模型经过多次训练后效果有所增强，可以对这些不确定的样本进行 更准确的聚类。 Algorithm 1 半监督社交媒体事件增量聚类算法 算法开始 Input: 社交文本T = {t 1 , t 2 , . . . , t n } 阈值L和H，表征簇的文本个数N 特征提取模型M encoder ，相似度模型M sim 更新阈值U ，每次训练集样本数D，缓冲区Buf f er Output: 事件聚类结果C = {C 1 , C 2 , . . . , C k } 1: Initialize: 将t 1 初始化为第一个簇C = {C 1 }；Buf f er = ∅ 2: for each t i ∈ {t 2 , t 3 , . . . , t n } do 3: 对于文本t i ，利用M encoder 得到其特征向量X i 4: 利用M sim 计算X i 和C中的每个簇C m 的相似度Sim m 5: 得到与t i 相似度最大的簇C r ，Sim r = M ax (Sim m ) 6: if Sim r > H then 7: 将t i 加入C r 8: else if Sim r ≥ L then 9: 将t i 加入Buf f er 10: else 11: 将t i 设为新的簇，加入C 12: end if 13: if 有U 个样本加入C then 14: 抽取D组数据组成训练集，对模型M sim 和M encoder 进行训练 15: 通过M encoder 更新C中簇的表示 16: end if 17: end for 18: 20: 21: end if 22: end for 23: 24: 25: 26: 27: if Sim r > 0.5 then 28: 将t j 加入C r 29: else 30: 将t j 设为新的簇，加入C 31: end if 32: end for 33: 5.3 模 模 模型型 型再 再 再训 训 训练 练 练和 和 和不 不 不确 确 确定 定 定样 样 样本 本 本再 再 再聚 聚 聚类 类 类的 的 的有 有 有效 效 效性 性 性 为了证明模型再训练步骤和不确定样本再聚类步骤的有效性，分别对这两个步骤进行 了测试。Retrain表示仅加入模型再训练步骤，Recluster表示仅加入不确定样本再聚类步骤。 以4.1节中的10次地震事件作为测试集，以Wang and Zhang (2017)的模型为baseline，将测试数 据随机打乱进行10次聚类，对各项聚类指标取平均值，得到结果如表2。以Wang and Zhang (2017)的模型为baseline，以NMI为参考指标，将测试数据随机打乱进行10次聚类，对NMI取 平均值，得到结果如图3。 图 3: 不同U值对聚类结果的影响 由图3中结果可以看出，当U值较小，为10时，聚类效果较差，主要原因在于训练集较小， 所属类别分布不均匀的概率较大，训练反而导致模型偏向于个别事件，使得聚类所得事件数比 实际事件数少，聚类效果较差；当U值逐渐增大到200，样本分布趋于均匀的时候，聚类效果相 对baseline会有明显提高；但不断增大U值会减少模型再训练的次数，导致大量数据仅能使用原 始模型进行聚类，从而使聚类结果不断趋近但不低于baseline。因此，U的取值要在保证数据分 布尽量均匀的情况下，取较小的值，此时可以使SemiEC模型达到最好的聚类效果。 聚类模型 Purity NMI ARI Wang 0.78 0.79 0.60 Retrain 0.79 0.82 0.69 Recluster 0.80 0.80 0.65 SemiEC 0.81 0.83 0.70 表 2: 模型再训练和不确定样本再聚类有效性对比 由表2数据可以看出，Retrain和Recluster，相比baseline在各项聚类指标上均有所提高，这 充分说明了模型再训练和不确定样本再聚类步骤的有效性。其中，模型再训练步骤可以帮助模 型学习新的事件特征，增强对后续样本的聚类效果。不确定样本再聚类步骤可以防止不确定样 本加入簇心，减少错误样本对簇心表征的影响，从而增强聚类效果。将两者结合后的SemiEC模 型，通过不确定样本再聚类减少错误样本进入训练集，增强模型再训练的效果，同时对不确定 样本用再训练后的模型重新聚类，进一步增强不确定样本的聚类效果，两者相互提高，得到最 好的聚类效果。 6 总 总 总结 结 结 本文提出了一种半监督增量型中文社交文本事件聚类模型SemiEC，采用LSTM提取文本特 征，采用线性模型计算文本相似度，进行增量聚类。利用增量聚类过程产生的标注样本对模型 进行再训练。对聚类过程中分配不确定的样本在结束后重新聚类。再训练过程可以让模型学习 新的事件信息，使模型准确度随着聚类过程不断提高。对不确定样本的重新聚类可以防止不确 定样本影响簇心表征，减少错误样本对模型进行再训练的概率，同时提高不确定样本的聚类准 确度。SemiEC模型与经过同样预训练的有监督聚类模型相比，在各项聚类指标上均有所提高。 Event detection in social streams. 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Removing Left Recursion from Context-Free Grammars

A long-standing issue regarding algorithms that manipulate context-free grammars (CFGs) in a "topdown" left-to-right fashion is that left recursion can lead to nontermination. An algorithm is known that transforms any CFG into an equivalent nonleft-recursive CFG, but the resulting grammars are often too large for practical use. We present a new method for removing left recursion from CFGs that is both theoretically superior to the standard algorithm, and produces very compact non-left-recursive CFGs in practice.

Removing Left Recursion from Context-Free Grammars Robert C Moore bobmoore@microsoft.corn Microsoft Research One Microsoft Way Redmond 98052Washington Removing Left Recursion from Context-Free Grammars A long-standing issue regarding algorithms that manipulate context-free grammars (CFGs) in a "topdown" left-to-right fashion is that left recursion can lead to nontermination. An algorithm is known that transforms any CFG into an equivalent nonleft-recursive CFG, but the resulting grammars are often too large for practical use. We present a new method for removing left recursion from CFGs that is both theoretically superior to the standard algorithm, and produces very compact non-left-recursive CFGs in practice. Introduction A long-standing issue regarding algorithms that manipulate context-free grammars (CFGs) in a "topdown" left-to-right fashion is that left recursion can lead to nontermination. This is most familiar in the case of top-down recursive-descent parsing (Aho et al., 1986, pp. 181-182). A more recent motivation is that off-the-shelf speech recognition systems are now available (e.g., from Nuance Communications and Microsoft) that accept CFGs as language models for constraining recognition; but as these recognizers process CFGs top-down, they also require that the CFGs used be non-left-recursive. The source of the problem can be seen by considering a directly left-recursive grammar production such as A -4 As. Suppose we are trying to parse, or recognize using a speech recognizer, an A at a given position in the input. If we apply this production top-down and left-to-right, our first subgoal will be to parse or recognize an A at the same input position. This immediately puts us into an infinite recursion. The same thing will happen with an indirectly left-recursive grammar, via a chain of subgoals that will lead us from the goal of parsing or recognizing an A at a given position to a descendant subgoal of parsing or recognizing an A at that position. In theory, the restriction to non-left-recursive CFGs puts no additional constraints on the languages that can be described, because any CFG can in principle be transformed into an equivalent non-left-recursive CFG. However, the standard algo-rithm for carrying out this transformation (Aho et al., 1986, pp. 176-178) (Hopcroft and Ullman, 1979, p. 96)--attributed to M. C. Panll by Hopcroft and Ullman (1979, p. 106)--can produce transformed grammars that are orders of magnitude larger than the original grammars. In this paper we develop a number of improvements to Panll's algorithm, which help somewhat but do not completely solve the problem. We then go on to develop an alternative approach based on the left-corner grammar transform, which makes it possible to remove left recursion with no significant increase in size for several grammars for which Paull's original algorithm is impractical. 2 Notation and Terminology Grammar nonterminals will be designated by "low order" upper-case letters (A, B, etc.); and terminals will be designated by lower-case letters. We will use "high order" upper-case letters (X, Y, Z) to denote single symbols that could be either terminals or nonterminals, and Greek letters to denote (possibly empty) sequences of terminals and/or nonterminals. Any production of the form A --4 a will be said to be an A-production, and a will be said to be an expansion of A. We will say that a symbol X is a direct left corner of a nonterminal A, if there is an A-production with X as the left-most symbol on the right-hand side. We define the left-corner relation to be the reflexive (Moore et al., 1997), a spoken-language interface to a military simulation system. The ATIS grammar was extracted from an internally generated treebank of the DARPA ATIS3 training sentences (Dahl et al., 1994). The PT grammar 2 was extracted from the Penn Treebank (Marcus et al., 1993). To these grammars we add a small "toy" grammar, simply because some of the algorithms cannot be run to completion on any of the "real" grammars within reasonable time and space bounds. Some statistics on the test grammars are contained in Table 1. The criterion we use to judge effectiveness of the algorithms under test is the size of the' resulting grammar, measured in terms of the total number of terminal and nonterminal symbols needed to express the productions of the grammar. We use a slightly nonstandard metric, counting the symbols as if, for each nonterminal, there were a single production of the form A --+ al I ..-[ a,~. This reflects the size of files and data structures typically used to store grammars for top-down processing more accurately than counting a separate occurrence of the left-hand side for each distinct righthand side. It should be noted that the CT grammar has a very special property: none of the 535 left recursive nonterminals is indirectly left recursive. The grammar was designed to have this property specifically because Paull's algorithm does not handle indirect left recursion well. It should also be noted that none of these grammars contains empty productions or cycles, which can cause problems for algorithms for removing left recursion. It is relatively easy to trasform an arbitrary CFG into an equivalent grammar which does not contain any of the probelmatical cases. In its initial form the PT grammar contained cycles, but these were removed at a cost of increasing the size of the grammar by 78 productions and 89 total symbols. No empty productions or cycles existed anywhere else in the original grammars. Paull's Algorithm Panll's algorithm for eliminating left recursion from CFGs attacks the problem by an iterative procedure for transforming indirect left recursion into direct left recursion, with a subprocedure for eliminating direct left recursion, This algorithm is perhaps more familiar to some as the first phase of the textbook algorithm for transfomrming CFGs to Greibach norreal form (Greibach, 1965). 3 The subprocedure to eliminate direct left recursion performs the following transformation (Hopcroft and UUman, 1979, p. 96 This transformation is embedded in the full algorithm (Aho et al., 1986, p. 177 ), displayed in Fig- ure 1. The idea of the algorithm is to eliminate left recursion by transforming the grammar so that all the direct left corners of each nonterminal strictly follow that nonterminal in a fixed total ordering, in which case, no nonterminal can be left recursive. This is accomplished by iteratively replacing direct left corners that precede a given nonterminal with all their expansions in terms of other nonterminals that are greater in the ordering, until the nonterminal has only itself and greater nonterminals as direct left Assign an ordering A1,..., A,~ to the nonterminals of the grammar. for i := 1 to n do begin for j :--1 to i -1 do begin for each production of the form Ai ~ Aja do begin remove Ai -+ Aja from the grammar for each production of the form Aj -~/~ do begin add Ai --~/~a to the grammar end end end transform the Ai-productions to eliminate direct left recursion end Grammar Description Grammar Size original toy grammar 88 PA, "best" ordering 156 PA, lexicographical ordering 970 PA, "worst" ordering 5696 corners. Any direct left recursion for that nonterminal is then eliminated by the first transformation discussed. The difficulty with this approach is that the iterated substitutions can lead to an exponential increase in the size of the grammar. Consider the grammar consisting of the productions Az -+ 0 I 1, plus Ai+z -+ AiO I Ail for I < i < n. It is easy to see that Paull's algorithm will transform the grammar so that it consists of all possible Ai-productions with a binary sequence of length i on the right-hand side, for 1 < i < n, which is exponentially larger than the original grammar. Notice that the efficiency of PauU's algorithm crucially depends on the ordering of the nonterminals. If the ordering is reversed in the grammar of this example, Paull's algorithm will make no changes, since the grammar will already satisfy the condition that all the direct left corners of each nonterminal strictly follow that nonterminal in the revised ordering. The textbook discussions of Paull's algorithm, however, are silent on this issue. In the inner loop of Panll's algorithm, for nonterminals Ai and Aj, such that i > j and Aj is a direct left corner of Ai, we replace all occurrences of Aj as a direct left corner of Ai with all possible expansions of Aj. This only contributes to elimination of left recursion from the grammar if Ai is a left-recursive nonterminal, and Aj ]ies on a path that makes Ai left recursive; that is, if Ai is a left corner of A3 (in addition to Aj being a left corner of Ai). We could eliminate replacements that are useless in removing left recursion if we could order the nonterminals of the grammar so that, if i > j and Aj is a direct left corner of Ai, then Ai is also a left corner of Aj. We can achieve this by ordering the nonterminals in decreasing order of the number of distinct left corners they have. Since the left-corner relation is transitive, if C is a direct left corner of B, every left corner of C is also a left corner of/3. In addition, since we defined the left-corner relation to be reflexive, B is a left corner of itself. Hence, if C is a direct left corner of B, it must follow B in decreasing order of number of distinct left corners, unless B is a left corner of C. Table 2 shows the effect on Paull's algorithm of ordering the nonterminals according to decreasing number of distinct left corners, with respect to the toy grammar. 4 In the table, "best" means an ordering consistent with this constraint. Note that if a grammar has indirect left recursion, there will be multiple orderings consistent with our constraint, since indirect left recursion creates cycles in the the left-corner relation, so every nonterminal in one of these cycles will have the same set of left corners. Our "best" ordering is simply an arbitrarily chosen 4As mentioned previously, grammar sizes are given in terms of total terminal and nonterminal symbols needed to express the grammar. PT Grammar 67,904 > 5,000,000 37,811 > 5,000,000 > 5,000,000 Table 3: Grammar size comparisons with Panll's algorithm variants ordering respecting the constraint; we are unaware of any method for finding a unique best ordering, other than trying all the orderings respecting the constraint. As a neutral comparison, we also ran the algorithm with the nonterminals ordered lexicographically. Finally, to test how bad the algorithm could be with a really poor choice of nonterminal ordering, we defined a "worst" ordering to be one with increasing numbers of distinct left corners. It should be noted that with either the lexicographical or worst ordering, on all of our three large grammars Panll's algorithm exceeded a cut-off of 5,000,000 grammar symbols, which we chose as being well beyond what might be considered a tolerable increase in the size of the grammar. Let PA refer to Paull's algorithm with the nonterminals ordered according to decreasing number of distinct left corners. The second line of Table 3 shows the results of running PA on our three large grammars. The CT grammar increases only modestly in size, because as previously noted, it has no indirect left recursion. Thus the combinatorial phase of Paull's algorithm is never invoked, and the increase is solely due to the transformation applied to directly left-recursive productions. With the ATIS grammar and PT grammar, which do not have this special property, Panll's algorithm exceeded our cutoff, even with our best ordering of nonterminals. Some additional optimizations of Panll's aglorithm are possible. One way to reduce the number of substitutions made by the inner loop of the algorithm is to "left factor" the grammar (Aho et al., 1986, pp. 178-179). The left-factoring transformation (LF) applies the following grammar rewrite schema repeatedly, until it is no longer applicable: LF: For each nonterminal A, let a be the longest nonempty sequence such that there is more than one grammar production of the form A --+ a~. With left factoring, for each nonterminal A there will be only one A-production for each direct left corner of A, which will in general reduce the number of substitutions performed by the algorithm. The effect of left factoring by itself is shown in the third line of Table 3. Left factoring actually reduces the size of all three grammars, which may be unintuitive, since left factoring necessarily increases the number of productions in the grammar. However, the transformed productions axe shorter, and the grammar size as measured by total number of symbols can be smaller because common left factors are represented only once. The result of applying PA to the left-factored grammars is shown in the fourth line of Table 3 (LF+PA). This produces a modest decrease in the size of the non-left-recursive form of the CT grammar, and brings the nomleft-recursive form of the ATIS grammar under the cut-off size, but the nonleft-recursive form of the PT grammar still exceeds the cut-off. The final optimization we have developed for Paull's algorithm is to transform the grammar to combine all the non-left-recursive possibilities for each left-recursive nonterminal under a new nonterminal symbol. This transformation, which we might call "non-left-recursion grouping" (NLRG), can be defined as follows: Since all the new nonterminals introduced by this transformation will be non-left-recursive, Paull's algorithm with our best ordering will never substitute the expansions of any of these new nonterminals into the productions for any other nonterminal, which in general reduces the number of substitutions the algorithm makes. We did not empirically measure the effect on grammar size of applying the NLRG transformation by itself, but it is easy to see that it increases the grammar size by exactly two symbols for each left-recursive nontermina] to which it is applied. Thus an addition of twice the number of left-recursive nontermina]s will be an upper bound on the increase in the size of the grammar, but since not every left-recursive nonterminal necessarily has more than one non-left-recursive expansion, the increase may be less than this. The fifth line of Table 3 (LF+NLRG+PA) shows the result of applying LF, followed by NLRG, followed by PA. This produces another modest decrease in the size of the non-left-recursive form of the CT grammar and reduces the size of the nonleft-recursive form of the ATIS grammar by a factor of 27.8, compared to LF÷PA. The non-left-recursive form of the PT grammar remains larger than the cut-off size of 5,000,000 symbols, however. Left-Recursion Elimination Based on the Left-Corner Transform An alternate approach to eliminating left-recursion is based on the left-corner (LC) grammar transform of Rosenkrantz and Lewis (1970) as presented and modified by Johnson (1998). Johnson's second form of the LC transform can be expressed as follows, with expressions of the form A-a, A-X, and A-B being new nonterminals in the transformed grammar: 1. If a terminal symbol a is a proper left corner of A in the original grammar, add A -4 aA-a to the transformed grammar. If B is a proper left corner of A and B --+ X~ is a production of the original grammar, add A-X -+ ~A-B to the transformed grammar. If X is a proper left corner of A and A --+ X~ is a production of the original grammar, add A-X -+ ~ to the transformed grammar. In Rosenkrantz and Lewis's original LC transform, schema 2 applied whenever B is a left corner of A, including all cases where B = A. In Johnson's version schema 2 applies when B --A only if A is a proper left corner of itself. Johnson then introduces schema 3 handle the residual cases, without introducing instances of nonterminals of the form A-A that need to be allowed to derive the empty string. The original purpose of the LC transform is to allow simulation of left-corner parsing by top-down parsing, but it also eliminates left recursion from any noncyclic CFG. 5 Fhrthermore, in the worst case, the total number of symbols in the transformed grammar cannot exceed a fixed multiple of the square of the number of symbols in the original grammar, in contrast to Paull's algorithm, which exponentiates the size of the grammar in the worst case. Thus, we can use Johnson's version of the LC transform directly to eliminate left-recursion. Before applying this idea, however, we have one genera] improvement to make in the transform. Johnson notes that in his version of the LC transform, a new nontermina] of the form A-X is useless unless X is a proper left corner of A. We further note that a new nonterminal of the form A-X, as well as the orginal nonterminal A, is useless in the transformed grammar, unless A is either the top nonterminal of the grammar or appears on the right-hand side of an original grammar production in other than the left-most position. This can be shown by induction on the length of top-down derivations using the productions of the transformed grammar. Therefore, we will call the original nonterminals meeting this condition "retained nontermina]s" and restrict the LC transform so that productions involving nonterminals of the form A-X are created only if A is a retained nonterminal. Let LC refer to Johnson's version of the LC transform restricted to retained nonterminals. In Table 4 the first three lines repeat the previously shown sizes for our three original grammars, their left-factored form, and their non-left-recursive form using our best variant of Panll's algorithm (LF+NLRG+PA). The fourth line shows the results of applying LC to the three original grammars. Note that this produces a non-left-recursive form of the PT grammar smaller than the cut-off size, but the non-leftrecursive forms of the CT and ATIS grammars are Sin the case of a cyclic CFG, the schema 2 fails to guarantee a non-left-recursive transformed grammar. considerably larger than the most compact versions created with Paull's algorithm. We can improve on this result by noting that, since we are interested in the LC transform only as a means of eliminating left-recursion, we can greatly reduce the size of the transformed grammars by applying the transform only to left-recursive nonterminals. More precisely, we can retain in the transformed grammar all the productions expanding nonleft-recursive nonterminals of the original grammar, and for the purposes of the LC transform, we can treat nomleft-recursive nonterminals as if they were terminals: 1. If a terminal symbol or non-left-recursive nonterminal X is a proper left corner of a retained left-recursive nonterminal A in the original grammar, add A -+ XA-X to the trans- Let LCLR refer to the LC transform restricted by these modifications so as to apply only to leftrecursive nonterminals. The fifth line of Table 4 shows the results of applying LCLR to the three original grammars. LCLR greatly reduces the size of the non-left-recursive forms of the CT and ATIS grammars, but the size of the non-left-recursive form of the PT grammar is only slightly reduced. This is not surprising if we note from Table 1 that almost all the productions of the PT grammar are productions for left-recursive nonterminals. However, we can apply the additional transformations that we used with Paull's algorithm, to reduce the number of productions for left-recursive nonterminals before applying our modified LC transform. The effects of left factoring the grammar before applying LCLR (LF+LCLR), and additionally combining non-left-recursive productions for left-recursive nonterminals between left factoring and applying LCLR (LF+NLRG+LCLR), are shown in the sixth and seventh lines of Table 4. With all optimizations applied, the non-leftrecursive forms of the ATIS and PT grammars are smaller than the originals (although not smaller than the left-factored forms of these grammars), and the non-left-recursive form of the CT grammar is only slightly larger than the original. In all cases, LF+NLRG+LCLR produces more compact grammars than LF+NLRG+PA, the best variant of Paull's algorithm--slightly more compact in the case of the CT grammar, more compact by a factor of 5.9 in the case of the ATIS grammar, and more compact by at least two orders of magnitude in the case of the PT grammar. Conclusions We have shown that, in its textbook form, the standard algorithm for eliminating left recursion from CFGs is impractical for three diverse, independently-motivated, natural-language grammars. We apply a number of optimizations to the algorithm--most notably a novel strategy for ordering the nonterminals of the grammar--but one of the three grammars remains essentially intractable. We then explore an alternative approach based on the LC grammar transform. With several optimizations of this approach, we are able to obtain quite compact non-left-recursive forms of all three grammars. Given the diverse nature of these grammars, we conclude that our techniques based on the LC transform are likely to be applicable to a wide range of CFGs used for natural-language processing. A Aa11... IAa be the set of all directly left recursive Aproductions, and let I/?s be the remaining A-productions. Replace all these productions with A --+/71 [/?IA' [ ... [/?8 [/?sA', and A' --+ az [ alA' [ ... I as [ asA', where A ~ is a new nonterminal not used elsewhere in the grammar. Figure 1 : 1Paull's algorithm. Replace the set of all productions A-+aft1, ..., A-+a~n with the productions A -+ aA', A' --~ ill, ..., A' --~ fin, where A' is a new nonterminal symbol. NLRG: For each left-recursive nonterminal A, let al,...,an be all the expansions of A that do not have a left recursive nonterminal as the left most symbol. If n > 1, replace the set of productions A -~ al , ..., A --~ a,~ with the productions A~A ~,A ~al, ...,A ~-~an, where A t is a new nonterminal symbol. formed grammar. 2 . 2If B is a left-recursive proper left corner of a retained left-recursive nonterminal A and B --~ X/~ is a production of the original grammar, add A-X -~ ~A-B to the transformed grammar. 3. If X is a proper left corner of a retained leftrecursive nonterminal A and A --~ X/~ is a production of the original grammar, add A-X --~ to the transformed grammar. 4. If A is a non-left-recursive nonterminal and A -~ /3 is a production of the original grammar, add A -~/~ to the transformed grammar. Table 2 : 2Effect of nonterminal ordering on Paull's algorithm. Table 4 : 4Grammar size comparisons for LC transform variants 3This has led some readers to attribute the algorithm to Greibach, but Greibach's original method was quite different and much more complicated. 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Computational Linguistics, 19(2):313-330, June. Commandtalk: A spoken-language interface for battlefield simulations. R Moore, J Dowding, H Bratt, J M Gawron, Y Gorfu, A Cheyer, Proceedings of the Fifth Conference on Applied Natural Language Processing. the Fifth Conference on Applied Natural Language ProcessingWashington, DCAssociation for Computational LinguisticsR. Moore, J. Dowding, H. Bratt, J. M. Gawron, Y. Gorfu, and A. Cheyer. 1997. Commandtalk: A spoken-language interface for battlefield simu- lations. In Proceedings of the Fifth Conference on Applied Natural Language Processing, pages 1-7, Washington, DC. Association for Computational Linguistics. Deterministic left corner parser. S J Rosenkrantz, P M Lewis, IEEE Conference Record of the 11th Annual Symposium on Switching and Automata Theory. S. J. Rosenkrantz and P. M. Lewis. 1970. Deter- ministic left corner parser. In IEEE Conference Record of the 11th Annual Symposium on Switch- ing and Automata Theory, pages 139-152.

Variations prosodiques en synthèse par sélection d'unités : l'exemple des phrases interrogatives

Cet article propose une méthode automatique d'augmentation des variations prosodiques en synthèse par sélection d'unités. Plus particulièrement, nous nous sommes intéressés à la synthèse de phrases interrogatives au sein du système de synthèse eLite, qui procède par sélection d'unités non uniformes et qui ne possède pas les unités nécessaires à la production de questions dans sa base de données. L'objectif de ce travail a été de pouvoir produire des interrogatives via ce système de synthèse, sans pour autant enregistrer une nouvelle base de données pour la sélection des unités. Après avoir décrit les phénomènes syntaxiques et prosodiques en jeu dans l'énonciation de phrases interrogatives, nous présentons la méthode développée, qui allie pré-traitement des cibles à rechercher dans la base de données, et post-traitement du signal de parole lorsqu'il a été généré. Une évaluation perceptive des phrases synthétisées via notre application nous a permis de percevoir l'intérêt du post-traitement en synthèse et de pointer les précautions qu'un tel traitement implique.ABSTRACTProsodic variations in unit-based speech synthesis: the example of interrogative sentencesThis paper proposes an automatic method to increase the number of possible prosodic variations in non-uniform unit-based speech synthesis. More specifically, we are interested in the production of interrogative sentences through the eLite text-to-speech synthesis system, which relies on the selection of non-uniform units, but does not have interrogative units in its speech database. The purpose of this work was to make the system able to synthesize interrogative sentences without having to record a new, interrogative database. After a study of the syntactic and prosodic phenomena involved in the production of interrogative sentences, we present our two-step method: an adapted pre-processing of the unit selection itself, and a post-processing of the whole speech signal built by the system. A perceptual evaluation of sentences synthesized by our approach is then described, which points out both pros and cons of the method and highlights some issues in the very principles of the eLite system. MOTS-CLÉS : synthèse NUU, phrases interrogatives, variations prosodiques.

Variations prosodiques en synthèse par sélection d'unités : l'exemple des phrases interrogatives 2012 Actes De La Conférence Conjointe Jep-Taln-Recital Variations prosodiques en synthèse par sélection d'unités : l'exemple des phrases interrogatives JEP Grenoble12012NUU text-to-speech synthesisinterrogative sentencesprosodic variations 161 Cet article propose une méthode automatique d'augmentation des variations prosodiques en synthèse par sélection d'unités. Plus particulièrement, nous nous sommes intéressés à la synthèse de phrases interrogatives au sein du système de synthèse eLite, qui procède par sélection d'unités non uniformes et qui ne possède pas les unités nécessaires à la production de questions dans sa base de données. L'objectif de ce travail a été de pouvoir produire des interrogatives via ce système de synthèse, sans pour autant enregistrer une nouvelle base de données pour la sélection des unités. Après avoir décrit les phénomènes syntaxiques et prosodiques en jeu dans l'énonciation de phrases interrogatives, nous présentons la méthode développée, qui allie pré-traitement des cibles à rechercher dans la base de données, et post-traitement du signal de parole lorsqu'il a été généré. Une évaluation perceptive des phrases synthétisées via notre application nous a permis de percevoir l'intérêt du post-traitement en synthèse et de pointer les précautions qu'un tel traitement implique.ABSTRACTProsodic variations in unit-based speech synthesis: the example of interrogative sentencesThis paper proposes an automatic method to increase the number of possible prosodic variations in non-uniform unit-based speech synthesis. More specifically, we are interested in the production of interrogative sentences through the eLite text-to-speech synthesis system, which relies on the selection of non-uniform units, but does not have interrogative units in its speech database. The purpose of this work was to make the system able to synthesize interrogative sentences without having to record a new, interrogative database. After a study of the syntactic and prosodic phenomena involved in the production of interrogative sentences, we present our two-step method: an adapted pre-processing of the unit selection itself, and a post-processing of the whole speech signal built by the system. A perceptual evaluation of sentences synthesized by our approach is then described, which points out both pros and cons of the method and highlights some issues in the very principles of the eLite system. MOTS-CLÉS : synthèse NUU, phrases interrogatives, variations prosodiques. Introduction De nos jours, la synthèse par sélection d'unités non-uniformes (Non-Uniform Units, NUU) reste la plus commercialisée. Ce succès, elle le doit au naturel de la parole qu'elle produit, résultat de son principe fondateur : choisir dans une base de données les unités de parole les plus proches de la mélodie à produire afin de les modifier le moins possible par traitement du signal. À toute médaille, cependant, son revers : en synthèse NUU, les variations prosodiques de la parole de synthèse sont limitées aux variations présentes dans la base de parole utilisée. Plusieurs travaux ont d'ailleurs proposé d'enrichir les bases de données utilisées. Soit en enregistrant une base de données par style (émotion, expression, etc.) à produire : dans ce cas, le système choisit d'abord la base correspondant le mieux au style désiré, avant de réaliser la sélection des unités de parole (Kawanami et al., 2000;Iida et al., 2003). Soit en rassemblant les styles au sein d'une seule et même base : dans ce cas, l'étiquetage des unités de parole est enrichi de caractéristiques faisant référence au style, et l'algorithme de sélection est modifié pour en tenir compte (Strom et al., 2006;Syrdal et Kim, 2008). L'enrichissement des bases a cependant deux inconvénients : la taille des bases obtenues, et la nécessité de disposer du même locuteur lorsque de nouveaux enregistrements sont nécessaires. Afin de devoir éviter d'enrichir les bases de données, Roekhaut et al. (2010) ont proposé de modifier le système de synthèse lui-même, en intervenant en amont et en aval de la sélection des unités. En amont, en modifiant les valeurs de l'étiquetage à rechercher dans la base de données. En aval, en post-traitant le signal obtenu pour accentuer les caractéristiques prosodiques désirées. Le résultat est une parole effectivement expressive, mais parfois dégradée par le post-traitement réalisé. Nous nous inscrivons dans la continuité directe des travaux de Roekhaut et al. (2010). Notre objectif est d'apporter des réponses aux questions que leurs résultats avaient suscitées. Pour ce faire, nous sommes partis d'un cas précis : celui de la synthèse de phrases interrogatives à partir d'une base de données de parole exclusivement déclarative. L'étude est donc spécifique, mais a été menée avec la volonté de proposer des résultats applicables à d'autres types de variations prosodiques. La suite de cet article s'articule comme suit. Après avoir présenté en section 2 le système de synthèse concerné, nous analysons en section 3 le comportement prosodique des questions. Sur cette base, nous décrivons en section 4 les traitements mis en place pour synthétiser des interrogatives à partir d'unités déclaratives. Nous évaluons ensuite la méthode en section 5, et présentons, en section 6, les réflexions que nos résultats suscitent concernant l'étiquetage de la base de données du système. eLite-LiONS eLite, prononcé [ i l a j t ], est un système complet de synthèse de la parole à partir du texte développé à Multitel ASBL 1 de 2001 à 2008 et maintenu au CENTAL depuis. Le système comprend un module de traitement automatique du langage naturel (Beaufort et Ruelle, 2006), qui construit une représentation phonético-prosodique du texte, un module de sélection NUU, LiONS (Colotte et Beaufort, 2005), qui exploite cette représentation pour choisir dans une base de données les unités de parole à concaténer, et un module de traitement du signal, qui concatène les unités sélectionnées en se limitant à un lissage de leurs frontières par Copy-OLA (Bozkurt et al., 2004). L'algorithme LiONS. L'unité de sélection utilisée par LiONS est le diphone 2 . La séquence de phonèmes de la phrase à prononcer est donc convertie en une séquence de diphones. Chaque diphone se voit associer une liste de critères de sélection linguistiques, qui sont calculés au niveau 1. Centre de recherche belge situé à Mons, Hainaut, Belgique. 2. Le diphone est une unité acoustique qui s'étend de la partie stable d'un phonème à la partie stable du phonème suivant. Cette unité englobe donc la phase de coarticulation entre phonèmes, si difficile à modéliser. de la syllabe à laquelle il appartient. Les diphones aux frontières de syllabes ou de groupes reçoivent des caractéristiques particulières. L'ensemble « diphone-critères » constitue une cible, pour laquelle on recherche des candidats dans la base de données. Lorsque des candidats ont été trouvés pour chaque cible, la meilleure séquence de candidats est sélectionnée en optimisant un double coût « cible-concaténation ». Le coût de concaténation est une mesure de la distance acoustique entre les candidats de deux cibles différentes amenées à être concaténées dans le signal de parole. Le coût cible est la distance d'un candidat par rapport à sa cible et dépend des critères de sélection utilisés. L'objectif de ces critères est avant tout de permettre au système de distinguer les unités toniques et proéminentes des autres, atones et non-proéminentes. Pendant longtemps, cette distinction a été exclusivement réalisée sur la base de valeurs acoustiques : F0, durée, spectre et énergie (Black et Campbell, 1995;Balestri et al., 1999). LiONS appartient à une deuxième génération de systèmes NUU, qui ont remplacé les critères acoustiques par des critères linguistiques afin d'autoriser plus de variations dans la courbe prosodique. Initialement, LiONS utilisait 40 critères linguistiques pour décrire une cible. De nombreux tests on ensuite permis de réduire cette liste à 4 critères, tous calculés au niveau de la syllabe à laquelle appartient le diphone : 1. la structure syllabique : V, CV, VC, CVC, etc. (où V=voyelle et C=consonne) ; 2. l'accent syllabique : primaire (AP), secondaire (AS) ou non accentué (NA) ; 3. la position de la syllabe dans le groupe rythmique (GR). Le GR est une notion propre à eLite. Il s'agit d'un groupe de souffle portant un léger accent sur sa première syllabe (BOG), un accent marqué sur sa dernière syllabe (EOG) et susceptible d'être suivi d'une pause. Le GR est constitué d'une ou de plusieurs unités grammaticales ; 4. la position de la syllabe par rapport à la pause courte (SH), moyenne (MD) ou longue (LG). Une syllabe devant la pause est toujours proéminente, mais son contour intonatif varie selon le type de pause : légèrement montant devant SH et MD, il devient descendant devant LG. L'exemple suivant illustre, sur un énoncé simple, les critères linguistiques calculés par LiONS à partir de l'analyse linguistique produite par eLite : Analyse Mots Aujourd'hui , il fait froid . linguistique Syllabes o'' Z u K d 4 i' _ i l f E' f K w a' _ (eLite) GR GR1 GR2 Critères (1) V CVC CV VC CV CV linguistiques (2) AS NA AP NA AP AP (LiONS) (3) BOG EOG BOG EOG (4) SH LG Comportement prosodique des interrogatives Typologie. Il existe de nombreuses typologies syntaxiques de la question. Dans le cadre de cette étude, nous avons décidé de rassembler les interrogatives selon les 4 classes suivantes : 1. Les questions partielles : l'interrogation porte sur un élément particulier de la phrase, qui est représenté par un mot interrogatif (Où allons-nous ?) ; 2. Les questions totales : l'interrogation porte sur la totalité de la phrase, qui appelle une réponse de type oui/non (Vous avez bien dormi ?) ; 3. Les questions alternatives : un choix entre plusieurs possibilités équivalentes et acceptables est proposé à l'interlocuteur (Tu veux du thé ou du café ?) ; 4. Les demandes de continuation : l'interrogation ne porte pas sur un élément de l'énoncé, mais pousse l'interlocuteur à poursuivre son développement (Et alors ?, Ah bon ?, C'est-à-dire ?). Nous avons également réalisé un recensement des marqueurs syntaxiques de la question, et de la façon dont ils peuvent se combiner avec les quatre classes ci-dessus (voir figure 1). Le marquage syntaxique peut consister en : A. un mot (déterminant, adverbe ou pronom) interrogatif ; B. un « tag » en fin d'énoncé, par exemple n'est-ce pas, marquant une demande de confirmation établie par le locuteur (Grundstorm, 1973) ; C. la locution est-ce que ; D. une inversion sujet-verbe ; E. aucun marqueur : l'énoncé est déclaratif, mais présente un point d'interrogation à l'écrit. Corpus. Sur cette base, nous avons constitué un corpus d'interrogatives pouvant être, de manière univoque, transcrites et ponctuées à l'aide d'un point d'interrogation. Afin de faciliter l'identification des comportements prosodiques propres à chaque classe de notre typologie, nous avons arrêté notre choix sur 123 énoncés relativement stéréotypés, provenant d'un CD audio d'exercices destinés à des apprenants du français langue seconde (Berthet et al., 2006). Chaque énoncé a été manuellement classé dans notre typologie, puis a été transcrit phonétiquement et aligné avec le signal de parole au moyen de Praat, un logiciel libre d'annotation et de manipulation de données orales (Boersma et Weenink, 2011), et son module EasyAlign (Goldman, 2011). Les énoncés étudiés ont enfin été soumis à une analyse prosodique afin de produire un prosogramme (Mertens, 2004), représentation graphique du contour prosodique d'un énoncé, basée sur les valeurs de hauteur de chaque noyau syllabique exprimées en demi-tons. Ceci nous a permis d'observer, de manière systématique, les valeurs de hauteur accordées aux syllabes finales, ainsi qu'aux syllabes des éventuels marqueurs syntaxiques. Analyse. Les comportements prosodiques observés sur notre corpus, illustrés sur les exemples de la figure 1, confirment plusieurs théories linguistiques (Delattre, 1966;Léon et Léon, 2007). En finale, seules les interrogatives de forme déclarative sont obligées de monter du fait de l'absence de tout marqueur syntaxique (Ex. 2.E, 4.E). À l'inverse, une montée en finale en présence d'un marqueur syntaxique se perçoit comme redondante et n'est pas obligatoire (Ex. marqués syntaxiquement par A, C ou D). Enfin, une montée est fréquente sur la dernière syllabe d'un mot interrogatif (Ex. marqués syntaxiquement par A), quelle que soit sa position dans l'énoncé. La littérature (Vion et al., 2002;Fónagy, 2003) signale également que dans le cas d'une alternative, le premier terme serait montant et le second, descendant (3.C, 3.D, 3.E). Sans questions alternatives dans notre corpus, nous n'avons pu vérifier cette hypothèse. Par contre, notre corpus nous a permis d'observer que dans le cas d'énoncés marqués par un tag en finale, une descente et une courte pause précèdent toujours le tag, tandis qu'une montée prend place sur la dernière syllabe du tag (Ex. 2B). Traitements appliqués aux interrogatives La base de données de parole manipulée par eLite-LiONS est constituée de 56 000 diphones provenant exclusivement de phrases déclaratives. Puisqu'elle ne contient pas d'unité proéminente et à contour mélodique ascendant en finale d'énoncé, cette base n'est donc pas adaptée telle quelle à la modélisation de certaines interrogatives, notamment celles qui ne sont pas marquées par la syntaxe et qui doivent alors obligatoirement monter en finale. Pour tendre vers l'interrogation, à l'instar de Roekhaut et al. (2010), nous intervenons par un pré-traitement en amont et un posttraitement en aval de la sélection des unités. Le pré-traitement doit permettre de choisir des unités dont la courbe prosodique, montante ou descendante, va dans le sens désiré. Le post-traitement doit accentuer cette tendance naturelle, la rendre audible et distinguable. Pré-traitement. Le principe est de modifier les valeurs des critères linguistiques des cibles à rechercher dans la base de données. Selon le type de question, deux modifications peuvent s'envisager. 1) Soit, nous avons besoin d'une intonation montante et proéminente là où une déclarative serait naturellement plate ou descendante. Dans une phrase déclarative, les seules syllabes de ce type se situent à l'endroit d'une continuation majeure : une pause courte, correspondant dans le texte à une virgule. Nous avons forcé le système à sélectionner ces unités en imposant au critère linguistique « distance par rapport à la pause » la valeur « devant pause courte ». Cette modification a pu être appliquée à tous les cas où notre analyse prosodique a mis en évidence la nécessité d'une intonation montante, sauf à la dernière syllabe d'un mot interrogatif, parce que le mot interrogatif n'est jamais suivi d'une pause, contrairement à l'unité que nous aurions voulu lui substituer. Dans ce cas précis, seul le post-traitement décrit ci-dessous a pu être appliqué. 2) Soit, nous avons besoin d'une intonation descendante là où une déclarative serait naturellement ascendante. Dans une déclarative, cette intonation se retrouve typiquement en finale de phrase. Ici, nous avons forcé le système à sélectionner ces unités en imposant au critère linguistique « distance par rapport à la pause » la valeur « devant pause longue ». Ce cas ne concerne que les interrogatives terminées par un tag, dont la syllabe précédant le tag est caractérisée par une descente et une légère pause. Post-traitement. L'analyse des prosogrammes de nos énoncés interrogatifs nous a permis de fixer à 3.4 demi-tons la différence de hauteur moyenne entre la syllabe montante de l'interrogative et la syllabe qui la précède. Pour obtenir cette différence de hauteur entre les syllabes concernées des interrogatives générées par eLite-LiONS, nous avons utilisé l'algorithme de synthèse PSOLA (Moulines et Charpentier, 1990) implémenté dans Praat. La figure 2 illustre l'évolution de la courbe prosodique d'un énoncé subissant successivement les pré-et post-traitements décrits ci-dessus. Évaluation Vingt-et-un évaluateurs ont réalisé deux tests perceptifs en ligne 3 afin d'évaluer trois aspects des phrases interrogatives produites : le naturel du signal (les traitements appliqués altèrent-ils la qualité du signal ?), la force de l'intonation interrogative (les questions générées sont-elles bien perçues comme telles ?) et, enfin, le naturel de l'intonation interrogative (le résultat prosodique obtenu est-il perçu comme naturel ?). Naturel du signal : Pour chacun des 6 énoncés proposés, les évaluateurs devaient indiquer s'ils préféraient avec ou sans post-traitement. Lorsque l'étape de pré-traitement a sélectionné des unités fortement proéminentes pour la dernière syllabe de l'énoncé, le post-traitement a accentué cette proéminence et provoqué une sortie du registre de la voix de la base de données : la majorité des participants a alors préféré les énoncés non post-traités. À l'inverse, lorsque l'étape de pré-traitement a été correctement réalisée, le post-traitement n'a pas été rejeté. Force de l'intonation interrogative : Les évaluateurs devaient détecter les 11 interrogatives de forme déclarative dans un ensemble de 16 énoncés comptant 5 vraies déclaratives. l'intonation La force d'interrogation de chaque énoncé a été évaluée sur une échelle de 1 à 5. Le test est concluant : la grande majorité des questions générées par notre application sont comprises comme telles. Seules 3 interrogatives sur 11 ont résisté à nos traitements : leur intonation normalement montante ne s'est pas montrée aussi marquée que prévu. Elles n'ont de ce fait pas été perçues comme interrogatives. Nous revenons sur les causes possibles de cette irrégularité en section 6. Naturel de l'intonation : Questions marquées syntaxiquement. Nous l'avons mentionné en section 3, une montée finale dans le cas de questions marquées syntaxiquement peut être perçue comme redondante. Dans ce cas précis, nous avons demandé aux évaluateurs de choisir entre 3 versions différentes des mêmes 6 questions marquées : une version sans traitement, une version avec pré-traitement uniquement, et une version avec pré-et post-traitement. Globalement, la majorité des évaluateurs préfèrent la version uniquement pré-traitée, dont la légère montée permet d'insister suffisamment sur la question, tout en évitant les éventuelles dégradations dues au post-traitement. Questions marquées par un mot interrogatif. Nous rappelons que la montée normalement attendue sur les mots interrogatifs (section 3) ne peut être produite que par post-traitement (section 4). Au vu des résultats du test perceptif réalisé sur 22 énoncés, cette limite n'est cependant pas gênante : les 10 questions marquées par un mot interrogatif dont la mélodie n'était montante qu'en finale ont été jugées plus naturelles, ou sans différence perceptible avec les énoncés où la mélodie était également montante sur le mot interrogatif. Ce résultat s'explique sans doute par la difficulté que nous avons eue à produire une montée significative préalable sur les mots interrogatifs. Questions taguées. Sur la base de notre corpus, nous avons constaté la nécessité d'une descente mélodique sur la dernière syllabe du mot précédant le tag, suivie d'une légère pause (section 3). Le test perceptif, réalisé sur 2 énoncés, a validé cette observation : des 2 courbes intonatives, les participants ont systématiquement préféré celle qui présentait un contour déclaratif descendant avant le tag. Le pré-traitement est donc pertinent. Questions alternatives. Pour 3 énoncés, les évaluateurs ont départagé 2 intonations : la première, avec une montée sur le premier terme de la question uniquement, l'autre, avec une montée en finale également. La finale d'une question alternative ne correspond pourtant pas toujours au focus de la question : dans "Il est en avril ou en septembre ton examen le plus difficile ?", une montée finale semblerait mal venue. Cet exemple met au jour la nécessité de repérer le focus de l'interrogative avant d'en prédire la prosodie. Pourtant, la majorité des évaluateurs ont apprécié la montée en finale, même lorsqu'elle ne correspondait pas au focus. Ceci est peut-être dû au fait que le pré-traitement décrit est impossible à appliquer au premier terme des alternatives, qui ne profite de ce fait que du post-traitement, marquant alors moins bien la question. Portée de l'ascendance finale. L'objectif était de déterminer le meilleur nombre de syllabes sur lequel réaliser la montée en finale. Les utilisateurs ont dû choisir entre deux versions de 6 énoncés : la première avec une montée sur la dernière syllabe, l'autre avec une montée sur les trois dernières syllabes. La majorité des réponses obtenues ne font aucune différence entre les deux intonations proposées, ou sont favorables à la montée sur la dernière syllabe uniquement. La montée sur les trois dernières syllabes n'a été préférée que dans le cas d'énoncés où le post-traitement a provoqué une sortie du registre de la voix de synthèse, du fait de la sélection en amont d'unités fortement proéminentes. La diffusion de la montée sur les trois dernières syllabes de l'énoncé permet alors, sans doute, de réduire cette proéminence exagérée. Conclusions et perspectives Les variations prosodiques de la synthèse par sélection d'unités sont par nature limitées aux variations présentes dans les bases de données de parole utilisées. C'est en partant de ce constat que de nombreux chercheurs ont proposé diverses méthodes pour enrichir les bases en question. Cependant, l'enrichissement des bases est coûteux, et lie dans le temps le système de synthèse à la disponibilité de la voix utilisée. Afin d'éviter ces désagréments, et en partant du cas particulier des interrogatives, cet article a proposé une méthode alliant pré-et post-traitement de la sélection d'unités pour augmenter les possibilités prosodiques de la base. Le pré-traitement permet de choisir des unités présentant la tendance prosodique souhaitée, tandis que le post-traitement accentue cette tendance pour la rendre audible. L'évaluation perceptive que nous avons réalisée a montré que dans l'ensemble, les variations prosodiques obtenues par notre approche étaient audibles, reconnaissables et naturelles. Cependant, l'évaluation a également mis au jour un point important : la qualité et la pertinence du posttraitement réalisé dépendent directement des caractéristiques acoustiques de l'unité traitée. Si l'unité à traiter est déjà aux limites du registre de la voix de la base de données, le post-traitement peut l'en faire sortir et dégrader le signal de manière audible. À l'inverse, si l'unité à traiter ne possède pas la tendance prosodique recherchée (ici, une montée ou une descente), le posttraitement n'a aucune efficacité. Cette inefficacité du post-traitement a également été constatée lorsque l'unité à traiter, proéminente dans son contexte initial, ne l'est plus ou pas assez dans le contexte de la phrase de synthèse. Ce dernier constat est très important, parce qu'il remet en cause le bienfondé du recours à des critères purement linguistiques pour décrire les cibles à sélectionner. Ces critères sont-ils suffisants pour distinguer les unités qui, hors de leur contexte initial, conserveront un comportement prosodique donné ? Nous n'en sommes pas certains. Au contraire, ces résultats semblent indiquer que des valeurs acoustiques restent somme toute nécessaires pour obtenir un signal de parole où l'alternance entre syllabes proéminentes et non proéminentes respecte les standards de la langue. FIGURE 1 - 1Typologie syntaxique et prosodique des interrogatives FIGURE 2 - 2Illustration de l'application des pré-et post-traitements . Disponibles sur http ://cental.fltr.ucl.ac.be/testperceptif2/ et sur http ://cental.fltr.ucl.ac.be/testperceptif3/. Choose the best to modify the least : A new generation concatenative synthesis system. M Balestri, A Pacchiotti, S Quazza, P Salza, S Sandri, Proceedings of Eurospeech. EurospeechBudapest, HungaryBALESTRI, M., PACCHIOTTI, A., QUAZZA, S., SALZA, P. et SANDRI, S. (1999). 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A Iida, N Campbell, F Higuchi, M Yasumura, Speech Communication. 401IIDA, A., CAMPBELL, N., HIGUCHI, F. et YASUMURA, M. (2003). A corpus-based speech synthesis system with emotion. Speech Communication, 40(1):161-187. Designing speech database with prosodic variety for expressive tts system. H Kawanami, T Masuda, T Toda, K Shikano, Proceedings of LRE. LREKAWANAMI, H., MASUDA, T., TODA, T. et SHIKANO, K. (2000). Designing speech database with prosodic variety for expressive tts system. In Proceedings of LRE. La prononciation du français. M Léon, P Léon, Armand CollinLÉON, M. et LÉON, P. (2007). La prononciation du français. Armand Collin. Un outil pour la transcription de la prosodie dans les corpus oraux. P Mertens, Traitement Automatique des langues. 452MERTENS, P. (2004). Un outil pour la transcription de la prosodie dans les corpus oraux. Traitement Automatique des langues, 45(2):109-130. Pitch-synchronous waveform processing techniques for text-to-speech synthesis using diphones. 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Graph-based Approaches for Organization Entity Resolution in MapReduce

Entity Resolution is the task of identifying which records in a database refer to the same entity. A standard machine learning pipeline for the entity resolution problem consists of three major components: blocking, pairwise linkage, and clustering. The blocking step groups records by shared properties to determine which pairs of records should be examined by the pairwise linker as potential duplicates. Next, the linkage step assigns a probability score to pairs of records inside each block. If a pair scores above a user-defined threshold, the records are presumed to represent the same entity. Finally, the clustering step turns the input records into clusters of records (or profiles), where each cluster is uniquely associated with a single real-world entity. This paper describes the blocking and clustering strategies used to deploy a massive database of organization entities to power a major commercial People Search Engine. We demonstrate the viability of these algorithms for large data sets on a 50-node hadoop cluster.

Graph-based Approaches for Organization Entity Resolution in MapReduce Association for Computational LinguisticsCopyright Association for Computational Linguistics18 October 2013. 2013 Hakan Kardes hkardes@inome.com inome Inc. Bellevue WAUSA Deepak Konidena dkonidena@inome.com inome Inc. Bellevue WAUSA Siddharth Agrawal sagrawal@inome.com inome Inc. Bellevue WAUSA Micah Huff mhuff@inome.com inome Inc. Bellevue WAUSA Ang Sun asun@inome.com inome Inc. Bellevue WAUSA Graph-based Approaches for Organization Entity Resolution in MapReduce Proceedings of the TextGraphs-8 Workshop the TextGraphs-8 WorkshopSeattle, Washington, USAAssociation for Computational Linguistics18 October 2013. 2013 Entity Resolution is the task of identifying which records in a database refer to the same entity. A standard machine learning pipeline for the entity resolution problem consists of three major components: blocking, pairwise linkage, and clustering. The blocking step groups records by shared properties to determine which pairs of records should be examined by the pairwise linker as potential duplicates. Next, the linkage step assigns a probability score to pairs of records inside each block. If a pair scores above a user-defined threshold, the records are presumed to represent the same entity. Finally, the clustering step turns the input records into clusters of records (or profiles), where each cluster is uniquely associated with a single real-world entity. This paper describes the blocking and clustering strategies used to deploy a massive database of organization entities to power a major commercial People Search Engine. We demonstrate the viability of these algorithms for large data sets on a 50-node hadoop cluster. Introduction A challenge for builders of databases whose information is culled from multiple sources is the detection of duplicates, where a single real-world entity gives rise to multiple records (see (Elmagarmid, 2007) for an overview). Entity Resolution is the task of identifying which records in a database refer to the same entity. Online citation indexes need to be able to navigate through the different capitalization and abbreviation conventions that appear in bibliographic entries. Government agencies need to know whether a record for "Robert Smith" living on "Northwest First Street" refers to the same person as one for a "Bob Smith" living on "1st St. NW". In a standard machine learning approach to this problem all records first go through a cleaning process that starts with the removal of bogus, junk and spam records. Then all records are normalized to an approximately common representation. Finally, all major noise types and inconsistencies are addressed, such as empty/bogus fields, field duplication, outlier values and encoding issues. At this point, all records are ready for the major stages of the entity resolution, namely blocking, pairwise linkage, and clustering. Since comparing all pairs of records is quadratic in the number of records and hence is intractable for large data sets, the blocking step groups records by shared properties to determine which pairs of records should be examined by the pairwise linker as potential duplicates. Next, the linkage step assigns a score to pairs of records inside each block. If a pair scores above a user-defined threshold, the records are presumed to represent the same entity. The clustering step partitions the input records into sets of records called profiles, where each profile corresponds to a single entity. In this paper, we focus on entity resolution for the organization entity domain where all we have are the organization names and their relations with individuals. Let's first describe the entity resolution for organization names, and discuss its significance and the challenges in more detail. Our process starts by collecting billions of personal records from three sources of U.S. records to power a major commercial People Search Engine. Example fields on these records might include name, address, birthday, phone number, (encrypted) social security number, relatives, friends, job title, universities attended, and organizations worked for. Since the data sources are heterogeneous, each data source provides different aliases of an organization including abbreviations, preferred names, legal names, etc. For example, Person A might have both "Microsoft", "Microsoft Corp", "Microsoft Corporation", and "Microsoft Research" in his/her profile's organization field. Person B might have "University of Washington", while Person C has "UW" as the organization listed in his/her profile. Moreover, some organizations change their names, or are acquired by other in-stitutions and become subdivisions. There are also many organizations that share the same name or abbreviation. For instance, both "University of Washington", "University of Wisconsin Madison", "University of Wyoming" share the same abbreviation, "UW". Additionally, some of the data sources might be noisier than the others and there might be different kind of typos that needs to be addressed. Addressing the above issues in organization fields is crucial for data quality as graphical representations of the data become more popular. If we show different representations of the same organization as separate institutions in a single person's profile, it will decrease the confidence of a customer about our data quality. Moreover, we should have a unique representation of organizations in order to properly answer more complicated graph-based queries such as "how am I connected to company X?", or "who are my friends that has a friend that works at organization X, and graduated from school Y?". We have developed novel and highly scalable components for our entity resolution pipeline which is customized for organizations. The focus of this paper is the graph-based blocking and clustering components. In the remainder of the paper, we first describe these components in Section 2. Then, we evaluate the performance of our entity resolution framework using several real-world datasets in Section 3. Finally, we conclude in Section 4. Methodology In this section, we will mainly describe the blocking and clustering strategies as they are more graph related. We will also briefly mention our pairwise linkage model. The processing of large data volumes requires highly scalable parallelized algorithms, and this is only possible with distributed computing. To this end, we make heavy use of the hadoop implementation of the MapReduce computing framework, and both the blocking and clustering procedures described here are implemented as a series of hadoop jobs written in Java. It is beyond the scope of this paper to fully describe the MapReduce framework (see (Lin, 2010) for an overview), but we do discuss the ways its constraints inform our design. MapReduce divides computing tasks into a map phase in which the input, which is given as (key,value) pairs, is split up among multiple machines to be worked on in parallel and a reduce phase in which the output of the map phase is put back together for each key to independently process the values for each key in parallel. Moreover, in a MapReduce context, recursion becomes iteration. Blocking How might we subdivide a huge number of organizations based on similarity or probability scores when all we have is their names and their relation with people? We could start by grouping them into sets according to the words they contain. This would go a long way towards putting together records that represent the same organization, but it would still be imperfect because organizations may have nicknames, abbreviations, previous names, or misspelled names. To enhance this grouping we could consider a different kind of information like soundex or a similar phonetic algorithm for indexing words to address some of the limitations of above grouping due to typos. We can also group together the organizations which appear in the same person's profile. This way, we will be able to block the different representations of the same organization to some extent. With a handful of keys like this we can build redundancy into our system to accommodate different types of error, omission, and natural variability. The blocks of records they produce may overlap, but this is desirable because it gives the clustering a chance to join records that blocking did not put together. The above blocks will vary widely in size. For example, we may have a small set of records containing the word "Netflix" which can then be passed along immediately to the linkage component. However, we may have a set of millions of records containing the word "State" which still needs to be cut down to subsets with manageable sizes, otherwise it will be again impractical to do all pairwise computations in this block. One way to do this is to find other common properties to further subdivide this set. The set of all records containing not only "State" but also a specific state name like "Washington" is smaller than the set of all records containing the word "State", and intuitively records in this set will be more likely to represent the same organization. Additionally we could block together all the "State" records with the same number of words, or combination of the initials of each word. As with the original blocks, overlap between these sub-blocks is desirable. We do not have to be particularly artful in our choice of sub-blocking criteria: any property that seems like it might be individuating will do. As long as we have an efficient way to search the space, we can let the data dynamically choose different sub-blocking strategies for each oversize block. To this end, we use the ordering on block keys to define a binomial tree where each node contains a list of block keys and is the parent of nodes that have keys that come later in the ordering appended to the list. Figure 1 shows a tree for the oversize top-level set tTkn1 with three subblocking tokens sTkn1 < sTkn2 < sTkn3. With each node of the tree we can associate a block whose key is the list of blocks keys in that node and whose records are the intersection of the records in those blocks, e.g. the tTkn1 ∩ sTkn1 ∩ sTkn2 node represents all the records for organizations containing all these tokens. Because the cardinality of an intersected set is less than or equal to the cardinalities of the sets that were intersected, every block Figure 1: The root node of this tree represents an oversized block for the name Smith and the other nodes represent possible sub-blocks. The sub-blocking algorithm enumerates the tree breadth-first, stopping when it finds a correctly-sized sub-block. in the tree is larger than or equal to any of its children. We traverse the tree breadth-first and only recurse into nodes above the maximum block size. This allows us to explore the space of possible sub-blocks in cardinality order for a given branch, stopping as soon as we have a small enough sub-block. tTkn1 tTkn1 ∩ sTkn3 tTkn1 ∩ sTkn2 tTkn1 ∩ sTkn2 ∩ sTkn3 tTkn1 ∩ sTkn1 tTkn1 ∩ sTkn1 ∩ sTkn3 tTkn1 ∩ sTkn1 ∩ sTkn2 tTkn1 ∩ sTkn1 ∩ sTkn2 ∩ sTkn3 The algorithm that creates the blocks and sub-blocks takes as input a set of records and a maximum block size M . All the input records are grouped into blocks defined by the top-level properties. Those top-level blocks that are not above the maximum size are set aside. The remaining oversized blocks are partitioned into sub-blocks by sub-blocking properties that the records they contain share, and those properties are appended to the key. The process is continued recursively until all sub-blocks have been whittled down to an acceptable size. The pseudo code of the blocking algorithm is presented in Figure 2. We will represent the key and value pairs in the MapReduce framework as < key; value >. The input organization records are represented as < IN P U T F LAG, ORG NAME >. For the first iteration, this job takes the organization list as input. In later iterations, the input is the output of the previous blocking iteration. In the first iteration, the mapper function extracts the top-level and sub-level tokens from the input records. It combines the organization name and all the sub-level tokens in a temp variable called newV alue. Next, for each top-level token, it emits this top-level token and the newValue in the following format: < topT oken, newV alue >. For the later iterations, it combines each sub level token with the current blocking key, and emits them to the reducer. Also note that the lexicographic ordering of the block keys allows separate mapper processes to work on different nodes in a level of the binomial tree without creating redundant subblocks (e.g. if one mapper creates a International ∩ Business ∩ Machines block another mapper will not create a International ∩ Machines ∩ Business one). This is necessary because individual MapReduce jobs run independently without shared memory or other runtime communication mechanisms. In the reduce phase, all the records will be grouped together for each block key. The reducer function iterates over all the records in a newly-created sub-block, counting them to determine whether or not the block is small enough or needs to be further subdivided. The blocks that the reducer deems oversized become inputs to the next iteration. Care is taken that the memory requirements of the reducer function are constant in the size of a fixed buffer because otherwise the reducer runs out of memory on large blocks. Note that we create a black list from the high frequency words in organization names, and we don't use these as top-level properties as such words do not help us with individuating the records. More formally, this process can be understood in terms of operations on sets. In a set of N records there are 1 2 N (N − 1) unique pairs, so an enumeration over all of them is O(N 2 ). The process of blocking divides this original set into k blocks, each of which contains at most a fixed maximum of M records. The exhaustive comparison of pairs from these sets is O(k), and the constant factors are tractable if we choose a small enough M . In the worst case, all the sub-blocks except the ones with the very longest keys are oversize. Then the sub-blocking algorithm will explore the powerset of all possible blocking keys and thus have exponential runtime. However, as the blocking keys get longer, the sets they represent get smaller and eventually fall beneath the maximum size. In practice these two countervailing motions work to keep this strategy tractable. Pairwise Linkage Model In this section, we give just a brief overview of our pairwise linkage system as a detailed description and evaluation of that system is beyond the scope of this paper. We take a feature-based classification approach to predict the likelihood of two organization names < o 1 , o 2 > referring to the same organization entity. Specifically, we use the OpenNLP 1 maximum entropy (maxent) package as our machine learning tool. We choose to work with maxent because the training is fast and it has a good support for classification. Regarding the features, we mainly have two types: surface string features and context features. Examples of surface string features are edit dis- tance of the two names, whether one name is an abbreviation of the other name, and the longest common substring of the two names. Examples of context features are whether the two names share the same url and the number of times that the two names co-occur with each other in a single person record. Clustering In this section, we present our clustering approach. Let's, first clarify a set of terms/conditions that will help us describe the algorithms. Definition (Connected Component): Let G = (V, E) be an undirected graph where V is the set of vertices and E is the set of edges. C = (C 1 , C 2 , ..., C n ) is the set of disjoint connected components in this graph where (C 1 ∪ C 2 ∪ ... ∪ C n ) = V and (C 1 ∩ C 2 ∩ ... ∩ C n ) = ∅. For each connected component C i ∈ C, there exists a path in G between any two vertices v k and v l where (v k , v l ) ∈ C i . Additionally, for any distinct connected component (C i , C j ) ∈ C, there is no path between any pair v k and v l where v k ∈ C i , v l ∈ C j . Moreover, the problem of finding all connected components in a graph is finding the C satisfying the above conditions. Definition (Component ID): A component id is a unique identifier assigned to each connected component. Definition (Max Component Size): This is the maximum allowed size for a connected component. Definition (Cluster Set): A cluster set is a set of records that belong to the same real world entity. Definition (Max Cluster Size): This is the maximum allowed size for a cluster. Definition (Match Threshold): Match threshold is a score where pairs scoring above this score are said to represent the same entity. Definition (No-Match Threshold): No-Match threshold is a score where pairs scoring below this score are said to represent different entities. Definition (Conflict Set): Each record has a conflict set which is the set of records that shouldn't appear with this record in any of the clusters. The naive approach to clustering for entity resolution is transitive closure by using only the pairs having scores above the match threshold. However, in practice we might see many examples of conflicting scores. For example, (a,b) and (b,c) pairs might have scores above match threshold while (a,c) pair has a score below nomatch threshold. If we just use transitive closure, we will end up with a single cluster with these three records (a,b,c). Another weakness of the regular transitive closure is that it creates disjoint sets. However, organizations might share name, or abbreviation. So, we need a soft clustering approach where a record might be in different clusters. On the other hand, the large volume of our data requires highly scalable and efficient parallelized algorithms. However, it is very hard to implement par- Figure 4: Transitive Closure Component allelized clustering approaches with high precision for large scale graphs due to high time and space complexities (Bansal, 2003). So, we propose a two-step approach in order to build both a parallel and an accurate clustering framework. The high-level architecture of our clustering framework is illustrated in Figure 3. We first find the connected components in the graph with our MapReduce based transitive closure approach, then further, partition each connected component in parallel with our novel soft clustering algorithm, sClust. This way, we first combine similar record pairs into connected components in an efficient and scalable manner, and then further partition each connected component into smaller clusters for better precision. Note that there is a dangerous phenomenon, black hole entities, in transitive closure of the pairwise scores (Michelson, 2009). A black hole entity begins to pull an inordinate amount of records from an increasing number of different true entities into it as it is formed. This is dangerous, because it will then erroneously match on more and more records, escalating the problem. Thus, by the end of the transitive closure, one might end up with black hole entities with millions of records belonging to multiple different entities. In order to avoid this problem, we define a black hole threshold, and if we end up with a connected component above the size of the black hole threshold, we increment the match threshold by a delta and further partition this black hole with one more transitive closure job. We repeat this process until the sizes of all the connected components are below the black hole threshold, and then apply sClust on each connected component. Hence at the end of the entire entity resolution process, the system has partitioned all the input records into cluster sets called profiles, where each profile corresponds to a single entity. Transitive Closure In order to find the connected components in a graph, we developed the Transitive Closure (TC) module shown in Figure 4. The input to the module is the list of all pairs having scores above the match threshold. As an output from the module, what we want to obtain is the mapping from each node in the graph to its corresponding com-ponentID. For simplicity, we use the smallest node id in each connected component as the identifier of that component. Thus, the module should output a mapping table from each node in the graph to the smallest node id in its corresponding connected component. To this end, we designed a chain of two MapReduce jobs, namely, TC- Iterate, and TC-Dedup, that will run iteratively till we find the corresponding componentIDs for all the nodes in the graph. TC-Iterate job generates adjacency lists AL = (a 1 , a 2 , ..., a n ) for each node v, and if the node id of this node v id is larger than the min node id a min in the adjacency list, it first creates a pair (v id , a min ) and then a pair for each (a i , a min ) where a i ∈ AL, and a i = a min . If there is only one node in AL, it means we will generate the pair that we have in previous iteration. However, if there is more than one node in AL, it means we might generate a pair that we didn't have in the previous iteration, and one more iteration is needed. Please note that, if v id is smaller than a min , we don't emit any pair. The pseudo code of TC-Iterate is given in Figure 5-(a). For the first iteration, this job takes the pairs having scores above the match threshold from the initial edge list as input. In later iterations, the input is the output of TC-Dedup from the previous iteration. We first start with the initial edge list to construct the first degree neighborhood of each node. To this end, for each edge < a; b >, the mapper emits both < a; b >, and < b; a > pairs so that a should be in the adjacency list of b and vice versa. In the reduce phase, all the adjacent nodes will be grouped together for each node. Reducers don't receive the values in a sorted order. So, we use a secondary sort approach to pass the values to the reducer in a sorted way with custom partitioning (see (Lin, 2010) for details). This way, the first value becomes the minValue. If the minValue is larger than the key, we don't emit anything. Otherwise, we first emit the < key; minV alue > pair. Next, we emit a pair for all other values as < value; minV alue >, and increase the global NewPair counter by 1. If the counter is 0 at the end of the job, it means that we found all the components and there is no need for further iterations. During the TC-Iterate job, the same pair might be emitted multiple times. The second job, TC-Dedup, just deduplicates the output of the CCF-Iterate job. This job increases the efficiency of TC-Iterate job in terms of both speed and I/O overhead. The pseudo code for this job is given in Figure 5-(b). The worst case scenario for the number of necessary iterations is d+1 where d is the diameter of the network. The worst case happens when the min node in the largest connected component is an end-point of the largest shortest-path. The best case scenario takes d/2+1 iterations. For the best case, the min node should be at the center of the largest shortest-path. sClust: A Soft Agglomerative Clustering Approach After partitioning the records into disjoint connected components, we further partition each connected component into smaller clusters with sClust approach. sClust is a soft agglomerative clustering approach, and its main difference from any other hierarchical clustering method is the "conflict set" term that we described above. Any of the conflicting nodes cannot appear in a cluster with this approach. Additionally, the maximum size of the clusters can be controlled by an input parameter. First as a preprocessing step, we have a two-step MapReduce job (see Figure 6) which puts together and sorts all the pairwise scores for each connected component discovered by transitive closure. Next, sClust job takes the sorted edge lists for each connected component as input, and partitions each connected component in parallel. The pseudo-code for sClust job is given in Figure 7. sClust iterates over the pairwise scores twice. During the first iteration, it generates the node structures, and conflict sets for each of these structures. For example, if the pairwise score for (a, b) pair is below the no-match threshold, node a is added to node b's conflict set, and vice versa. By the end of the first iteration, all the conflict sets are generated. Now, one more pass is needed to build the final clusters. Since the scores are sorted, we start from the highest score to agglomeratively construct the clusters by going over all the scores above the match threshold. Let's assume we have a pair (a, b) with a score above the match threshold. There might be 4 different conditions. First, both node a and node b are not in any of the clusters yet. In this case, we generate a cluster with these two records and the conflict set of this cluster becomes the union of conflict sets of these two records. Second, node a might already be assigned to a set of clusters C' while node b is not in any of the clusters. In these case, we add node b to each cluster in C' if it doesn't conflict with b. If there is no such cluster, we build a new cluster with nodes a and b. Third is the opposite version of the second condition, and the procedure is the same. Finally, both node a and node b might be in some set of clusters. If they already appear in the same cluster, no further action needed. If they just appear in different clusters, these clusters will be merged as long as there is no conflict between these clusters. If there are no such unconflicting clusters, we again build a new cluster with nodes a and b. This way, we go over all the scores above the match threshold and build the cluster sets. Note that if the clusters are merged, their conflict sets are also merged. Additionally, if the max cluster size parameter is defined, this condition is also checked before merging any two clusters, or adding a new node to an existing cluster. Evaluation In this section, we present the experimental results for our entity resolution framework. We ran the experiments on a hadoop cluster consisting of 50 nodes, each with 8 cores. There are 10 mappers, and 6 reducers available at each node. We also allocated 3 Gb memory for each map/reduce task. We used two different real-world datasets for our experiments. The first one is a list of 150K organizations along with their aliases provided by freebase 2 . By using this dataset, we both trained our pairwise linkage model and measured the precision and recall of our system. We randomly selected 135K organizations from this list for the training. We used the rest of the organizations to mea- sure the performance of our system. Next, we generated positive examples by exhaustively generating a pair between all the aliases. We also randomly generated equal number of negative examples among pairs of different organization alias sets. We trained our pairwise classifier with the training set, then ran it on the test set and measured its performance. Next, we extracted all the organization names from this set, and ran our entire entity resolution pipeline on top of this set. Table 1 presents the performance results. Our pairwise classifier has 97% precision and 63% recall when we use a match threshold of 0.65. Using same match threshold, we then performed transitive closure. We also measured the precision and recall numbers for transitive closure as it is the naive approach for the entity resolution problem. Since transitive closure merges records transitively, it has very high recall but the precision is just 64%. Finally, we performed our sClust approach with the same match threshold. We set the no-match threshold to 0.3. The pairwise classifier has slightly better precision than sClust but sClust has much better recall. Overall, sClust has a much better f-measure than both the pairwise classifier and transitive closure. Second, we used our production set to show the viability of our framework. In this set, we have 68M organization names. We ran our framework on this dataset. Blocking generated 14M unique blocks, and there are 842M unique comparisons in these blocks. The distribution of the block sizes presented in Figure 8-(a) and (b). Blocking finished in 42 minutes. Next, we ran our pairwise classifier on these 842M pairs and it finished in 220 minutes. Finally, we ended up with 10M clusters at the end of the clustering stage which took 3 hours. The distribution of the connected components and final clusters are presented in Figure 8-(c). Figure 2 : 2Alg.1 -Blocking Figure 3 : 3Clustering Component Figure 5 : 5Alg.3 -Transitive Closure Figure 6 : 6Alg.3 -Bring together all edges for each partition Figure 7 : 7Alg Figure 8 : 8Size Distributions Clustering map(key, valueList) for each (value ∈ valueList) if(value.score ≥ MAT CH T HR) nodes.insert(value.entity1) nodes.insert(value.entity2) else node1Index ← find(value.entity1, nodes) node2Index ← find(value.entity2, nodes) nodes[node1Index].conf lictSet.insert(node2Index) nodes[node2Index].conf lictSet.insert(node1Index) for each (value ∈ valueList) if(value.score ≥ MAT CH T HR) node1Index ← find(value.entity1, nodes) node2Index ← find(value.entity2, nodes) node1ClusIDLength ← nodes[node1Index].clusIDs.length node2ClusIDLength ← nodes[node2Index].clusIDs.length if((node1ClusIDLength = 0) && (node2ClusIDLength = 0)) clusters[numClusters].nodes[0] ← node1Index clusters[numClusters].nodes[1] ← node2Index clusters[numClusters].conf Set ← mergeSortedLists(nodes[node1Index].conf Set, nodes[node2Index].conf Set) nodes[node1Index].clusIDs.insert(numClusters) nodes[node2Index].clusIDs.insert(numClusters) numClusters++ elseif(node1ClusIDLength = 0) for each (node2ClusID ∈ nodes[node2Index].clusIDs) if(notContain(clusters[node2ClusID].conf Set, node1Index)) insertT oSortedList(clusters[node2ClusID].nodes, node1Index) clusters[node2ClusID].conf Set ← mergeSortedLists(clusters[node2ClusID].conf Set, nodes[node1Index].conf Set) nodes[node1Index].clusIDs.insert(node2ClusID) elseif(node2ClusIDLength = 0) for each (node1ClusID ∈ nodes[node1Index].clusIDs) if(notContain(clusters[node1ClusID].conf Set, node2Index)) insertT oSortedList(clusters[node1ClusID].nodes, node2Index) clusters[node1ClusID].conf Set ← mergeSortedLists(clusters[node1ClusID].conf Set, nodes[node2Index].conf Set) nodes[node2Index].clusIDs.insert(node1ClusID) elseif(notIntersect(clusters[node1ClusID].clusIDs, clusters[node2ClusID].clusIDs)) for each (node1ClusID ∈ nodes[node1Index].clusIDs) for each (node2ClusID ∈ nodes[node2Index].clusIDs) if( notIntersect(clusters[node1ClusID].conf Set, clusters[node2ClusID].nodes) && notIntersect(clusters[node2ClusID].conf Set, clusters[node1ClusID].nodes) ) clusters[node1ClusID].nodes ← mergeSortedList(clusters[node1ClusID].nodes, clusters[node2ClusID].nodes) clusters[node1ClusID].conf Set ← mergeSortedLists(clusters[node1ClusID].conf Set, clusters[node2ClusID].conf Set) for each (nodeIndex ∈ clusters[node2ClusID].nodes) nodes[nodeIndex].clusIDs.insert(node1ClusID) clusters[node2ClusID].isRemoved ← true clusters[node2ClusID].nodes ← null clusters[node2ClusID].confSet ← null 2 http://www.freebase.com/precision recall f-measure Pairwise Classifier 97 63 76 Transitive Closure 64 98 77 sClust 95 76 84 Table 1 : 1Performance Comparison http://opennlp.apache.org/ ConclusionIn this paper, we presented a novel entity resolution approach for the organization entity domain. We have implemented this in the MapReduce framework with low memory requirements so that it may scale to large scale datasets. We used two different real-world datasets in our experiments. We first evaluated the performance of our approach on truth data provided by freebase. Our clustering approach, sClust, significantly improved the recall of the pairwise classifier. Next, we demonstrated the viability of our framework on a large scale dataset on a 50-node hadoop cluster. Duplicate record detection: A survey. A K Elmagarmid, P G Iperirotis, V S Verykios, IEEE Transactions on Knowledge and Data Engineering. A. K. Elmagarmid, P. G. Iperirotis, and V. S. Verykios. Dupli- cate record detection: A survey. In IEEE Transactions on Knowledge and Data Engineering, pages 1-16, 2007. Data-Intensive Text Processing with MapReduce. J Lin, C Dyer, Synthesis Lectures on Human Langugage Technologies. Morgan & Claypool. J. Lin and C. Dyer. Data-Intensive Text Processing with MapReduce. 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Extraction de collocations et leurs équivalents de traduction à partir de corpus parallèles

Violeta.Seretan@unige.ch RÉSUMÉ. Identifier les collocations dans le texte source (par exemple, break record) et les traduire correctement (battre record contre *casser record) constituent un réel défi pour la traduction automatique, d'autant plus que ces expressions sont très nombreuses et très flexibles du point de vue syntaxique. Cet article présente une méthode permettant de repérer des équivalents de traduction pour les collocations à partir de corpus parallèles, qui sera utilisée pour augmenter la base de données lexicales d'un système de traduction. La méthode est fondée sur une approche syntaxique « profonde », dans laquelle les collocations et leurs équivalents potentiels sont extraits à partir de phrases alignées à l'aide d'un analyseur multilingue. L'article présente également les outils qui sont utilisés par cette méthode. Il se concentre en particulier sur les efforts déployés afin de rendre compte des divergences structurelles entre les langues et d'optimiser la performance de la méthode, notamment en ce qui concerne la couverture.ABSTRACT. Identifying collocations in a text (e.g., break record) and correctly translating them (battre record vs. *casser record) represent key issues in machine translation, notably because of their prevalence in language and their syntactic flexibility. This article describes a method for discovering translation equivalents for collocations from parallel corpora, aimed at increasing the lexical coverage of a machine translation system. The method is based on a "deep" syntactic approach, in which collocations and candidate translations are identified from sentence-aligned text with the help of a multilingual parser. The article also introduces the tools on which this method relies. It focuses in particular on the efforts made to account for structural divergences between languages and to improve the method's performance in terms of coverage. MOTS-CLÉS : collocations, équivalents de traduction, analyse syntaxique, alignement de texte.

Extraction de collocations et leurs équivalents de traduction à partir de corpus parallèles 2009. 2009 Violeta Seretan Laboratoire d'analyse et de technologie du langage Université de Genève 2, rue de CandolleCH-1205GenèveSuisse Extraction de collocations et leurs équivalents de traduction à partir de corpus parallèles 5012009. 2009collocationstranslation equivalentssyntactic parsingtext alignment Violeta.Seretan@unige.ch RÉSUMÉ. Identifier les collocations dans le texte source (par exemple, break record) et les traduire correctement (battre record contre *casser record) constituent un réel défi pour la traduction automatique, d'autant plus que ces expressions sont très nombreuses et très flexibles du point de vue syntaxique. Cet article présente une méthode permettant de repérer des équivalents de traduction pour les collocations à partir de corpus parallèles, qui sera utilisée pour augmenter la base de données lexicales d'un système de traduction. La méthode est fondée sur une approche syntaxique « profonde », dans laquelle les collocations et leurs équivalents potentiels sont extraits à partir de phrases alignées à l'aide d'un analyseur multilingue. L'article présente également les outils qui sont utilisés par cette méthode. Il se concentre en particulier sur les efforts déployés afin de rendre compte des divergences structurelles entre les langues et d'optimiser la performance de la méthode, notamment en ce qui concerne la couverture.ABSTRACT. Identifying collocations in a text (e.g., break record) and correctly translating them (battre record vs. *casser record) represent key issues in machine translation, notably because of their prevalence in language and their syntactic flexibility. This article describes a method for discovering translation equivalents for collocations from parallel corpora, aimed at increasing the lexical coverage of a machine translation system. The method is based on a "deep" syntactic approach, in which collocations and candidate translations are identified from sentence-aligned text with the help of a multilingual parser. The article also introduces the tools on which this method relies. It focuses in particular on the efforts made to account for structural divergences between languages and to improve the method's performance in terms of coverage. MOTS-CLÉS : collocations, équivalents de traduction, analyse syntaxique, alignement de texte. Introduction Les collocations (ou associations habituelles de mots, telles que jouer un rôle, battre un record, jeter les bases, combler une lacune, remplir une condition) constituent un sous-type d'expressions à mots multiples qui présentent un intérêt particulier dans des domaines comme l'étude des langues étrangères et la lexicographie, et qui ont attiré aussi, durant les dernières années, une attention toujours plus grande dans le domaine du traitement automatique des langues. Deux raisons majeures contribuent à cet intérêt croissant : d'une part, la présence massive des collocations dans le langage -plusieurs auteurs affirment que les collocations sont les plus nombreuses parmi les expressions à mots multiples, par exemple, (Mel'čuk, 1998), tandis qu'une étude de corpus (Howarth et Nesi, 1996) avait montré que, effectivement, toute phrase est susceptible de contenir au moins une collocation -, d'autre part, l'idiosyncrasie des collocations. Ainsi, même si les collocations sont assez semblables aux constructions régulières du langage, elles partagent certains traits des expressions figées, notamment le caractère idiomatique de l'encodage. Par exemple, l'expression battre un record est facile à décoder, son sens étant relativement transparent et compréhensible ; néanmoins, tout comme les expressions idiomatiques, elle est difficile à encoder. Le collocatif du nom record, le verbe battre, est imprédictible pour les locuteurs non natifs du français. En accord avec (Fillmore et al., 1988), nous considérons que dans les collocations, l'encodage (à la différence du décodage) est idiomatique. La conséquence de cette idiomaticité est que les collocations n'admettent généralement pas de traduction littérale vers une autre langue. Ainsi, l'équivalent anglais de battre un record est to break a record, plutôt que *to beat a record ; inversement, la collocation anglaise to break a record ne peut pas être traduite littéralement en français, comme *casser un record. L'expression allemande [eine] falsche Entscheidung treffen ('prendre une mauvaise décision', lit. [une] fausse décision rencontrer) est un autre exemple qui est particulièrement éloquent. Cette collocation à trois termes est composée de deux collocations binaires imbriquées, une de type adjectif-nom (falsche Entscheidung), l'autre de type verbe-objet (Entscheidung treffen), aucune des deux n'admettant de traduction littérale vers le français 1 . 1. Le terme collocation étant ambigu, nous tenons à préciser que nous adoptons ici son acception syntaxique (comme association de mots liés syntaxiquement), plus restrictive que son acception statistique qui est plus répandue (comme association de mots apparaissant à courte distance dans le texte). Parmi les traits principaux des collocations, nous mentionnons leur caractère récurrent, préfabriqué, arbitraire et imprédictible. Pour une discussion plus détaillée sur la définition des collocations, nous renvoyons le lecteur intéressé à (Seretan, 2008). Beaucoup moins de travaux ont en revanche été réalisés sur le traitement ultérieur des résultats d'extraction, afin de rendre possible leur intégration dans des applications clés, telles que la traduction automatique, la génération de textes, la désambiguïsation lexicale, ou l'analyse syntaxique. Parmi les exceptions, on peut citer des essais de classification sémantique des collocations (Wanner et al., 2006), de détection de synonymes pour les collocations (Wu et Zhou, 2003), de détection de traductions pour les collocations (Smadja et al., 1996 ;Lü et Zhou, 2004), ou d'utilisation des collocations pour l'analyse syntaxique (Hindle et Rooth, 1993), pour la traduction (Smadja et al., 1996 ;Lü et Zhou, 2004), ainsi que pour la génération (Heid et Raab, 1989) et la classification de textes (Williams, 2002). Il s'agit, en général, de travaux qui restent préliminaires et isolés, malgré la reconnaissance du rôle crucial que ces expressions jouent dans le traitement du langage (Sag et al., 2002), et malgré le développement soutenu des techniques d'extraction. Le travail décrit dans cet article vise à détecter des équivalents de traduction pour les collocations à partir de corpus de textes parallèles, afin de peupler le lexique d'un système de traduction multilingue. La méthode développée repose -tout comme ce système -sur une approche essentiellement syntaxique, rendue possible par le progrès réalisé dans le domaine de l'analyse syntaxique en général, et en particulier par le niveau de développement atteint par l'analyseur multilingue Fips, créé dans notre laboratoire . Les avantages de cette méthode par rapport aux travaux précédents sont, principalement, qu'elle est capable de prendre en charge des collocations plus flexibles du point de vue syntaxique (en plus des constructions plus rigides), et qu'elle est opérationnelle même pour des données de taille réduite, ou en l'absence de dictionnaires bilingues. L'article est organisé de la manière suivante. La section 2 passe en revue les travaux existants sur l'extraction d'équivalents de traduction pour les collocations, y compris les travaux connexes concernant d'autres types d'expressions. La section 3 décrit brièvement Fips, l'analyseur syntaxique qui est à la base de notre méthode d'extraction. Ensuite, dans la section 4, nous présentons cette méthode, ainsi que les différents modules de traitement qu'elle utilise -notamment, le module d'extraction des collocations à partir de corpus et le module d'alignement de phrases. La section 5 présente des résultats expérimentaux accompagnés d'une évaluation de la performance de notre méthode. La section 6 se penche sur l'analyse des erreurs en discutant, également, les possibles améliorations de la méthode. La dernière section conclut l'article en comparant notre approche aux approches existantes. Travaux précédents Le travail de (Kupiec, 1993) peut être considéré comme l'un des premiers travaux sur l'extraction, à partir de corpus, d'équivalents de traduction pour les collocations. Ce travail était centré sur des groupes nominaux comme late spring (la fin du printemps). Des correspondances bilingues sont identifiées à partir du corpus parallèle français-anglais Hansard aligné au niveau de la phrase. Les deux corpus, source et cible, sont étiquetés à l'aide d'un étiqueteur morphosyntaxique fondé sur un modèle markovien (HMM), et des groupes nominaux sont identifiés sur la base de la catégorie lexicale des mots en utilisant un reconnaisseur à états finis. Ensuite, des correspondances bilingues sont formées à l'aide d'un algorithme itératif de réestimation, Expectation Maximization (EM). La précision 2 de cette méthode, rapportée pour les 100 premières correspondances obtenues, est de 90 %. Une méthode similaire d'identification de groupes nominaux a été employée par (van der Eijk, 1993) pour la paire de langues néerlandais-anglais. L'appariement est réalisé en employant deux heuristiques principales : le groupe nominal cible est choisi en fonction i) de sa fréquence dans le sous-ensemble de phrases cibles correspondant au groupe nominal source, et ii) de la corrélation entre sa position (dans la phrase cible) et la position du groupe nominal source (dans la phrase source). Évaluée sur 1 100 correspondances sélectionnées de manière aléatoire, la méthode a atteint une précision de 68 % et une couverture 3 de 64 %. Dans le cadre du système Termight (Dagan et Church, 1994), on identifie également des équivalents bilingues pour les groupes nominaux à partir de corpus parallèles, mais en faisant appel à l'alignement des mots plutôt qu'à l'alignement des phrases. Une fois les correspondances des mots trouvées, ce système considère tout simplement la plage de mots entre les équivalents du premier et du dernier mot d'un syntagme nominal comme sa traduction potentielle. Les syntagmes sont déterminés auparavant en considérant des séquences de noms dans un corpus anglais étiqueté. Les différentes traductions potentielles obtenues pour un syntagme sont triées dans l'ordre des fréquences des têtes syntaxiques et sont affichées dans un concordancier bilingue. La précision obtenue pour les 192 correspondances anglaises-allemandes testées est de 40 %, mais les auteurs soulignent qu'en descendant dans la liste des résultats, une traduction correcte a toujours été trouvée. tions sont d'abord identifiées dans la version anglaise du Hansard à l'aide du système Xtract (Smadja, 1993). Pour trouver l'équivalent français, Champollion sélectionne les mots des phrases cibles qui sont le plus corrélés avec la collocation source. La corrélation est mesurée avec le coefficient statistique de corrélation Dice, qui prend en compte le nombre d'occurrences de deux termes simultanément dans une paire de phrases alignées, ainsi que le nombre d'occurrences total de chaque terme dans le corpus. Cette méthode nécessite une étape ultérieure qui consiste à parcourir les phrases cibles afin de déterminer l'ordre des mots de la traduction proposée, car le système ne dispose pas d'informations d'ordre syntaxique. L'évaluation effectuée sur deux jeux de test différents, contenant chacun 300 collocations, a montré une précision de 77 % dans le premier cas et de 61 % dans le second. Cette baisse de performance a été expliquée par les auteurs par la plus faible fréquence des collocations du second jeu de test. Finalement, le travail de (Lü et Zhou, 2004) sur la paire de langues anglais-chinois prend aussi en charge les collocations flexibles, car ces dernières sont identifiées dans le corpus source et cible à l'aide d'un analyseur syntaxique. Trois configurations syntaxiques ont été considérées : verbe-objet, adjectif-nom, et adverbe-verbe. À la différence des méthodes précédentes, cette méthode peut s'appliquer aussi sur des corpus non parallèles. Elle utilise un modèle de traduction statistique dans lequel les probabilités de traduction des mots sont estimées à l'aide de l'algorithme EM. Les traductions initiales pour chaque mot sont assignées à partir de dictionnaires bilingues. La précision de cette méthode, mesurée sur un échantillon de 1 000 collocations, varie entre 51 % et 68 %, selon la configuration syntaxique. La méthode que nous allons présenter est une méthode générique qui peut traiter un large éventail de configurations syntaxiques et plusieurs paires de langues. Relativement simple, elle est efficace même pour des collocations peu fréquentes, et même si des dictionnaires bilingues ne sont pas disponibles ; d'un autre côté, elle nécessite des outils d'analyse syntaxique pour les langues traitées, tels que l'analyseur multilingue Fips présenté dans la section suivante. L'analyseur Fips Fips (Laenzlinger et Wehrli, 1991 ;Wehrli, 1997 ;Wehrli, 2004 ; est un analyseur symbolique « profond » développé au LATL, le Laboratoire d'analyse et de technologie du langage de l'Université de Genève. Initialement conçu pour le français -l'acronyme Fips dérive de l'anglais French Interactive Parsing System -, il a été étendu successivement à l'anglais, l'italien, l'allemand, et plus récemment à l'espagnol et au grec 5 . Fips s'appuie sur une adaptation des concepts de grammaire générative inspirés par des théories chomskyennes (Chomsky, 1995 ;Haegeman, 1994). Le constituant 5. D'autres langues, parmi lesquelles le romanche, le roumain et le japonais, seront ajoutées par la suite dans le cadre d'un projet d'extension multilingue ultérieur. syntaxique est représenté sous la forme d'une structure X simplifiée, [ XP L X R], limitée à deux niveaux : XP -la projection maximale, et X -la tête de la projection. L et R dénotent des listes (éventuellement vides) de sous-constituants gauches et droits de X. X représente les catégories lexicales usuelles, N (nom), A (adjectif), V (verbe), Adv (adverbe), D (déterminant), P (préposition), Conj (conjonction), etc. Les entrées lexicales disponibles dans les lexiques créés manuellement contiennent des informations morphosyntaxiques détaillées, telles que des propriétés sélectionnelles, des informations de sous-catégorisation, et des traits syntactico-sémantiques susceptibles d'influencer l'algorithme d'analyse. L'analyseur est implémenté dans le langage orienté objet Component Pascal 6 . Du point de vue de l'architecture, Fips est constitué d'un noyau générique qui définit des structures de données abstraites et des opérations s'appliquant à toutes les langues traitées, auquel s'ajoutent des modules de traitement spécifiques à chaque langue. Les opérations principales de l'analyseur sont : Project, l'opération de projection, qui est associée à un élément lexical (tel qu'il apparaît dans le lexique) et qui crée un constituant syntaxique avec la tête lexicale correspondante (par exemple, pour un nom, on crée une projection NP) ; Merge, l'opération de combinaison des constituants, qui permet l'ajout d'un nouveau constituant à une structure existante par attachement à gauche ou à droite dans un noeud actif (le site d'attachement) ; les attachements sont contraints par des conditions définies dans les règles de grammaire spécifiques à chaque langue ; Move, l'opération de déplacement, qui établit un lien entre un élément extraposé et un constituant abstrait dans une position canonique 7 . La stratégie d'analyse est fondée sur un algorithme essentiellement ascendant, de type gauche à droite, dirigé par les données. À chaque pas, l'analyseur applique une des trois opérations mentionnées ci-dessus. Les alternatives sont traitées en parallèle, et un filtre descendant est utilisé conjointement avec des heuristiques d'élagage afin de limiter l'espace de recherche. La sortie fournie par l'analyseur pour une phrase d'entrée consiste en une structure riche, englobant, outre la structure des constituants, i) l'interprétation des constituants comme arguments (représentée sous la forme d'une table d'arguments similaire à la f -structure de la LFG 8 ), ii) l'interprétation des clitiques, pronoms interrogatifs et re-6. L'environnement de développement utilisé est BlackBox Component Builder (http://www.oberon.ch/blackbox.html). 7. Selon la théorie chomskyenne, les éléments extraposés sont des éléments déplacés par une transformation de mouvement à partir d'une position dite canonique, gouvernée par un prédicat. L'identification de ce mouvement permet de déterminer le rôle thématique de l'élément extraposé. 8. Dans la théorie LFG, Lexical functional grammar -« Grammaires lexicales fonctionnelles » (Bresnan, 2001), la f -structure sert à la représentation des fonctions grammaticales. latifs, et iii) les chaînes de co-indexation mettant en relation les éléments extraposés avec leur position canonique. Par exemple, pour la phrase donnée en (1a) Fips propose l'analyse en (1b), où l'index i montre la chaîne connectant le nom records au constituant vide e qui suit la forme verbale broken. Dans la table d'arguments, on trouvera ce constituant vide dans la position de complément d'objet direct du prédicat to break. À travers l'index i, il est ensuite possible d'identifier la relation verbe-objet entre broken et records. (1) a. Records are made to be broken. b. [ T P [ N P Records] i are[ V P made[ N P e] i [ T P [ DP e] i to[ V P be[ V P broken[ DP e] i ]]]]] Très robuste, l'analyseur Fips peut traiter des grands corpus de texte sans restrictions, en un laps de temps acceptable (environ 150-200 symboles sont traités par seconde). Sa large couverture grammaticale permet de traiter des phénomènes aussi complexes que ceux exemplifiés ci-dessous 9 : (2) a. Interrogation : Quel objectif spécifique le projet doit-il atteindre comme contribu- Détection d'équivalents L'application de détection d'équivalents bilingues pour les collocations est construite comme l'extension d'un système plus complexe d'aide à la traduction que nous avons développé pendant les dernières années (Seretan et al., 2004 ;Seretan, 2008), et qui intègre les modules principaux suivants : 1) un extracteur hybride de collocations, alliant aux calculs statistiques d'association lexicale des informations de nature syntaxique fournies par l'analyseur Fips ; 2) un concordancier monolingue/bilingue, qui visualise les résultats d'extraction en présentant la phrase d'origine et, simultanément, la phrase alignée si des corpus parallèles sont disponibles ; 3) un module (sous-jacent) d'alignement, qui met en correspondance la phrase d'origine avec la phrase qui représente sa traduction dans le document parallèle (la phrase alignée) ; 4) un module de validation des résultats, qui permet la création, par l'utilisateur, d'une base de données monolingue/bilingue de collocations, pouvant servir comme référence pour les traductions futures. 9. Les phrases discutées dans cet article sont toutes attestées, et proviennent des corpus utilisés. Figure 1. L'interface du concordancier bilingue pour les collocations Les figures 1 et 2 montrent l'interface du concordancier bilingue et celle du module de validation de notre système. Dans la première, nous avons affiché (dans la liste à gauche) des constructions de type verbe-objet avec le verbe atteindre, que nous avons triées par leur score statistique d'association. La zone de texte en haut à droite présente, pour la première collocation obtenue, atteindre -objectif, l'occurrence numéro 100 dans le corpus source (sur 271 identifiées au total). Le système fait défiler automatiquement le texte dans le document d'origine jusqu'à l'occurrence recherchée et sa phrase source, les deux étant mises en évidence par des couleurs contrastées. La zone de texte en bas présente de manière similaire la phrase correspondante dans le document parallèle en anglais. La deuxième interface (figure 2) montre que cette collocation a été choisie pour la validation, et que l'utilisateur a introduit une traduction vers l'anglais, attain -goal, dans le champ correspondant. Comme notre exemple le montre, à l'aide de notre système l'utilisateur peut repérer la traduction d'une collocation en consultant les phrases alignées correspondant aux différentes occurrences de cette collocation, et ensuite stocker cette traduction dans la base de données bilingue, avec des contextes qui constituent des exemples d'utilisation. Le but de notre méthode est d'automatiser ce travail, et d'identifier automatiquement la traduction d'une collocation en utilisant la technologie dont on dispose. 4.1. L'extracteur de collocations À la différence de la plupart des extracteurs de collocations existants (certains d'entre eux ont été mentionnés dans la section 1), notre système d'extraction fait appel à une analyse syntaxique complète du texte source, comme étape préliminaire à la procédure de calculs statistiques qui, en mesurant le degré d'association entre les mots, identifie des collocations potentielles. D'autres systèmes pouvant aussi être qualifiés d'hybrides s'appuient sur une analyse moins détaillée, souvent fondée sur l'étiquetage des catégories lexicales et la reconnaissance de certaines relations syntaxiques en utilisant des expressions régulières sur les étiquettes assignées aux mots. Dans notre système, l'analyse syntaxique fournie par Fips (cf. section 3) permet un traitement plus uniforme et fiable de ces expressions. Premièrement, les paires de mots qui constituent des collocations candidates sont identifiées à partir de la structure normalisée associée à une phrase par l'analyseur : indifféremment de la manière dont une relation syntaxique est réalisée dans le texte, le système considère toujours l'ordre canonique des mots et la forme de base à la place de la forme fléchie, ceci permettant un traitement plus général et plus homogène des données. Deuxièmement, grâce à une analyse plus complète qui permet l'interprétation dans un contexte plus large et la désambiguïsation au niveau des catégories lexicales (ainsi qu'au niveau des lectures pour une même catégorie), l'analyse syntaxique profonde contribue à améliorer la précision des résultats. Aussi, le regroupement des variantes morphosyntaxiques d'une collocation sous le même type permet l'application plus fiable des mesures d'association lexicale ; il est connu que ces mesures ont un comportement insatisfaisant pour les candidates dont le nombre d'occurrences dans le corpus est très petit (moins de 5, selon (Evert, 2004)), et que la plupart des candidates dans un corpus n'apparaissent que très peu de fois. En regroupant, en revanche, les variantes dispersées d'un même candidat, on réduit la fragmentation des données (surtout pour les langues ayant une morphologie riche), ce qui permet d'obtenir des résultats statistiques plus fiables. La procédure d'extraction peut être décrite, dans les grandes lignes, comme suit : au fur et à mesure que le corpus source est analysé, le module d'extraction parcourt récursivement les structures obtenues 10 afin d'y repérer des collocations candidates. La sélection d'une paire obéit au critère principal suivant : l'un des éléments de la paire, celui qui représente la tête lexicale X de la structure courante [ XP L X R], peut former une paire avec la tête lexicale d'un des sous-constituants droit ou gauche (de L ou de R). Ce critère assure la présence d'une relation syntaxique entre les deux éléments de la paire, c'est-à-dire leur proximité syntaxique (en opposition à la proximité linéaire utilisée par les approches traditionnelles). Pour les verbes, les relations de type prédicat-argument sont récupérées directement à partir de la table d'arguments. Ainsi, les paires break -record et atteindre -objectif sont facilement identifiées à partir de phrases comme celles présentées en (1) et (2), puisque tout le calcul nécessaire pour identifier le lien verbe-objet est déjà effectué par l'analyseur. À ce critère principal s'ajoutent des contraintes plus spécifiques sur la paire candidate, qui s'appliquent soit à la configuration syntaxique de la paire, soit à chacun de ses éléments individuellement. Plus précisément, une paire est retenue seulement si elle est dans une configuration prédéfinie, telles que celles montrées dans le tableau 1, et si elle ne contient pas comme élément un verbe modal ou un nom propre. À la différence de beaucoup d'autres systèmes existants, notre système n'applique pas de filtre fondé sur les stop-words, ni sur la fréquence des paires candidates ; un tel filtre peut être appliqué ultérieurement par l'utilisateur en fonction de ses propres exigences. Configuration Abréviation Exemple Adjectif-nom A-N haute technologie Nom-adjectif N-A voie ferrée Nom-[prédicat-]adjectif N-Pred-A livre [semble] intéressant Nom(tête)-nom N(tête)-N bouc émissaire Nom-préposition-nom N-P-N danger de mort Nom-préposition-verbe N-P-V machine à laver Nom-préposition N-P précaution quant Adjectif-préposition-nom A-P-N fou de rage Adjectif-préposition A-P tributaire de Sujet-verbe S-V incendie se déclarer Verbe-objet V-O présenter risque Verbe-préposition-argument V-P-N répondre à besoin Verbe-préposition V-P centrer sur Verbe-adverbe V-Adv refuser catégoriquement Verbe-adjectif V-A sonner creux Verbe-verbe V-V faire suivre Adverbe-adjectif Adv-A grièvement blessé Adverbe-adverbe Adv-Adv très bien Déterminant-nom D-N ce matin Préposition-nom P-N sur mesure Préposition-nom-adjectif P-N-A à titre indicatif Adjectif-et-adjectif A&A bête et méchant Nom-et-nom N&N frères et soeurs Tableau 1. Configurations syntaxiques permises pour les paires candidates La seconde étape du processus d'extraction consiste en l'application des mesures d'association lexicale afin d'estimer le degré d'affinité entre les éléments des paires candidates. Le résultat d'extraction est représenté par la liste de paires candidates triées par ordre décroissant du score d'association obtenu. Les paires situées en haut de la liste sont les plus susceptibles, théoriquement, de constituer de vraies collocations et de présenter un intérêt lexicographique. Seulement un nombre limité de paires peuvent être examinées en pratique par les utilisateurs, d'où les efforts pour perfectionner les mesures d'association afin de placer les paires intéressantes au sommet de la liste et celles moins intéressantes vers la fin. Notre système implémente une douzaine de mesures décrites dans la littérature récente sur l'extraction de collocations. Parmi celles-ci, le système propose par défaut la mesure log-lihelihood ratio (LLR), ou le rapport de vraisemblance (Dunning, 1993). Son choix est motivé par le fait qu'elle a reçu des évaluations positives de la part de beaucoup d'autres auteurs, et qu'elle est jugée comme particulièrement appropriée aux données les moins fréquentes. Avant d'être soumises au calcul du score, les paires candidates sont divisées en des ensembles syntaxiquement homogènes sur la base de leur configuration syntaxique (cf. tableau 1) ; cette stratégie est censée avoir des effets bénéfiques sur la performance des mesures d'association (Heid, 1994). Un autre trait distinctif de notre système d'extraction est qu'il est capable de reconnaître, grâce aux informations fournies par l'analyseur syntaxique, si l'un des termes d'une paire candidate fait partie d'une autre collocation, et ensuite de traiter ce terme complexe comme une seule entité. En conséquence, le système peut retourner à la place des collocations binaires des collocations plus longues, comme on peut le remarquer dans la figure 1 dans le cas de la construction atteindre point culminant. Dans notre approche, la première étape (la sélection des paires candidates) reste l'étape clé du processus d'extraction, car c'est en premier lieu de la qualité des paires proposées que la qualité des résultats dépend. La supériorité des approches d'extraction hybride fondées sur l'analyse syntaxique complète, longtemps postulée par la théorie 11 mais souvent questionnée à cause des échecs et des erreurs inhérentes d'analyse, a été démontrée dans le cas de notre extracteur par des études d'évaluation que nous avons menées pour plusieurs langues. Nos expériences sur des corpus en français, anglais, italien et espagnol (Seretan, 2008 ;Seretan et Wehrli, 2009) ont démontré que, en effet, l'analyse syntaxique conduit à une réduction substantielle des faux positifs agrammaticaux parmi les résultats d'extraction, par rapport à une méthode standard d'extraction fondée sur l'étiquetage morphosyntaxique et sur le critère de proximité linéaire : 99 % des 500 meilleurs résultats de la méthode syntaxique sont valides, contre 76,4 % de la méthode standard ; si l'on considère des résultats situés à différents niveaux 12 dans la liste complète des résultats, on obtient un pourcentage moyen de 88,8 % pour la première méthode, et de seulement 33,2 % pour la seconde. En outre, nos expériences ont montré que le calcul statistique est lui aussi allégé, puisque le filtre syntaxique réduit considérablement la taille des données candidates. 11. Beaucoup de chercheurs avaient indiqué que, idéalement, l'identification des collocations devrait tenir compte de l'information syntaxique fournie par les analyseurs, surtout vu le progrès réalisé dans le domaine de l'analyse syntaxique (Smadja, 1993 ;Krenn, 2000 ;Pearce, 2002 ;Evert, 2004). 12. Un nombre de 50 résultats adjacents on été testés au début (0 %) et ensuite à 1 %, 3 %, 5 % et 10 % des listes retournées par les deux méthodes d'extraction. Alignement de phrases Afin de détecter la traduction d'une phrase source dans le document cible, on utilise notre propre méthode d'alignement intégrée au système d'aide à la traduction, qui a été décrite dans (Nerima et al., 2003). La spécificité de cette méthode consiste dans le calcul à la volée d'un appariement partiel, seulement pour la phrase couramment visualisée par l'utilisateur dans le concordancier. Très rapide, cette méthode n'a pas besoin d'un traitement préalable des documents parallèles disponibles ; notamment, elle ne présuppose pas un alignement macro-structurel (par exemple, au niveau des sections ou paragraphes), comme le font d'autres méthodes d'alignement de phrases. Étant donné le document source et la position d'occurrence d'une collocation dans ce document, la méthode identifie les positions dans le document cible qui délimitent la traduction de la phrase d'origine. Le calcul d'appariement est fait principalement selon le critère de conservation des longueurs relatives des paragraphes dans les documents source et cible, exprimées en nombre de caractères. Plus les proportions des longueurs dans le voisinage d'une phrase candidate ressemblent aux proportions des longueurs dans le voisinage de la phrase source, plus cette phrase est considérée comme une candidate valide. La méthode fait également appel à l'analyse du contenu des documents source et cible, mais uniquement pour vérifier la compatibilité de la numérotation, s'il y en a une 13 . La précision de cette méthode est d'environ 90 % pour des textes relativement difficiles 14 à aligner, qui peuvent contenir non seulement du texte mais aussi des tableaux (en format HTML) et qui comportent des paragraphes structurés de manière différente à travers les langues, ainsi que des paragraphes manquants 15 . La méthode de base de détection d'équivalents Les prérequis de notre méthode de traduction de collocations sont, d'un côté, la disponibilité d'un corpus parallèle, et de l'autre, la disponibilité d'un analyseur syntaxique pour la langue cible (bien que dans notre cas les collocations sources soient aussi extraites à l'aide de l'analyse syntaxique, du point de vue de la méthode il n'est pas nécessaire de disposer d'un analyseur pour la langue source). Étant donnée une collocation source CS et ses occurrences dans le corpus source, notre méthode essaie de trouver une traduction adéquate pour CS dans le corpus cible en appliquant la stratégie suivante : 13. D'autres méthodes s'appuient plus sur la comparaison de contenu, par exemple, la méthode de (Simard et al., 1992) fondée sur l'identification de mots semblables ou cognate words. 14. La précision obtenue par les meilleurs systèmes d'alignement est proche de 100 % pour les textes « normaux » qui sont identiques au niveau structurel, mais elle peut descendre jusqu'à environ 65 % pour les textes difficiles (Véronis et Langlais, 2000). 15. Dans le futur, notre système sera étendu afin de permettre l'utilisation d'autres méthodes d'alignement existantes, et de prendre en charge des corpus parallèles préalignés au niveau de la phrase. 1) dans une première étape, on constitue un mini-corpus de contextes cibles associés à CS en utilisant la méthode d'alignement de phrases décrite dans la section 4.2 ; pour chaque occurrence de CS, on met ainsi en correspondance la phrase source avec la phrase équivalente dans le corpus cible ; 2) dans la seconde étape, on utilise ce mini-corpus comme l'espace de recherche de CC, la collocation cible. La méthode de base d'identification de CC présentée dans (Seretan et Wehrli, 2007) était fondée sur les trois principes suivants : P1) CC conserve la configuration syntaxique de CS : en conformité avec des travaux précédents -par exemple, (Lü et Zhou, 2004) -, on assumait que, avec peu d'exceptions, une collocation peut être traduite par une expression du même type (ainsi, une paire verbe-objet et traduite par une paire verbe-objet, etc.) ; P2) la fréquence des paires ayant la même configuration syntaxique que CS dans le mini-corpus cible est un bon indicateur de la traduction de CS : vu la manière dont le mini-corpus a été construit, la paire la plus fréquente est très probablement la traduction de CS ; P3) les collocations sont partiellement compositionnelles et leur base (l'élément dont le sens est conservé dans le sens de la collocation) peut être traduite littéralement, tandis que le choix du collocatif reste arbitraire 16 . Par exemple, la base de la collocation battre un record (le nom record) est traduite littéralement vers l'anglais, à la différence du collocatif verbal battre, dont l'équivalent est to break (lit. casser) ; le collocatif mauvaise de la collocation mauvaise décision est traduit en allemand comme falsche (lit. fausse), etc. Ces principes étaient utilisés pour limiter successivement l'espace de recherche de CC, afin d'aboutir à une seule expression candidate à la traduction. Le minicorpus cible est d'abord analysé avec Fips, et l'extracteur de collocations (décrit dans la section 4.1) identifie, dans la sortie de l'analyseur, des cooccurrences syntaxiques dans toutes les configurations appropriées aux collocations (cf. tableau 1). Ces cooccurrences constituent l'espace initial de recherche, qui est ensuite réduit en retenant seulement les cooccurrences du même type que le type de CS. Si des dictionnaires bilingues sont disponibles pour la paire de langues en cause, on restreint cet espace aux cooccurrences dont la base (par exemple, le nom dans une combinaison verbe-objet) est une des traductions trouvées dans le dictionnaire pour la base de CS. Finalement, la décision finale qui permet de choisir CC est de considérer la paire la plus fréquente parmi les paires qui restent. La réponse de la méthode est unique ; en cas de fréquences identiques, le système ne fournit aucune traduction (des résultats expérimentaux avaient indiqué que le système perdait en précision lorsqu'il proposait 16. Ce principe est compatible avec les stipulations théoriques considérant les collocations comme des combinaisons polaires formées d'une base autosémantique et d'un collocatif synsémantique choisi en fonction de la base (Hausmann, 1989 ;Mel'čuk, 1998 ;Polguère, 2000). La base d'une collocation peut être déterminée de manière univoque en fonction de sa configuration syntaxique. des traductions multiples, même s'il réussissait ainsi à proposer plusieurs traductions synonymiques valides) 17 . Cette méthode de détection d'équivalents pour les collocations a été appliquée à des paires de type verbe-objet extraites du corpus parallèle Europarl (Koehn, 2005). L'extraction a été effectuée sur un sous-corpus d'environ 4 millions de mots en moyenne, pour 4 langues : le français, l'anglais, l'italien et l'espagnol. La méthode de traduction a été appliquée pour la totalité des 12 paires de langues possibles. Son évaluation effectuée sur 4 000 collocations (500 collocations 18 pour 8 paires de langues) a montré des résultats prometteurs : en moyenne pour les paires de langues évaluées, la précision obtenue était de 89,8 % et la couverture de 70,9 % (Seretan et Wehrli, 2007). Il a été aussi montré que la précision n'est que légèrement affectée par la diminution de la fréquence des collocations (elle varie entre 91,8 % pour les collocations avec au moins 30 occurrences et 84,6 % pour celles ayant une fréquence entre 1 et 15), alors que la couverture descend plus vite avec la fréquence (de 79,3 % à 53 %). Extensions Dans cette section, nous décrivons les améliorations que nous avons apportées à la méthode de base, afin d'étendre son champ d'application à d'autres configurations syntaxiques (autres que celle déjà traitée, verbe-objet) ; de permettre des divergences syntaxiques entre les collocations sources et cibles ; et d'augmenter sa performance, notamment en ce qui concerne la couverture (qui était plus déficitaire). Par souci de simplicité, on appelle méthode A la méthode de base d'obtention d'équivalents, décrite dans la section précédente (section 4.3). Puisque le principe P1 qu'elle utilise est trop contraignant, afin d'augmenter les chances qu'une collocation source puisse être traduite, la première extension prévue consiste à définir des correspondances entre des configurations syntaxiques sources et cibles, en fonction des langues traitées. Ainsi, pour la paire de langues français-anglais on spécifie, par exemple, qu'une collocation verbe-objet peut être traduite non seulement comme verbe-objet, mais aussi comme verbe-préposition-argument, et vice versa : relever défi -respond to challenge, profiter de occasion -take opportunity. De la même manière, on spécifie la liste de configurations cibles acceptées pour la configuration source adjectif-nom quand on traduit de l'anglais vers le français : adjectif-nom (serious problem -grave problème), nom-adjectif (humanitarian aid -aide humanitaire), etc. Les correspondances syntaxiques spécifiées sont considérées dans le système selon une échelle de préférences préétablie. Des poids 19 différents sont associés à chaque configuration syntaxique cible, prenant des valeurs numériques entre 0 et 1. Une fois que le système a appliqué le filtre syntaxique correspondant, ces poids sont pris en compte conjointement à la fréquence des collocations candidates restantes pour sélectionner la collocation cible (CC) : l'espace de recherche est restreint aux paires candidates c pour lesquelles l'expression fréquence(c) × poids(configuration(c)) atteint sa valeur maximale. La deuxième extension de la méthode consiste en l'introduction d'un critère supplémentaire qui s'applique pour départager les traductions candidates dans le cas où la valeur maximale est atteinte par plusieurs d'entre elles. La solution qui a été adoptée est de calculer le score LLR d'association lexicale pour toutes les paires cibles identifiées, et de départager les candidates à égalité selon ce score. Cette variante étendue de la méthode A est appelée méthode B. Comme on le verra dans la section suivante, les deux extensions considérées ont conduit à une augmentation considérable de la proportion des collocations pour lesquelles une traduction a pu être proposée. Une extension ultérieure a ensuite été réalisée, en partant de l'observation que le filtre lexical -conformément au principe P3 -peut avoir une influence négative sur la performance de notre méthode, si la couverture du dictionnaire bilingue est insatisfaisante pour les entrées consultées. En effet, l'analyse des résultats de la méthode A avait mis le doigt sur des situations où le manque d'alternatives supplémentaires de traduction pour la base d'une collocation conduisait à l'échec de la traduction de celleci. Par exemple, les traductions trouvées dans le dictionnaire français-anglais pour le mot remarque (remark, comment, et note) étaient insuffisantes pour pouvoir traduire la collocation faire une remarque, car le corpus proposait souvent make a point comme équivalent, et le mot point n'apparaissait pas dans le dictionnaire comme traduction de remarque. Quoique généralement valable, le principe P3 devrait être relâché si on admet que certaines collocations sont relativement moins transparentes et que leur base peut ne pas se traduire littéralement. Une nouvelle version de la méthode B a été ainsi développée (appelée méthode C), qui prévoit que si la traduction échoue à cause du filtre fondé sur le dictionnaire, le système devrait alors renoncer à ce filtre et appliquer seulement les autres critères de sélection. En suivant cette stratégie, la couverture du système est encore augmentée jusqu'à 94,2 %, ce qui représente, par rapport à la méthode de base, une hausse de presque 25 %. L'impact de ces extensions sur la qualité des résultats obtenus fait l'objet de l'étude d'évaluation présentée dans la section suivante. Résultats et évaluation Afin de faciliter la comparaison de la performance obtenue par les nouvelles versions de notre méthode (méthodes B et C décrites dans la section 4.4), nous avons utilisé le même cadre expérimental que celui de la méthode de base (méthode A, cf. section 4.3). La méthode de traduction a été appliquée sur les 500 premières collocations extraites automatiquement (sans aucune validation manuelle préalable, le pro-cessus étant entièrement automatique). Le tableau 2 présente quelques équivalences de traduction obtenues pour des collocations de type verbe-objet, adjectif-nom, et verbe-préposition-argument pour la paire de langues français-anglais. Adjectif-Nom Verbe-Objet Verbe-Préposition-Argument courte majorité/narrow majority accuser retard/experience delay aboutir à conclusion/come to conclusion étroite collaboration/close cooperation apporter aide/give aid acquitter de mission/fulfil duty faible densité/low density attirer attention/draw attention arriver à conclusion/reach conclusion ferme conviction/strong belief avoir sens/make sense assister à réunion/attend meeting forte augmentation/substantial increase commettre erreur/make mistake attaquer à problème/address problem forte concentration/high concentration déployer effort/make effort entrer dans détail/go into detail forte pression/heavy pressure effectuer visite/pay visit entrer en contact/come into contact grande attention/great attention entamer dialogue/start dialogue figurer à ordre du jour/be on agenda grande diversité/wide range exercer influence/have influence insister sur importance/stress importance grande vitesse/high speed ménager effort/spare effort parvenir à compromis/reach compromise grave erreur/bad error ouvrir voie/pave way parvenir à résultat/achieve result grossière erreur/great mistake prononcer discours/make speech répondre à exigence/meet requirement jeune âge/early age tirer leçon/learn lesson traduire en justice/bring to justice Tableau 2. Exemples d'équivalences extraites (français-anglais) Ces équivalences ont été choisies pour illustrer l'idiomaticité d'encodage dans la langue source et en particulier le choix du collocatif en fonction du mot avec lequel il se combine. L'adjectif forte, par exemple, est traduit de trois manières différentes (substantial, high, heavy), selon le nom qu'il modifie. De plus, aucune des trois alternatives ne correspond à sa traduction littérale, strong. De manière générale, les traductions littérales des collocations sont soit moins utilisées que leurs équivalents corrects (par exemple, strong increase contre substantial increase), soit complètement inadéquates et dans ce cas elles pourraient être qualifiées d'anticollocations 20 (par exemple, *short majority contre narrow majority). Le tableau 3 montre quelques équivalences qui ne conservent pas la structure syntaxique d'une langue à l'autre, et dont l'extraction a nécessité la prise en charge de correspondances structurelles non isomorphes entre les langues. Anglais (V-O) → Français (V-P-N) Anglais (V-P-N) → Français (V-O) answer question répondre à question adhere to deadline respecter délai attend meeting assister à réunion bring to attention attirer attention have access bénéficier de accès bring to end mettre terme lose sight perdre de vue come to decision prendre décision meet demand satisfaire à exigence comply with legislation respecter législation meet need répondre à besoin lead to improvement entraîner amélioration reach compromise parvenir à compromis meet with resistance rencontrer résistance reach conclusion aboutir à conclusion provide with opportunity fournir occasion reach consensus aboutir à consensus respond to challenge relever défi stress need insister sur nécessité touch on point aborder point Tableau 3. Exemples d'équivalences ne conservant pas la configuration syntaxique 20. Ce terme, introduit par (Pearce, 2001), désigne les variantes paradigmatiques des collocations perçues comme inadéquates par les locuteurs natifs d'une langue. L'évaluation des nouvelles méthodes proposées a été effectuée pour la paire de langues anglais-français, sur les mêmes données de test qui ont servi pour l'évaluation de la méthode A, c'est-à-dire sur les 500 premières collocations sources de type verbe-objet extraites. Les équivalences proposées par les méthodes B et C ont été classées en deux catégories, chaque équivalence étant considérée soit comme correcte, soit comme incorrecte. L'annotation des équivalences a été faite à l'aide du concordancier bilingue (décrit dans la section 4), ce qui a permis leur interprétation en contexte et rendu plus facile le processus d'évaluation 21 . Un large pourcentage des résultats fournis par les deux nouvelles méthodes sont identiques aux résultats de la méthode de base (environ 60 %). La comparaison des résultats distincts pour les trois méthodes a permis l'identification d'équivalents de traduction synonymiques, à partir de situations où plusieurs traductions ont été jugées correctes pour une même collocation source. Quelques exemples sont présentés dans le tableau 4. Collocation source Équivalent 1 Équivalent 2 achieve consensus parvenir à consensus établir consensus bridge gap combler fossé combler lacune do job accomplir travail faire travail draw distinction établir distinction faire distinction make effort faire effort déployer effort meet need répondre à besoin satisfaire besoin miss opportunity rater occasion perdre occasion provide aid fournir assistance apporter soutien raise issue aborder problème soulever question reach compromise parvenir à compromis trouver compromis reap benefit retirer avantage récolter fruit submit proposal présenter proposition soumettre proposition Tableau 4. Quelques équivalents synonymiques trouvés en comparant les méthodes La performance des méthodes est mesurée en tenant compte de leur capacité à fournir une traduction correcte pour les collocations sources testées. Si n est le nombre de collocations sources (dans notre cas, n = 500), p le nombre d'équivalences proposées, et c le nombre d'équivalences correctes proposées, les notions standard de précision et couverture sont définies comme suit : P = c p ; C = p n [1] Si l'on considère que la tâche à réaliser consiste à trouver une traduction possible pour chaque collocation source, on peut alors définir le rappel comme le pourcentage d'équivalences correctes retournées parmi toutes les équivalences correctes at-21. Compte tenu de la relative objectivité de la tâche et de la possibilité de vérifier les traductions dans le corpus aligné, nous avons effectué nous-mêmes l'annotation des équivalences proposées. tendues (n), et on peut employer la F-mesure 22 comme mesure globale de la performance : Les résultats comparatifs de l'évaluation sont présentés dans le tableau 5. Ils montrent que l'objectif principal d'amélioration de la méthode de base (méthode A) a été atteint, la couverture du système augmentant graduellement de 71,4 % à 80,4 % jusqu'à 94,2 %. Cette importante augmentation de la couverture (de 22,8 % par rapport à la valeur initiale) engendre une diminution de la précision du système, d'abord d'environ 3 %, ensuite d'environ 10 % ; toutefois, en balance, la performance globale est maintenue (la F-mesure croît même légèrement, de 78,6 % à 81,6 %), car le rappel connaît une amélioration substantielle, de 11,8 % (de 67,4 % à 79,2 %). R = c n ; F = 2P R P + R [2] La différence entre la nouvelle méthode (B) et sa variante plus évoluée (la méthode C) consiste en l'utilisation de techniques pour contourner les éventuelles lacunes des dictionnaires bilingues. De manière plus générale, nous nous sommes intéressés à l'impact de l'utilisation d'un dictionnaire sur la performance de notre méthode. Une évaluation secondaire a ainsi été menée, afin de comparer la performance obtenue par la méthode B en la présence et en l'absence d'un dictionnaire bilingue. Les résultats sont présentés dans le tableau 6. Comme on peut l'observer, la présence du dictionnaire contribue à l'augmentation de la précision mais, en même temps, à la diminution de la couverture ; globalement, on obtient une légère hausse du rappel et de la F-mesure. Notre précédente comparaison (Seretan et Wehrli, 2007) entre la performance obtenue pour les paires de langues pour lesquelles on disposait de dictionnaires bilingues et celles pour lesquelles on n'en disposait pas avait trouvé des écarts assez semblables de précision (92,9 % contre 84,5 %), et des différences mineures de couverture (71,7 % contre 69,5 %). Sur la base de ces résultats, on peut conclure que l'absence de dictionnaires (monolexèmes) bilingues, même si indésirable, n'entraîne qu'une perte limitée en performance. Tableau 6. Impact de la présence du dictionnaire sur la performance La performance de la version la plus récente de notre méthode (la méthode C) a été également mesurée sur des données dans d'autres configurations syntaxiques, qui sont actuellement prises en charge par le système. Les premières colonnes du tableau 7 affichent les résultats d'évaluation pour les 500 premières collocations de type adjectif-nom et nom-préposition-nom, pour la même paire de langues (anglais-français). Ces valeurs sont comparables aux valeurs obtenues pour les données de type verbe-objet, reprises dans l'avant-dernière colonne. La dernière colonne synthétise les résultats d'évaluation sur l'ensemble des données testées. Tableau 7. Performance de la méthode C par type de données Analyse des erreurs Le processus d'extraction d'équivalents est entièrement automatique et ne nécessite pas d'intervention humaine pour la validation des résultats intermédiaires dans le flux de traitements. En conséquence, les erreurs qui subsistent parmi les résultats finaux peuvent être dues aux différents modules de traitement utilisés. Nous avons repéré, ainsi, une première catégorie d'erreurs qui sont causées par des erreurs d'analyse syntaxique de la langue source. Les équivalences contenant des paires sources grammaticalement incorrectes ont été elles aussi marquées comme incorrectes : par exemple, l'équivalence develop country (EN) -frapper pays (FR), où la paire develop country est incorrectement analysée comme verbe-objet dans des contextes tels que the developing countries (« les pays en voie de développement »). Dans la sortie de la méthode C pour des données de type verbe-objet, que nous avons examinées en détail, ces erreurs représentent 32,7 % du nombre total de 104 erreurs ou échecs de traduction (les autres 396 paires étant des vrais positifs, cf. tableau 5) 23 . Une deuxième catégorie d'erreurs est représentée par les erreurs dues à une mauvaise analyse syntaxique de la langue cible. Assez rarement, il peut arriver que le système propose des équivalents agrammaticaux, comme par exemple libre de personne (FR) pour l'expression source movement of person (EN) ; cette erreur est due à la mauvaise analyse du syntagme d'origine libre mouvement des personnes. Pour les données de type verbe-objet examinées, la taux de ces erreurs est de 9,6 %. Aussi, il est vraisemblable qu'un certain nombre d'erreurs soient causées par des erreurs d'alignement de phrases. Les appariements incorrects ont comme conséquence la diminution du nombre des bons équivalents dans le corpus de phrases cibles, ce qui pose des problèmes spécialement pour les données très peu fréquentes, ainsi que pour les collocations sources n'ayant pas de traduction stable dans le corpus. Certaines expressions sont particulièrement difficiles à traduire, et donc leurs équivalents trouvés dans le corpus cible connaissent une grande variation. Un tel exemple se rencontre dans l'expression serve purpose (EN), pour laquelle on trouve des traductions montrant des grandes divergences syntaxiques (cf. exemple (3)). Notre analyse a trouvé que 17,3 % des erreurs examinées sont dues à cette cause. (3) a. an additional cost which serves no purpose un coût supplémentaire inutile b. would serve little purpose, in my opinion. ne sera selon moi que peu efficace c. Such a ban will serve no purpose at all. Une telle interdiction ne sert à personne. L'amélioration de tous les modules impliqués permettrait la réduction du taux d'erreurs de notre méthode. Une solution supplémentaire serait d'augmenter le nombre de phrases alignées considérées en entrée, si des données plus larges sont disponibles 24 . Il serait aussi très utile d'utiliser des mécanismes pour trouver toutes les variations grammaticales possibles pour les traductions d'une collocation, afin de pouvoir couvrir un nombre plus grand de configurations cibles que celui actuellement pris en charge par notre système. Notre étude a révélé que 4,8 % des traductions ont échoué à cause de l'insuffisance des configurations cibles prévues : par exemple, pour pouvoir détecter l'équivalence receive request -être saisi d'une demande, notre système aurait dû prévoir une configuration cible plus complexe que celles prises en charge actuellement. Pour poursuivre l'objectif mentionné, nos travaux futurs pourraient s'inspirer des travaux de (Daille, 2003 ;Jacquemin, 2001) sur la variation des termes, ou des travaux de (Ozdowska et Claveau, 2006) sur l'apprentissage de configurations équivalentes fondé sur l'alignement des mots et la détection de liens syntaxiques avec l'analyseur en dépendances SYNTEX (Bourigault et Fabre, 2000). Notre analyse a aussi révélé qu'une certaine partie des erreurs concernent des correspondances incomplètes, telles que celles montrées en (4), où le terme manquant est ajouté entre crochets. Pour les données de type verbe-objet examinées, leur taux est de seulement 2,9 %, mais les autres configurations semblent plus affectées. Dans notre approche, nous avons traité un seul cas de correspondance, 2:2, dans lequel une collocation à deux termes se traduit aussi par une collocation à deux termes. Il s'agit ici d'un cas typique ; néanmoins, une approche plus sophistiquée devrait prendre en compte le cas général des correspondances de type m:n. Le problème des fragments est un problème reconnu dans l'extraction de terminologie (Frantzi et al., 2000), mais l'état actuel de la technologie n'y fournit malheureusement pas des solutions suffisamment adéquates. Notre système est quand même capable de faire face à ce genre de situations de manière limitée, car il peut offrir une traduction plus complète s'il reconnaît, grâce à son lexique et à l'analyse syntaxique, qu'un des termes d'une paire est en réalité un terme complexe (multilexème). Un exemple est l'équivalence put on agenda (EN) -placer à l'ordre du jour (FR), où ordre du jour est une expression multilexème qui apparaît dans le lexique français de l'analyseur. Lorsqu'une collocation se traduit par un seul élément lexical (n = 1), comme par exemple bear witnesstémoigner, le système produit des équivalences erronées qui concernent l'ajout d'éléments indésirables : dans l'équivalence trouvée bear witness (EN) -témoigner de vue (FR), l'argument vue est superflu. L'exemple suivant montre un des contextes source et cible impliqués : This view bears witness to an understanding of the political constraints of the European Union in the Middle East. Cette approche témoigne d'une vue claire des limites politiques de l'Union européenne au Moyen-Orient. Le pourcentage de ce type d'erreurs est de 8,7 % pour les données verbe-objet de la méthode C qui ont été examinées. Une autre catégorie d'erreurs identifiée est celle des erreurs dues aux limitations de la couverture du dictionnaire bilingue (4,8 % des erreurs analysées). Si le nombre de traductions proposées pour la base est insuffisant (par exemple, notre entrée pour le mot anglais flag liste seulement drapeau), le système élimines les bonnes corres-pondances (comme battre pavillon pour la collocation source [to] fly flag), lorsqu'il applique le filtre lexicale fondé sur le dictionnaire 25 . Finalement, un pourcentage de 6,7 % d'erreurs 26 concerne des équivalences qui sont justes par rapport au corpus utilisé (car le traducteur humain a effectivement employé ces mêmes équivalences et elles sont appropriées aux contextes en cause), mais qui n'ont toutefois pas été jugées comme correctes, car la traduction est plutôt éloignée du sens initial et ne peut pas être considérée comme traduction de référence. C'est le cas, par exemple, de l'équivalence cover area (EN) -concerner domaine (FR). L'exemple (6a) montre une des paires de phrases alignées qui a permis d'inférer cette correspondance ; deux autres paires proposant des équivalents plus appropriés sont montrées en (6b) et (6c). (6) a. There are six vital areas covered by this unprecedented reform proposal Six domaines vitaux sont concernés par cette proposition de réforme sans précédent b. The synthesis report by its nature covers a wide area of the Commission's activity Le rapport de synthèse, de par sa nature, recouvre une grande partie des activités de la Commission c. so that it covers areas not covered by Community legislation de manière à couvrir des domaines qui ne sont pas visés par la législation communautaire. Nos travaux futurs pourraient explorer l'utilisation de modules de traitement complémentaires, tels que la désambiguïsation sémantique, afin d'identifier de manière encore plus précise les équivalents en fonction du contexte. Il serait également utile de proposer des équivalents multiples à la place d'une réponse unique, et de combiner, éventuellement, notre travail avec des travaux connexes de classification sémantique des collocations sources et des méthodes de détection de collocations synonymiques. Conclusion L'acquisition d'équivalents de traduction pour les expressions à mots multiples qui n'apparaissent pas toujours de manière adjacente dans le texte est un réel défi de la traduction automatique 27 . En nous fondant sur un analyseur syntaxique multilingue robuste et sur des outils que nous avons développés précédemment dans le cadre d'un système d'aide à la traduction, nous avons conçu une méthode de détection d'équi-25. Notre stratégie pour combler les lacunes du dictionnaire s'applique seulement si le système échoue, mais pas s'il propose tout de même une solution. 26. Le reste de 12,5% sont des erreurs qui n'appartiennent à aucune des classes mentionnées. 27. La nécessité de prendre en compte l'information de nature syntaxique afin de rendre compte des transformations qu'une expression peut subir est de plus en plus reconnue, y compris dans les approches les plus récentes de traduction statistique (comme en témoigne la série d'ateliers Syntax and Structure in Statistical Translation -« Syntaxe et structure dans la traduction statistique » (Wu et Chiang, 2007 ;Chiang et Wu, 2008)). valents de traduction pour un sous-type très fréquent et versatile d'expressions, les collocations. Selon (Orliac et Dillinger, 2003), les collocations représentent le facteur clé pour la production de textes acceptables dans les systèmes de traduction automatique. La méthode présentée dans cet article est une version évoluée de la méthode de base décrite dans (Seretan et Wehrli, 2007), que nous reprenons également ici et présentons avec plus de détails. Les extensions apportées permettent de rendre compte des différences structurelles entre la langue source et la langue cible et d'améliorer considérablement la couverture et le rappel de cette méthode, tout en maintenant un bon niveau de précision. Par rapport aux travaux similaires (passés en revue dans la section 2), les avantages de notre méthode sont multiples : prise en charge des constructions syntaxiquement flexibles (contrairement aux méthodes de (Kupiec, 1993 ;van der Eijk, 1993 ;Dagan et Church, 1994) qui, en l'absence d'outils d'analyse syntaxique adéquats, se limitent aux patrons simples) ; détection d'équivalents dans la forme canonique, sans besoin de traitement ultérieur pour trouver le bon ordre des mots (contrairement aux travaux de (Smadja et al., 1996)) ; utilisation optionnelle de dictionnaires monolexèmes bilingues (au contraire, la méthode de (Lü et Zhou, 2004) en est complètement dépendante ; notre méthode fournit des résultats satisfaisants même en l'absence de ce type de dictionnaires). Notre travail se distingue notamment de celui de (Lü et Zhou, 2004) par l'utilisation partielle des traductions des mots, seulement pour la base et pas pour le collocatif. De ce point de vue, notre méthode se démarque aussi des méthodes fondées sur dictionnaire et validation sur Internet des combinaisons générées, qui ont été proposées dans (Grefenstette, 1999 ;Léon et Millon, 2005). En effet, conformément aux prescriptions théoriques (Mel'čuk, 1998 ;Polguère, 2000), nous considérons que dans une collocation, un des mots -le collocatif -ne peut pas toujours être traduit de façon littérale. Toutefois, ces méthodes sont efficaces pour traduire des collocations relativement compositionnelles mais dont les termes sont des mots très ambigus, pourvu que le dictionnaire ait une couverture satisfaisante 28 . Par rapport aux travaux mentionnés, notre méthode est fonctionnelle pour un nombre plus grand de configurations syntaxiques et de paires de langues. À la différence des méthodes statistiques récentes, elle ne nécessite que très peu de paires de phrases alignées (nos expériences actuelles ont été faites en considérant au maximum 50 phrases alignées par collocation), et aucun effort d'entraînement au changement du domaine textuel. En contrepartie, elle est tributaire de la disponibilité d'outils d'analyse syntaxique pour les langues cibles, et il est aussi souhaitable (même si ce n'est pas nécessaire) qu'elle dispose de dictionnaires bilingues. Nous pensons, tout de même, 28. Par exemple, pour obtenir l'équivalence groupe de travail -work group, (Grefenstette, 1999) combine les cinq traductions disponibles dans un dictionnaire bilingue français-anglais pour le mot groupe (cluster, group, grouping, concern, collective), correspondant chacune à une lecture différente, avec les trois traductions disponibles pour travail (work, labor, labour). que l'essor des analyseurs syntaxiques rendra les approches telles que la nôtre de plus en plus courantes, et que notre travail pourra servir, à cet égard, comme référence pour les travaux similaires futurs 29 . L'inconvénient principal de notre approche est la disponibilité relativement réduite des corpus parallèles, ainsi que leur limitation à quelques domaines textuels spécifiques (Léon, 2008). Les développements futurs de notre méthode seront dédiés (en plus des points déjà mentionnés dans la section 6) à son adaptation pour pouvoir être appliquée sur des corpus comparables, à la place des corpus parallèles. Dans cette perspective, nos travaux pourraient s'inspirer des méthodes telles que celle de (Sharoff et al., 2009), fondée sur la détection de contextes similaires dans les corpus source et cible. Figure 2 . 2L'interface du module de validation de collocations tion aux objectifs globaux ? b. Passivation : Premièrement, parce que l'objectif du système des écopoints a pratiquement été atteint. c. Relativisation : Face aux nombreux objectifs que ce programme doit atteindre, [...] d. Topicalisation : Ce sont là des objectifs tout à fait essentiels qui ne peuvent être atteints qu'au terme d'efforts communs. Tableau 5. Performance des trois méthodes sur des données de type verbe-objetMéthode A Méthode B Méthode C c (vrais positifs) 337 367 396 p (résultats proposés) 357 402 471 C (couverture) 71,4 % 80,4 % 94,2 % P (précision) 94,4 % 91,3 % 84,1 % R (rappel) 67,4 % 73,4 % 79,2 % F (F-mesure) 78,6 % 81,4 % 81,6 % résoudre problème -find [solution] to problem faire travail -carry [out] work nombreux problèmes -whole host [of problems] learn from experience -tirer leçon [des expériences] translate into action -joindre [acte/geste] à parole TAL. Volume 50 -n o 1/2009 Ces méthodes sont conçues pour un type d'expressions plutôt rigide, les groupes nominaux simples. En revanche, Champollion(Smadja et al., 1996), le premier système de traduction des collocations proprement dit, est aussi capable de trouver un équivalent pour les collocations flexibles, qui comportent des verbes 4 . Les colloca-2. La précision mesure le pourcentage de réponses correctes parmi les résultats d'un système. 3. Cette méthode est la seule pour laquelle on dispose de détails sur la couverture (c'est-à-dire, le pourcentage de données pour lesquelles une traduction a été proposée). L'auteur précise que celle-ci est affectée par le fait que l'équivalent d'un groupe nominal n'est pas toujours un groupe nominal. À noter que la couverture est différente du rappel, autre mesure standard utilisée conjointement à la précision dans la recherche documentaire, exprimant le pourcentage de réponses correctes fournies par un système parmi toutes les réponses correctes attendues. Le calcul du rappel de la traduction est plus difficile, car il présuppose l'existence d'un jeu de traductions de référence, ce qui n'est pas toujours le cas. 4. Ces collocations sont caractérisées par une grande permissivité syntaxique. Les mots qui les composent peuvent être inversés et séparés par plusieurs autres mots, comme par exemple break et record dans records are made to be broken (les records sont faits pour être battus) ; voir aussi les exemples fournis dans la section 3. Ainsi, la méthode conçue consiste en la constitution, pour chaque collocation source, d'un mini-corpus de phrases alignées, et en son analyse afin d'y détecter, à l'aide de certaines heuristiques, une traduction potentielle pour cette collocation. La procédure de base a été décrite dans(Seretan et Wehrli, 2007). Dans le présent article, nous décrivons une version améliorée et étendue de cette procédure de base, et nous présentons des nouveaux résultats d'évaluation. Aussi, nous fournissons une description plus détaillée des outils sur lesquels cette méthode repose. . Lorsqu'une phrase n'est pas complètement analysée (c'est-à-dire que l'analyseur n'aboutit pas à une analyse globale de la phrase, mais seulement à des analyses partielles de ses sousparties), plusieurs structures sont fournies, correspondant aux morceaux analysés. TAL. Volume 50 -n o 1/2009 . Cependant, le choix du numéro de réponses désirées pourrait se faire en fonction du type d'application considérée : souvent (par exemple, dans un contexte lexicographique), on décide de favoriser le rappel au détriment de la précision. 18. Il s'agit, plus précisément, des 500 meilleures paires extraites, selon le score LLR. 19. Actuellement, ces poids privilégient la ressemblance avec la configuration syntaxique source, mais il est aussi envisageable que leur détermination soit faite expérimentalement. . Dans la recherche documentaire, la F-mesure englobe dans un seul score les valeurs de précision et rappel, en considérant leur moyenne harmonique. . Ces erreurs ne devraient pourtant pas être comptabilisées comme erreurs de notre méthode ; si on évalue la méthode C seulement sur les paires sources correctes, on obtient une précision de 88,4 % et un rappel de 85 % (F-mesure = 86,7 %). 24. Actuellement, le système exploite au maximum 50 phrases cibles par collocation, mais pour beaucoup de collocations on ne dispose que d'un nombre limité de contextes. RemerciementsCe travail a pu être réalisé grâce au soutien financier du Fonds national suisse de la recherche scientifique (subside n o 100012-117944). L'auteur souhaite remercier Eric Wehrli pour les discussions fructueuses et ses encouragements, Alexis Kauffmann et Béatrice Pelletier pour la révision répétée de ce manuscrit, ainsi que les trois relecteurs anonymes pour leurs commentaires détaillés et suggestions pertinentes dont la présente version a bénéficié. Approche linguistique pour l'analyse syntaxique de corpus ». 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Blueprint for a High Performance NLP Infrastructure

Natural Language Processing (NLP) system developers face a number of new challenges. Interest is increasing for real-world systems that use NLP tools and techniques. The quantity of text now available for training and processing is increasing dramatically. Also, the range of languages and tasks being researched continues to grow rapidly. Thus it is an ideal time to consider the development of new experimental frameworks. We describe the requirements, initial design and exploratory implementation of a high performance NLP infrastructure. 1 We use high performance to refer to both state of the art performance and high runtime efficiency.

Blueprint for a High Performance NLP Infrastructure James R Curran jamesc@cogsci.ed.ac.uk School of Informatics University of Edinburgh Buccleuch Place EH8 9LWEdinburgh Blueprint for a High Performance NLP Infrastructure Natural Language Processing (NLP) system developers face a number of new challenges. Interest is increasing for real-world systems that use NLP tools and techniques. The quantity of text now available for training and processing is increasing dramatically. Also, the range of languages and tasks being researched continues to grow rapidly. Thus it is an ideal time to consider the development of new experimental frameworks. We describe the requirements, initial design and exploratory implementation of a high performance NLP infrastructure. 1 We use high performance to refer to both state of the art performance and high runtime efficiency. Introduction Practical interest in NLP has grown dramatically in recent years. Accuracy on fundamental tasks, such as part of speech (POS) tagging, named entity recognition, and broad-coverage parsing continues to increase. We can now construct systems that address complex real-world problems such as information extraction and question answering. At the same time, progress in speech recognition and text-to-speech technology has made complete spoken dialogue systems feasible. Developing these complex NLP systems involves composing many different NLP tools. Unfortunately, this is difficult because many implementations have not been designed as components and only recently has input/output standardisation been considered. Finally, these tools can be difficult to customise and tune for a particular task. NLP is experiencing an explosion in the quantity of electronic text available. Some of this new data will be manually annotated. For example, 10 million words of the American National Corpus (Ide et al., 2002) will have manually corrected POS tags, a tenfold increase over the Penn Treebank (Marcus et al., 1993), currently used for training POS taggers. This will require more efficient learning algorithms and implementations. However, the greatest increase is in the amount of raw text available to be processed, e.g. the English Gigaword Corpus (Linguistic Data Consortium, 2003). Recent work (Banko and Brill, 2001;Curran and Moens, 2002) has suggested that some tasks will benefit from using significantly more data. Also, many potential applications of NLP will involve processing very large text databases. For instance, biomedical text-mining involves extracting information from the vast body of biological and medical literature; and search engines may eventually apply NLP techniques to the whole web. Other potential applications must process text online or in realtime. For example, Google currently answers 250 million queries per day, thus processing time must be minimised. Clearly, efficient NLP components will need to be developed. At the same time, state-of-the-art performance will be needed for these systems to be of practical use. Finally, NLP is growing in terms of the number of tasks, methods and languages being researched. Although many problems share algorithms and data structures there is a tendency to reinvent the wheel. Software engineering research on Generative Programming (Czarnecki and Eisenecker, 2000) attempts to solve these problems by focusing on the development of configurable elementary components and knowledge to combine these components into complete systems. Our infrastructure for NLP will provide high performance 1 components inspired by Generative Programming principles. This paper reviews existing NLP systems and discusses the requirements for an NLP infrastructure. We then describe our overall design and exploratory implementation. We conclude with a discussion of programming interfaces for the infrastructure including a script language and GUI interfaces, and web services for distributed NLP system development. We seek feedback on the overall design and implementation of our proposed infrastructure and to promote discussion about software engineering best practice in NLP. Existing Systems There are a number of generalised NLP systems in the literature. Many provide graphical user interfaces (GUI) for manual annotation (e.g. General Architecture for Text Engineering (GATE) (Cunningham et al., 1997) and the Alembic Workbench (Day et al., 1997)) as well as NLP tools and resources that can be manipulated from the GUI. For instance, GATE currently provides a POS tagger, named entity recogniser and gazetteer and ontology editors (Cunningham et al., 2002). GATE goes beyond earlier systems by using a component-based infrastructure (Cunningham, 2000) which the GUI is built on top of. This allows components to be highly configurable and simplifies the addition of new components to the system. A number of stand-alone tools have also been developed. For example, the suite of LT tools (Mikheev et al., 1999;Grover et al., 2000) perform tokenization, tagging and chunking on XML marked-up text directly. These tools also store their configuration state, e.g. the transduction rules used in LT CHUNK, in XML configuration files. This gives a greater flexibility but the tradeoff is that these tools can run very slowly. Other tools have been designed around particular techniques, such as finite state machines (Karttunen et al., 1997;Mohri et al., 1998). However, the source code for these tools is not freely available, so they cannot be extended. Efficiency has not been a focus for NLP research in general. However, it will be increasingly important as techniques become more complex and corpus sizes grow. An example of this is the estimation of maximum entropy models, from simple iterative estimation algorithms used by Ratnaparkhi (1998) that converge very slowly, to complex techniques from the optimisation literature that converge much more rapidly (Malouf, 2002). Other attempts to address efficiency include the fast Transformation Based Learning (TBL) Toolkit (Ngai and Florian, 2001) which dramatically speeds up training TBL systems, and the translation of TBL rules into finite state machines for very fast tagging (Roche and Schabes, 1997). The TNT POS tagger (Brants, 2000) has also been designed to train and run very quickly, tagging between 30,000 and 60,000 words per second. The Weka package (Witten and Frank, 1999) provides a common framework for several existing machine learning methods including decision trees and support vector machines. This library has been very popular because it allows researchers to experiment with different methods without having to modify code or reformat data. Finally, the Natural Language Toolkit (NLTK) is a package of NLP components implemented in Python (Loper and Bird, 2002). Python scripting is extremely simple to learn, read and write, and so using the existing components and designing new components is simple. Performance Requirements As discussed earlier, there are two main requirements of the system that are covered by "high performance": speed and state of the art accuracy. Efficiency is required both in training and processing. Efficient training is required because the amount of data available for training will increase significantly. Also, advanced methods often require many training iterations, for example active learning (Dagan and Engelson, 1995) and co-training (Blum and Mitchell, 1998). Processing text needs to be extremely efficient since many new applications will require very large quantities of text to be processed or many smaller quantities of text to be processed very quickly. State of the art accuracy is also important, particularly on complex systems since the error is accumulated from each component in the system. There is a speed/accuracy tradeoff that is rarely addressed in the literature. For instance, reducing the beam search width used for tagging can increase the speed without significantly reducing accuracy. Finally, the most accurate systems are often very computationally intensive so a tradeoff may need to be made here. For example, the state of the art POS tagger is an ensemble of individual taggers (van Halteren et al., 2001), each of which must process the text separately. Sophisticated modelling may also give improved accuracy at the cost of training and processing time. The space efficiency of the components is important since complex NLP systems will require many different NLP components to be executing at the same time. Also, language processors many eventually be implemented for relatively low-specification devices such as PDAs. This means that special attention will need to be paid to the data-structures used in the component implementation. The infrastructure should allow most data to be stored on disk (as a configuration option since we must tradeoff speed for space). Accuracy, speed and compactness are the main execution goals. These goals are achieved by implementing the infrastructure in C/C++, and profiling and optimising the algorithms and data-structures used. Design Requirements The remaining requirements relate to the overall and component level design of the system. Following the Generative Programming paradigm, the individual components of the system must be elementary and highly configurable. This ensures minimal redundancy between components and makes them easier to understand, implement, test and debug. It also ensures components are maximally composable and extensible. This is particularly important in NLP because of the high redundancy across tasks and approaches. Machine learning methods should be interchangeable: Transformation-based learning (TBL) (Brill, 1993) and Memory-based learning (MBL) (Daelemans et al., 2002) have been applied to many different problems, so a single interchangeable component should be used to represent each method. We will base these components on the design of Weka (Witten and Frank, 1999). Representations should be reusable: for example, named entity classification can be considered as a sequence tagging task or a bag-of-words text classification task. The same beam-search sequence tagging component should be able to be used for POS tagging, chunking and named entity classification. Feature extraction components should be reusable since many NLP components share features, for instance, most sequence taggers use the previously assigned tags. We will use an objectoriented hierarchy of methods, representations and features to allow components to be easily interchanged. This hierarchy will be developed by analysing the range of methods, representations and features in the literature. High levels of configurability are also very important. Firstly, without high levels of configurability, new systems are not easy to construct by composing existing components, so reinventing the wheel becomes inevitable. Secondly, different languages and tasks show a very wide variation in the methods, representations, and features that are most successful. For instance, a truly multilingual tagger should be able to tag a sequence from left to right or right to left. Finally, this flexibility will allow for research into new tasks and languages to be undertaken with minimal coding. Ease of use is a very important criteria for an infrastructure and high quality documentation and examples are necessary to make sense of the vast array of components in the system. Preconfigured standard components (e.g. an English POS tagger) will be supplied with the infrastructure. More importantly, a Python scripting language interface and a graphical user interface will be built on top of the infrastructure. This will allow components to be configured and composed without expertise in C++. The user interface will generate code to produce standalone components in C++ or Python. Since the Python components will not need to be compiled, they can be distributed immediately. One common difficulty with working on text is the range of file formats and encodings that text can be stored in. The infrastructure will provide components to read/write files in many of these formats including HTML files, text files of varying standard formats, email folders, Postscript, Portable Document Format, Rich Text Format and Microsoft Word files. The infrastructure will also read XML and SGML marked-up files, with and without DTDs and XML Schemas, and provide an XPath/XSLT query interface to select particular subtrees for processing. All of these reading/writing components will use existing open source software. It will also eventually pro-vide components to manipulate groups of files: such as iterate through directories, crawl web pages, get files from ftp, extract files from zip and tar archives. The system will provide full support to standard character sets (e.g. Unicode) and encodings (e.g. UTF-8 and UTF-16). Finally, the infrastructure will provide standard implementations, feature sets and configuration options which means that if the configuration of the components is published, it will be possible for anyone to reproduce published results. This is important because there are many small design decisions that can contribute to the accuracy of a system that are not typically reported in the literature. Components Groups When completed the infrastructure will provide highly configurable components grouped into these broad areas: file processing reading from directories, archives, compressed files, sockets, HTTP and newsgroups; text processing reading/writing marked-up corpora, HTML, emails, standard document formats and text file formats used to represent annotated corpora. lexical processing tokenization, word segmentation and morphological analysis; feature extraction extracting lexical and annotation features from the current context in sequences, bag of words from segments of text data-structures and algorithms efficient lexical representations, lexicons, tagsets and statistics; Viterbi, beam-search and n-best sequence taggers, parsing algorithms; machine learning methods statistical models: Naïve Bayes, Maximum Entropy, Conditional Random Fields; and other methods: Decision Trees and Lists, TBL and MBL; resources APIs to WordNet (Fellbaum, 1998), Google and other lexical resources such as gazetteers, ontologies and machine readable dictionaries; existing tools integrating existing open source components and providing interfaces to existing tools that are only distributed as executables. Implementation The infrastructure will be implemented in C/C++. Templates will be used heavily to provide generality without significantly impacting on efficiency. However, because templates are a static facility we will also provide dynamic versions (using inheritance), which will be slower but accessible from scripting languages and user interfaces. To provide the required configurability in the static version of the code we will use policy templates (Alexandrescu, 2001), and for the dynamic version we will use configuration classes. A key aspect of increasing the efficiency of the system will be using a common text and annotation representation throughout the infrastructure. This means that we do not need to save data to disk, and load it back into memory between each step in the process, which will provide a significant performance increase. Further, we can use techniques for making string matching and other text processing very fast such as making only one copy of each lexical item or annotation in memory. We can also load a lexicon into memory that is shared between all of the components, reducing the memory use. The implementation has been inspired by experience in extracting information from very large corpora (Curran and Moens, 2002) and performing experiments on maximum entropy sequence tagging (Curran and . We have already implemented a POS tagger, chunker, CCG supertagger and named entity recogniser using the infrastructure. These tools currently train in less than 10 minutes on the standard training materials and tag faster than TNT, the fastest existing POS tagger. These tools use a highly optimised GIS implementation and provide sophisticated Gaussian smoothing (Chen and Rosenfeld, 1999). We expect even faster training times when we move to conjugate gradient methods. The next step of the process will be to add different statistical models and machine learning methods. We first plan to add a simple Naïve Bayes model to the system. This will allow us to factor out the maximum entropy specific parts of the system and produce a general component for statistical modelling. We will then implement other machine learning methods and tasks. Interfaces Although C++ is extremely efficient, it is not suitable for rapidly gluing components together to form new tools. To overcome this problem we have implemented an interface to the infrastructure in the Python scripting language. Python has a number of advantages over other options, such as Java and Perl. Python is very easy to learn, read and write, and allows commands to be entered interactively into the interpreter, making it ideal for experimentation. It has already been used to implement a framework for teaching NLP (Loper and Bird, 2002). Using the Boost.Python C++ library (Abrahams, 2003), it is possible to reflect most of the components directly into Python with a minimal amount of coding. The Boost.Python library also allows the C++ code to access new classes written in Python that are derived from the C++ classes. This means that new and extended components can be written in Python (although they will be considerably slower). The Python interface allows the components to be dynamically composed, configured and extended in any operating system environment without the need for a compiler. Finally, since Python can pro-duce stand-alone executables directly, it will be possible to create distributable code that does not require the entire infrastructure or Python interpreter to be installed. The basic Python reflection has already been implemented and used for large scale experiments with POS tagging, using pyMPI (a message passing interface library for Python) to coordinate experiments across a cluster of over 100 machines . An example of using the Python tagger interface is shown in Figure 1. On top of the Python interface we plan to implement a GUI interface for composing and configuring components. This will be implemented in wxPython which is a platform independent GUI library that uses the native windowing environment under Windows, MacOS and most versions of Unix. The wxPython interface will generate C++ and Python code that composes and configures the components. Using the infrastructure, Python and wxPython it will be possible to generate new GUI applications that use NLP technology. Because C++ compilers are now fairly standards compliant, and Python and wxPython are available for most architectures, the infrastructure will be highly portable. Further, we eventually plan to implement interfaces to other languages (in particular Java using the Java Native Interface (JNI) and Perl using the XS interface). Web services The final interface we intend to implement is a collection of web services for NLP. A web service provides a remote procedure that can be called using XML based encodings (XMLRPC or SOAP) of function names, arguments and results transmitted via internet protocols such as HTTP. Systems can automatically discover and communicate with web services that provide the functionality they require by querying databases of standardised descriptions of services with WSDL and UDDI. This standardisation of remote procedures is very exciting from a software engineering viewpoint since it allows systems to be totally distributed. There have already been several attempts to develop distributed NLP systems for dialogue systems (Bayer et al., 2001) and speech recognition (Hacioglu and Pellom, 2003). Web services will allow components developed by different researchers in different locations to be composed to build larger systems. Because web services are of great commercial interest they are already being supported strongly by many programming languages. For instance, web services can be accessed with very little code in Java, Python, Perl, C, C++ and Prolog. This allows us to provide NLP services to many systems that we could not otherwise support using a single interface definition. Since the service arguments and results are primarily text and XML, the web service interface will be reasonably efficient for small Type "help", "copyright", "credits" or "license" for more information. quantities of text (e.g. a single document). The second advantage they have is that there is no startup costs when tagger loads up, which means local copies of the web service could be run to reduce tagging latency. Finally, web services will allow developers of resources such as gazetteers to provide the most up to date resources each time their functionality is required. We are currently in the process of implementing a POS tagging web service using the gSOAP library, which will translate our C infrastructure binding into web service wrapper code and produce the necessary XML service description files. Conclusion The Generative Programming approach to NLP infrastructure development will allow tools such as sentence boundary detectors, POS taggers, chunkers and named entity recognisers to be rapidly composed from many elemental components. For instance, implementing an efficient version of the MXPOST POS tagger (Ratnaparkhi, 1996) will simply involve composing and configuring the appropriate text file reading component, with the sequential tagging component, the collection of feature extraction components and the maximum entropy model component. The individual components will provide state of the art accuracy and be highly optimised for both time and space efficiency. A key design feature of this infrastructure is that components share a common representation for text and annotations so there is no time spent reading/writing formatted data (e.g. XML) between stages. To make the composition and configuration process easier we have implemented a Python scripting interface, which means that anyone can construct efficient new tools, without the need for much programming experience or a compiler. The development of a graphical user interface on top of the infrastructure will further ease the development cycle. Figure 1 : 1>>> import nlp.tagger >>> op = nlp.tagger.Options('models/pos/options') >>> print op nklasses = 46 ... alpha = 1.65 >>> tagger = nlp.tagger.Tagger(op) >>> tags = tagger.tag(['The', 'cat', 'sat', 'on', 'the', 'mat', '.']) >>> print tags ['DT', 'NN', 'VBD', 'IN', 'DT', 'NN', '.'] >>> tagger.tag('infile', 'outfile') >>> Calling the POS tagger interactively from the Python interpreter % python Python 2.2.1 (#1, Sep 30 2002, 20:13:03) [GCC 2.96 20000731 (Red Hat Linux 7.3 2.96-110)] on linux2 AcknowledgementsWe would like to thank Stephen Clark, Tara Murphy, and the anonymous reviewers for their comments on drafts of this paper. This research is supported by a Commonwealth scholarship and a Sydney University Travelling scholarship. Boost.Python C++ library. David Abrahams, David Abrahams. 2003. Boost.Python C++ library. Modern C++ Design: Generic Programming and Design Patterns Applied. Andrei Alexandrescu , C++ In-Depth Series. Addison-WesleyAndrei Alexandrescu. 2001. 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Developments in Affect Detection in E-drama

We report work 1 in progress on adding affect-detection to an existing program for virtual dramatic improvisation, monitored by a human director. To partially automate the directors' functions, we have partially implemented the detection of emotions, etc. in users' text input, by means of pattern-matching, robust parsing and some semantic analysis. The work also involves basic research into how affect is conveyed by metaphor.

Developments in Affect Detection in E-drama Li Zhang l.zhang@cs.bham.ac.uk School of Computer Science University of Birmingham UK John A Barnden School of Computer Science University of Birmingham UK Robert J Hendley School of Computer Science University of Birmingham UK Alan M Wallington School of Computer Science University of Birmingham UK Developments in Affect Detection in E-drama We report work 1 in progress on adding affect-detection to an existing program for virtual dramatic improvisation, monitored by a human director. To partially automate the directors' functions, we have partially implemented the detection of emotions, etc. in users' text input, by means of pattern-matching, robust parsing and some semantic analysis. The work also involves basic research into how affect is conveyed by metaphor. Introduction Improvised drama and role-play are widely used in education, counselling and conflict resolution. Researchers have explored frameworks for edrama, in which virtual characters (avatars) interact under the control of human actors. The springboard for our research is an existing system (edrama) created by Hi8us Midlands Ltd, used in schools for creative writing and teaching in various subjects. The experience suggests that e-drama helps students lose their usual inhibitions, because of anonymity etc. In edrama, characters are completely human-controlled, their speeches textual in speech bubbles, and their visual forms cartoon figures. The actors (users) are given a loose scenario within which to improvise, but are at liberty to be creative. There is also a human director, who constantly monitors the unfolding drama and can intervene by, 1 This work is supported by grant RES-328-25-0009 from the ESRC under the ESRC/EPSRC/DTI "PACCIT" programme. We are grateful to Hi8us Midlands Ltd, Maverick Television Ltd, BT, and our colleagues W.H. Edmondson, S.R. Glasbey, M.G. Lee and Z. Wen. The work is also partially supported by EPSRC grant EP/C538943/1. for example, sending messages to actors, or by introducing and controlling a minor 'bit-part' character to interact with the main characters. But this places a heavy burden on directors, especially if they are, for example, teachers and unpracticed in the directorial role. One research aim is thus partially to automate the directorial functions, which importantly involve affect detection. For instance, a director may intervene when emotions expressed or discussed by characters are not as expected. Hence we have developed an affect-detection module. It has not yet actually been used for direction, but instead to control a simple automated bit-part actor, EmEliza. The module identifies affect in characters' speeches, and makes appropriate responses to help stimulate the improvisation. Within affect we include: basic and complex emotions such as anger and embarrassment; meta-emotions such as desiring to overcome anxiety; moods such as hostility; and value judgments (of goodness, etc.). Although merely detecting affect is limited compared to extracting full meaning, this is often enough for stimulating improvisation. Much research has been done on creating affective virtual characters in interactive systems. Emotion theories, particularly that of Ortony et al. (1988; OCC in the following), have been used widely. Prendinger & Ishizuka (2001) used OCC to reason about emotions. Mehdi et al. (2004) used OCC to generate emotional behaviour. Gratch and Marsella's (2004) model reasons about emotions. However, few systems are aimed at detecting affect as broadly as we do and in open-ended utterances. Although Façade (Mateas, 2002) included processing of open-ended utterances, the broad detection of emotions, rudeness and value judgements is not covered. Zhe & Boucouvalas (2002) demonstrated emotion extraction using a tagger and a chunker to help detect the speaker's own emotions. But it focuses only on emotional adjectives, considers only first-person emotions and neglects deep issues such as figurative expression. Our work is distinctive in several respects. Our interest is not just in (a) the positive first-person case: the affective states that a virtual character X implies that it has (or had or will have, etc.), but also in (b) affect that X implies it lacks, (c) affect that X implies that other characters have or lack, and (d) questions, commands, injunctions, etc. concerning affect. We aim also for the software to cope partially with the important case of metaphorical conveyance of affect (Fussell & Moss, 1998;Kövecses, 1998). Our project does not involve using or developing deep, scientific models of how emotional states, etc., function in cognition. Instead, the deep questions investigated are on linguistic matters such as the metaphorical expression of affect. Also, in studying how people understand and talk about affect, what is of prime importance is their common-sense views of how affect works, irrespective of scientific reality. Metaphor is strongly involved in such views. A Preliminary Approach Various characterizations of emotion are used in emotion theories. The OCC model uses emotion labels and intensity, while Watson and Tellegen (1985) use positive and negative affects as the major dimensions. Currently, we use an evaluation dimension (positive and negative), affect labels and intensity. Affect labels with intensity are used when strong text clues signalling affect are detected, while the evaluation dimension with intensity is used when only weak text clues are detected. Pre-processing Modules The language in the speeches created in e-drama sessions, especially by excited children, severely challenges existing language-analysis tools if accurate semantic information is sought. The language includes misspellings, ungrammaticality, abbreviations (such as in texting), slang, use of upper case and special punctuation (such as repeated exclamation marks) for affective emphasis, repetition of letters or words for emphasis, and open-ended onomatopoeic elements such as "grrrr". The genre is similar to Internet chat. To deal with the misspellings, abbreviations and onomatopoeia, several pre-processing modules are used before the detection of affect starts using pattern matching, syntactic processing by means of the Rasp parser (Briscoe & Carroll, 2002), and subsequent semantic processing. A lookup table has been used to deal with abbreviations e.g. 'im (I am)', 'c u (see you)' and 'l8r (later)'. It includes abbreviations used in Internet chat rooms and others found in an anlysis of previous edrama sessions. We handle ambiguity (e.g.,"2" (to, too, two) in "I'm 2 hungry 2 walk") by considering the POS tags of immediately surrounding words. Such simple processing inevitably leads to errors, but in evaluations using examples in a corpus of 21695 words derived from previous transcripts we have obtained 85.7% accuracy, which is currently adequate. The iconic use of word length (corresponding roughly to imagined sound length) as found both in ordinary words with repeated letters (e.g. 'seeeee') and in onomatopoeia and interjections, (e.g. 'wheee', 'grr', 'grrrrrr', 'agh', 'aaaggghhh') normally implies strong affective states. We have a small dictionary containing base forms of some special words (e.g. 'grr') and some ordinary words that often have letters repeated in e-drama. Then the Metaphone spelling-correction algorithm, which is based on pronunciation, works with the dictionary to locate the base forms of words with letter repetitions. Finally, the Levenshtein distance algorithm with a contemporary English dictionary deals with misspelling. Affect Detection In the first stage after the pre-processing, our affect detection is based on textual patternmatching rules that look for simple grammatical patterns or phrasal templates. Thus keywords, phrases and partial sentence structures are extracted. The Jess rule-based Java framework is used to implement the pattern/template-matching rules. This method has the robustness to deal with ungrammatical and fragmented sentences and varied positioning of sought-after phraseology, but lacks other types of generality and can be fooled by suitable syntactic embedding. For example, if the input is "I doubt she's really angry", rules looking for anger in a simple way will output incorrect results. The transcripts analysed to inspire our initial knowledge base and pattern-matching rules had independently been produced earlier from edrama improvisations based on a school bullying scenario. We have also worked on another, distinctly different scenario concerning a serious disease, based on a TV programme produced by Maverick Television Ltd. The rule sets created for one scenario have a useful degree of applicability to another, although some changes in the specific knowledge database will be needed. As a simple example of our pattern-matching, when the bully character says "Lisa, you Pizza Face! You smell", the module detects that he is insulting Lisa. Patterns such as 'you smell' have been used for rule implementation. The rules work out the character's emotions, evaluation dimension (negative or positive), politeness (rude or polite) and what response EmEliza might make. Although the patterns detected are based on English, we would expect that some of the rules would require little modification to apply to other languages. Multiple exclamation marks and capitalisation of whole words are often used for emphasis in edrama. If exclamation marks or capitalisation are detected, then emotion intensity is deemed to be comparatively high (and emotion is suggested even without other clues). A reasonably good indicator that an inner state is being described is the use of 'I' (see also Craggs and Wood (2004)), especially in combination with the present or future tense. In the school-bullying scenario, when 'I' is followed by a future-tense verb, a threat is normally being expressed; and the utterance is often the shortened version of an implied conditional, e.g., "I'll scream [if you stay here]." When 'I' is followed by a present-tense verb, other emotional states tend to be expressed, as in "I want my mum" and "I hate you". Another useful signal is the imperative mood, especially when used without softeners such as 'please': strong emotions and/or rude attitudes are often being expressed. There are common imperative phrases we deal with explicitly, such as "shut up" and "mind your own business". But, to go beyond the limitations of the pattern matching we have done, we have also used the Rasp parser and semantic information in the form of the semantic profiles for the 1,000 most frequently used English words (Heise, 1965). Although Rasp recognizes many simple imperatives directly, it can parse some imperatives as declaratives or questions. Therefore, further analysis is applied to Rasp's syntactic output. For example, if the subject of an input sentence is 'you' followed by certain special verbs or verb phrases (e.g. 'shut', 'calm', 'get lost', 'go away', etc), and Rasp parses a declarative, then it will be changed to imperative. If the softener 'please' is followed by a base forms of the verb, the inputs are also deemed to be imperatives. If a singular proper noun or 'you' is followed by a base form of the verb, the sentence is deemed to be imperative (e.g. "Dave bring me the menu"). When 'you' or a singular proper noun is fol-lowed by a verb whose base form equals its past tense form, ambiguity arises (e.g. "Lisa hit me"). For one special case of this, if the direct object is 'me', we exploit the evaluation value of the verb from Heise's (1965) semantic profiles. Heise lists values of evaluation (goodness), activation, potency, distance from neutrality, etc. for each word covered. If the evaluation value for the verb is negative, then the sentence is probably not imperative but a declarative expressing a complaint (e.g "Mayid hurt me"). If it has a positive value, then other factors suggesting imperative are checked in this sentence, such as exclamation marks and capitalizations. Previous conversation is checked to see if there is any recent question sentence toward the speaker. If so, then the sentence is taken to be declarative. There is another type of sentence: 'don't you + (base form of verb)', which is often a negative version of an imperative with a 'you' subject (e.g. "Don't you call me a dog"). Normally Rasp regards such strings as questions. Further analysis has also been implemented for such sentence structure, which implies negative affective state, to change the sentence type to imperative. Aside from imperatives, we have also implemented simple types of semantic extraction of affect using affect dictionaries and WordNet. Metaphorical Expression of Affect The explicit metaphorical description of emotional states is common and has been extensively studied (Fussell & Moss, 1998). Examples are "He nearly exploded", and "Joy ran through me." Also, affect is often conveyed implicitly via metaphor, as in "His room is a cess-pit", where affect associated with a source item (cess-pit) is carried over to the corresponding target item. Physical size is often metaphorically used to emphasize evaluations, as in "you are a big bully", "you're a big idiot", and "you're just a little bully", although the bigness may be literal as well. "Big bully" expresses strong disapproval (Sharoff, 2005) and "little bully" can express contempt, although "little" can also convey sympathy. Such examples are not only practically important but also theoretically challenging. We have also encountered quite creative use of metaphor in e-drama. For example, in a school-bullying improvisation that occurred, Mayid had already insulted Lisa by calling her a 'pizza', developing a previous 'pizza-face' insult. Mayid then said "I'll knock your topping off, Lisa" -a theoretically intriguing spontane-ous creative elaboration of the 'pizza' metaphor. Our developing approach to metaphor handling in the affect detection module is partly to look for stock metaphorical phraseology and straightforward variants of it, and partly to use a simple version of the more open-ended, reasoning-based techniques taken from the ATT-Meta project (Barnden et al., 2002;2003;. ATT-Meta includes a general-purpose reasoning engine, and can potentially be used to reason about emotion in relation to other factors in a situation. In turn, the realities of metaphor usage in e-drama sessions are contributing to our basic research on metaphor processing. Conclusion We have implemented a limited degree of affectdetection in an automated actor by means of pattern-matching, robust parsing and some semantic analysis. Although there is a considerable distance to go in terms of the practical affectdetection that we plan to implement, the already implemented detection is able to cause reasonably appropriate contributions by the automated character. We have conducted a two-day pilot user test with 39 secondary school students. We concealed the involvement of an earlier version of EmEliza in some sessions, in order to test by questionnaire whether its involvement affects user satisfaction, etc. None of the measures revealed a significant effect. Also, judging by the group debriefing sessions after the e-drama sessions, nobody found out that one bit-part character was sometimes computer-controlled. Further user testing with students at several Birmingham schools will take place in March 2006. Reasoning in metaphor understanding: The ATT-Meta approach and system. J A Barnden, S R Glasbey, M G Lee, A M Wallington, Proceedings of the 19th International Conference on Computational Linguistics. the 19th International Conference on Computational LinguisticsBarnden, J.A., Glasbey, S.R., Lee, M.G. & Walling- ton, A.M., 2002. Reasoning in metaphor under- standing: The ATT-Meta approach and system. In Proceedings of the 19th International Confer- ence on Computational Linguistics. Domain-transcending mappings in a system for metaphorical reasoning. J A Barnden, S R Glasbey, M G Lee, A M Wallington, Proceedings of the Research Note Sessions of the 10th Conference of EACL. the Research Note Sessions of the 10th Conference of EACLBarnden, J.A., Glasbey, S.R., Lee, M.G. & Walling- ton, A.M., 2003. Domain-transcending mappings in a system for metaphorical reasoning. In Pro- ceedings of the Research Note Sessions of the 10th Conference of EACL. Varieties and Directions of Interdomain. J A Barnden, S R Glasbey, M G Lee, A M Wallington, Influence in Metaphor. Metaphor and Symbol. 191Barnden, J.A., Glasbey, S.R., Lee, M.G. & Walling- ton, A.M. 2004. Varieties and Directions of Inter- domain Influence in Metaphor. Metaphor and Symbol, 19(1), pp.1-30. Robust Accurate Statistical Annotation of General Text. E Briscoe, &amp; J Carroll, Proceedings of the 3rd International Conference on Language Resources and Evaluation, Las Palmas. the 3rd International Conference on Language Resources and Evaluation, Las PalmasGran CanariaBriscoe, E. & J. Carroll. 2002. Robust Accurate Sta- tistical Annotation of General Text. In Proceed- ings of the 3rd International Conference on Language Resources and Evaluation, Las Pal- mas, Gran Canaria. pp.1499-1504. A Two Dimensional Annotation Scheme for Emotion in Dialogue. R Craggs, M Wood, Proceedings of AAAI Spring Symposium: Exploring Attitude and Affect in Text. AAAI Spring Symposium: Exploring Attitude and Affect in TextCraggs, R. & Wood. M. 2004. A Two Dimensional Annotation Scheme for Emotion in Dialogue. In Proceedings of AAAI Spring Symposium: Ex- ploring Attitude and Affect in Text. Figurative Language in Descriptions of Emotional States. S Fussell, M Moss, Social and cognitive approaches to. S. R. Fussell and R. J. Kreuzinterpersonal communicationFussell, S. & Moss, M. 1998. Figurative Language in Descriptions of Emotional States. In S. R. Fussell and R. J. Kreuz (Eds.), Social and cognitive ap- proaches to interpersonal communication. . Lawrence Erlbaum, Lawrence Erlbaum. A Domain-Independent Framework for Modeling Emotion. J Gratch, S Marsella, Journal of Cognitive Systems Research. 5Gratch, J. & Marsella, S. 2004. A Domain- Independent Framework for Modeling Emotion. Journal of Cognitive Systems Research. Vol 5, Issue 4, pp.269-306. Semantic Differential Profiles for 1,000 Most Frequent English Words. Psychological Monographs 79. D R Heise, Heise, D. R. 1965. Semantic Differential Profiles for 1,000 Most Frequent English Words. Psychologi- cal Monographs 79, pp.1-31. Are There Any Emotion-Specific Metaphors?. Z Kövecses, Speaking of Emotions: Conceptualization and Expression. Athanasiadou, A. and Tabakowska, E.Berlin and New YorkMouton de GruyterKövecses, Z. 1998. Are There Any Emotion-Specific Metaphors? In Speaking of Emotions: Concep- tualization and Expression. Athanasiadou, A. and Tabakowska, E. (eds.), Berlin and New York: Mouton de Gruyter, pp.127-151. . M Mateas, Art and Artificial Intelligence. School of Computer Science, Carnegie Mellon UniversityPh.D. Thesis. Interactive DramaMateas, M. 2002. Ph.D. Thesis. Interactive Drama, Art and Artificial Intelligence. School of Computer Science, Carnegie Mellon University. Modeling Character Emotion in an Interactive Virtual Environment. E J Mehdi, P Nico, D Julie, P Bernard, Proceedings of AISB 2004 Symposium: Motion, Emotion and Cognition. AISB 2004 Symposium: Motion, Emotion and CognitionMehdi, E.J., Nico P., Julie D. & Bernard P. 2004. Modeling Character Emotion in an Interactive Vir- tual Environment. In Proceedings of AISB 2004 Symposium: Motion, Emotion and Cognition. . U K Leeds, Leeds, UK. The Cognitive Structure of Emotions. A Ortony, G L Clore, A Collins, Ortony, A., Clore, G.L. & Collins, A. 1988. The Cognitive Structure of Emotions. CUP Simulating Affective Communication with Animated Agents. H Prendinger, M Ishizuka, Proceedings of Eighth IFIP TC.13 Conference on Human-Computer Interaction. Eighth IFIP TC.13 Conference on Human-Computer InteractionTokyo, JapanPrendinger, H. & Ishizuka, M. 2001. Simulating Af- fective Communication with Animated Agents. In Proceedings of Eighth IFIP TC.13 Conference on Human-Computer Interaction, Tokyo, Japan, pp.182-189. How to Handle Lexical Semantics in SFL: a Corpus Study of Purposes for Using Size Adjectives. S Sharoff, Systemic Linguistics and Corpus. LondonContinuumSharoff, S. 2005. How to Handle Lexical Semantics in SFL: a Corpus Study of Purposes for Using Size Adjectives. Systemic Linguistics and Corpus. London: Continuum. Toward a Consensual Structure of Mood. D Watson, A Tellegen, Psychological Bulletin. 98Watson, D. & Tellegen, A. 1985. Toward a Consen- sual Structure of Mood. Psychological Bulletin, 98, pp.219-235. Text-to-Emotion Engine for Real Time Internet Communication. X Zhe, A C Boucouvalas, Zhe, X. & Boucouvalas, A. C. 2002. Text-to-Emotion Engine for Real Time Internet Communication. In

Scaling Understanding up to Mental Spaces

Mental Space Theory(Fauconnier, 1985)encompasses a wide variety of complex linguistics phenomena that are largely ignored in today's natural language processing systems. These phenomena include conditionals (e.g. If sentences), embedded discourse, and other natural language utterances whose interpretation depends on cognitive partitioning of contextual knowledge. A unification-based formalism, Embodied Construction Grammar (ECG) (Chang et al., 2002a) took initial steps to include space as a primitive type, but most of the details are yet to be worked out. The goal of this paper is to present a scalable computational account of mental spaces based on the Neural Theory of Language (NTL) simulation-based understanding framework(Narayanan, 1999;Chang et al., 2002b). We introduce a formalization of mental spaces based on ECG, and describe how this formalization fits into the NTL framework. We will also use English Conditionals as a case study to show how mental spaces can be parameterized from language.

Scaling Understanding up to Mental Spaces Eva Mok emok@icsi.berkeley.edu International Computer Science Institute 1947 Center Street Suite 60094704BerkeleyCA John Bryant jbryant@icsi.berkeley.edu International Computer Science Institute 1947 Center Street Suite 60094704BerkeleyCA Jerome Feldman jfeldman@icsi.berkeley.edu International Computer Science Institute 1947 Center Street Suite 60094704BerkeleyCA Scaling Understanding up to Mental Spaces Mental Space Theory(Fauconnier, 1985)encompasses a wide variety of complex linguistics phenomena that are largely ignored in today's natural language processing systems. These phenomena include conditionals (e.g. If sentences), embedded discourse, and other natural language utterances whose interpretation depends on cognitive partitioning of contextual knowledge. A unification-based formalism, Embodied Construction Grammar (ECG) (Chang et al., 2002a) took initial steps to include space as a primitive type, but most of the details are yet to be worked out. The goal of this paper is to present a scalable computational account of mental spaces based on the Neural Theory of Language (NTL) simulation-based understanding framework(Narayanan, 1999;Chang et al., 2002b). We introduce a formalization of mental spaces based on ECG, and describe how this formalization fits into the NTL framework. We will also use English Conditionals as a case study to show how mental spaces can be parameterized from language. Introduction There are two dimensions to scalability: improving system performance (e.g. speed and size) for a fixed task, and expanding the range of tasks that the system can handle. Today's natural language processing (NLP) systems are not very scalable in the latter dimension. They tend to ignore a wide range of cognitive linguistic phenomena, notably those associated with mental spaces, which are key to understanding any non-trivial piece of natural language. Mental spaces (Fauconnier, 1985) are partial cognitive structures built up during discourse that keep track of entities and relations in dif-ferent contexts. Hypothetical reasoning, depictions (e.g. stories, paintings or movies) and reasoning about other minds are but a few examples where new mental spaces are required. Mental spaces provide an important partitioning of contextual knowledge that allows scalable reasoning in a partitioned large knowledge base. However, the literature on Mental Space Theory does not address how these cognitive structures are specified compositionally, let alone offering formalizations of mental space representations or computational realizations. We thus seek to scale up the complexity of natural language understanding (NLU) systems by proposing a computational method of handling mental spaces. In this paper we give a brief introduction to Mental Space Theory (Section 2), and then explain how Mental Space Theory can be incorporated into the existing Neural Theory of Language (NTL) simulation-based understanding framework (Section 3). In this framework, each mental space is a separate thread of simulation. Each thread of simulation comprises a dynamic structure combining knowledge, event models and beliefs that evolve over time. The use of a simulation-based framework imposes constraints on how mental spaces are represented, and we introduce a formalization of mental spaces based on Embodied Construction Grammar (ECG). As a case study (Section 4), we will walk through the formalization of English Conditionals (Dancygier and Sweetser, 2004) using mental space analysis. Through this case study we illustrate how mental spaces are parameterized compositionally by language, and capture this compositionality succinctly using construction grammar. Then with these formal tools, we address the issue of inference in mental spaces (Section 5), which is at the core of the scaling up understanding. Mental Space Theory Mental spaces refer to the partial cognitive structures built up, usually through discourse, that provide a partitioning of contextual as well as world knowledge. This partitioning in turn affects what inferences can be drawn. In traditional mental space analysis, certain linguistic constructions called space builders may open a new mental space or shift focus to an existing space. Examples are in this picture…, Nancy thinks…, if it rains…, back in the '50s… (Fauconnier, 1997). Consider the following sentence: (1) In Harry's painting of Paris, the Eiffel Tower is only half-finished. Harry's painting creates a new Depiction-Space. The Eiffel Tower is the only entity local to this Depiction-Space, and it maps to the physical Eiffel Tower. However, only the Eiffel Tower in the Depiction-Space has an additional attribute half-finished, and one should not be led to think that the real Eiffel Tower is also halfdone. This kind of space-building is traditionally illustrated in diagrams similar to Figure 1. In the mental space literature, the transfer of assumptions between spaces is guided by the presupposition float principle. The presupposition float principle states that any presupposed structure in the parent space can float to a child space unless it conflicts with structure already in that space. In the above example, any attributes about the real Eiffel Tower can be assumed in the Depiction-Space, as long as they do not depend on it being finished. However, this account of assumption and inference transfer is incomplete. Specifically, it is incorrect to assume that different types of mental spaces obey the same presupposition float principle. For example, if we are having a conversation right now about Harry's painting, very little of what is currently happening should transfer into the Depiction-Space. On the other hand, if we are having a conversation about our plans for tomorrow, our current situation is very relevant to our actions tomorrow, and this information should carry over to the future space. The other key piece that is missing from the presupposition float account is how inference is drawn across spaces in general. Any computational account of mental spaces must address this inference process precisely and supply a formalized representation that inference can operate with. We will outline such a computational solution in the next two sections of this paper. Simulation-Based Understanding A central piece of a scalable computational treatment of mental spaces is a robust language understanding framework. Our work relies on the NTL simulationbased understanding paradigm (Narayanan, 1999;Chang et al., 2002b), and extends the model in a conceptually straightforward way. The simulation-based understanding paradigm stipulates that, in addition to constructional analysis of the surface form, language understanding requires active simulation. The constructional analysis is based on Embodied Construction Grammar (Chang et al., 2002a) which contains four primitive types: schemas, constructions, maps and mental spaces. Schemas are the basic ECG unit of meaning, capturing embodied concepts such as image schemas, actions, and events. Constructions are the basic linguistic unit, pairing meaning schemas with representations of linguistic form (words, clauses, etc.). Maps and mental spaces are the subject of this paper, and will be discussed in detail in the next section. It is worth noting that in the ECG formalism, in addition to support of an inheritance hierarchy (with the keyword subcase of), there is also an evokes relation that makes an outside structure accessible to a schema through a local name. The evokes relation is neither a subcase-of or part-of relation, but is analogous to spreading activation in the neural sense. During analysis, a Semantic Specification (Sem-Spec) is created from the meaning poles of the constructions, and is essentially a network of schemas with the appropriate roles bounded and filled in. Crucially, within this network of schemas are executing schemas (or X-schemas), which are models of events. They are active structures for event-based asynchronous control that can capture both sequential flow and concurrency. Simulation is a dynamic process which includes executing the X-schemas specified in the SemSpec and propagating belief updates in a belief network. This mechanism is used for metaphor understanding in (Narayanan, 1999), and is being generalized to Coordinated Probabilistic Relational Models (CPRM) in current efforts (Narayanan, submitted). The CPRM mechanism is discussed in more detail in Section 5. Within a simulation-based understanding paradigm, each mental space involves a new thread of simulation, with its own separate belief network and simulation trace. This is necessary for keeping track of possibly contradictory beliefs, such as the alternative scenarios where it is sunny or rainy tomorrow. Each alternative scenario exists within its own mental space, and in many situations, there can be a large number of alternatives. However, not only is it computationally expensive Parent-Space painting Eiffel Tower Depiction-Space Eiffel Tower (half-finished) Figure 1. The painting opens a Depiction-Space where the Eiffel Tower is half-finished. to create a new thread of simulation, but cognitive capacity also constrains the number of concurrently open spaces. We need both a cognitively plausible and computationally feasible theory of how mental spaces are manipulated. The insight in addressing this problem is that at any given level of granularity, not all spaces need to be opened at the same time. Harry's painting, in example (1), may be represented at different granularity depending on the context. If it is discussed simply as a wallhanging, the Depiction-Space need not be expanded and the painting should be treated schematically as an object. However, once the contents of the painting are under discussion (e.g., the trip to Paris during which the painting was done), inference in a separate mental space is required, and the Depiction-Space needs to be built. As illustrated by this example, the simulation process dictates the actual building of mental spaces. The analysis process is responsible for supplying all the necessary parameterization of the spaces and their corresponding maps in case they need to be built. As a result, each potential space-builder is represented at two levels of granularity -as an object in its schematic form and as a full mental space. Formalizing this idea in ECG, mental spaces are represented in two ways: as a compressed mental space and an uncompressed version. In the ECG notation in Figure 2, the Compressed-Mental-Space is just a schema, and Mental-Space is of the space primitive type. In each version there is pointer to its counterpart, ums and cms respectively. The role parent-space points to the parent of this space. The uncompressed mental space contains the list of alternatives and the localcontent. Alternatives are scenarios that are different and cannot co-exist, such as the different activities one might be doing tomorrow at noon. Local-content at noon. Local-content provides the local semantics of mental spaces, maintaining a list of predications that are true in this space, ceteris paribus. Each predication contains a role local-to that denotes the space to which it belongs. The predication is then automatically added to the local-content of the space when this role is assigned. Figure 3 shows an example of the Cause-Effect schema with the cause, effect, and local-to role. In the next section, we will demonstrate the use of the above formalization with a case study on conditional sentences in English. In Section 5, we will discuss how this representation supports the needed inference. Case Study: English Conditionals One of the most common classes of space-building expressions is the predictive conditional. Predictive conditionals are sentences like (2) If it rains tomorrow, the game will be cancelled. They are space-builders, setting up a primary conditional space and an alternative space 1 (Dancygier and Sweetser, 2004). As shown in Figure 4, in the primary conditional space for tomorrow, it rains, and therefore the game is cancelled. In the alternative space, it does not rain, and the game is not cancelled. This case study will focus on predictive conditionals in English. An English predictive conditional is characterized by tense backshifting in the if clause, i.e., the use of the present tense for a future event. On the meaning side, the condition and the conclusion are related by some causal or enablement relationship. In this section we will gradually build up to the Conditional-Prediction construction by introducing the relevant schemas and smaller constructions. It is important to stress how construction grammar succinctly captures how mental spaces can be parameterized in a compositional way. The larger constructions supply information about the spaces that is not contained in any of its smaller constituents. Through compositionality, the grammar becomes much more scalable in handling a wide range of linguistic variations. 1 Readers interested in the mental space analysis of other types of English conditionals should refer to (Dancygier and Sweetser, 2004), as well a technical report for the formalization (Bryant and Mok, 2003 Conditions The condition, often preceding the conclusion in a conditional statement, sets up or locates the space in which the conclusion is to be placed. Therefore the Conditional-Schema, given below, is a subcase of the Compressed-Mental-Space. In addition to the inherited roles, the Conditional-Schema has roles for a condition, a premise, and a conclusion. The condition role stands for the condition P as expressed, and premise can be P or ~P, depending on the conjunction. Finally, epistemicstance is the degree of commitment that the speaker is making towards the condition happening, as indicated by the choice of verb tense. For example, a sentence such as If you got me a cup of coffee, I'd be grateful forever (Dancygier and Sweetser, 2004) has a negative epistemic stance. The speaker makes a low commitment (by using the past tense got) so as to not sound presumptuous, even though she may think that the addressee will very likely bring her coffee. On the other hand, a counterfactual such as If I'd've known you were coming, I'd've stayed home has a very negative epistemic stance. The speaker, in this case, implies that he did not know the addressee was coming. The If construction The abstract construction Conditional-Conjunction is a supertype of lexical constructions If and other conditionals conjunctions. Each conjunction leads to a different way of parameterizing the spaces, therefore a Conditional-Conjunction does not have meaning on its own. Instead, it EVOKES two copies of the Conditional-Schema, one as primary and one as alternative. Given the Conditional-Conjunction and the Conditional-Schemas it evokes, the If construction only needs to hook up the correct premise and set the epistemicstance to neutral. The condition itself is not filled in until a larger construction uses the Conditional-Conjunction as a constituent. The Condition Construction The Condition construction forms a Subordinate-Clause from a Conditional-Conjunction and a Clause, such as If it rains tomorrow from our game cancellation example in (2). The most important aspect of this construction is that it identifies its meaning pole (a Conditional-Schema) with the Conditional-Schema that is evoked by the Conditional-Conjunction, thereby preserving all the constraints on the premise, epistemic-stance and status that the conjunction sets up. In addition, it also fills in the content of the condition role with the meaning of the Clause. It sets the parentspace to the current focus-space. Predictions Predictions are not space builders in and of themselves. Instead, they are simply events that are marked as future. The Prediction-Schema is therefore not a subcase of Compressed-Mental-Space. It includes a predictedevent, which is a predication of category Event (denoted through the evokes statement and the binding), and a basis-of-prediction. A predicted event also has an associated probability of happening, which may be supplied linguistically (through hedges like perhaps or probably). This probability directly affects what inferences we draw based on the prediction, and is captured by the likelihood-of-predicted-event role. The Prediction Construction The Prediction construction is a Clause that evokes a Prediction-Schema in its meaning, such as the game will be cancelled. The meaning pole of a Clause is a Predication. The nature of prediction requires that the time reference be future with respect to the viewpoint-space, in this case, today. The meaning of the Prediction construction is itself the predicted-event. Conditional Statements Conditional-Statement is a very general construction that puts a Condition and a Clause together, in unrestricted order. The most important thing to notice is that this larger construction finally fills in the conclusion of the Condition with the meaning pole of the statement. Predictive Conditionals To a first approximation, a Conditional-Prediction is just a special case of the Conditional-Statement where the statement has to be a Prediction. However, extra care has to be taken to ensure that the alternative spaces and cause-effect relations between the premises and conclusions are set up correctly. Recall that the Conditional-Conjunction EVOKES two Conditional-Schemas, which are either partially filled in or not filled in at all. Intuitively, the goal of this construction is to completely fill out these two schemas (and their respective spaces), and put a Cause-Effect relation between the premise and conclusion in the localcontent of each space. A role alt is created with type Conditional-Schema to capture the fact that there is an alternative space parameterized by this construction. alt is then identified with the alternative Conditional-Schema evoked in the Conditional-Conjunction. This allows the unused alternative Conditional-Schema in the If construction to be filled in. The complete filling out of both Conditional-Schemas are done by identifying the premise in the alternative schema with the negation of the premise in the EVOKES Prediction-Schema AS ps ps.predicted-event ↔ self.m Figure 9. The Prediction construction makes itself the predicted-event in the Prediction-Schema primary schema, and like-wise for the conclusion. The epistemic-stance of the alternative schema is set to the opposite of that in the primary. For example, the opposite of a neutral stance is neutral, and the opposite of a negative stance is a positive stance. So far, the only things in the local-content of both spaces are the premise and conclusion, which are just predications without any relations between them. The next two sections assert a Cause-Effect relation between the respective premise and conclusion, and place that in the local-content. It also adds the other space to its list of alternatives. Finally, the last statement identifies the primary cause-effect with ce1 of the predicted-event (in the Event schema), thereby filling in the cause of the predicted-event. Example With the Conditional-Prediction construction and the smaller constructions in hand, along with the related schemas and spaces, we now return to example (2) in the beginning of this section: If it rains tomorrow, the game will be cancelled. Figure 12 shows the schemas and mental spaces that result from the analysis of this sentence. The first half of the sentence, If it rains tomorrow, is an instance of the Condition construction. The Conditional-Conjunction If evokes a primary and an alternative Conditional-Schema, and partially fills out the primary one. Specifically, the If construction sets the epistemic-stance to neutral, and identifies the premise with the condition. The Condition construction then fills in the condition role with the meaning of the Clause it rains tomorrow. For simplicity, the actual schemas for representing a rain event are omitted from the diagram. The second half of the sentence, the game will be cancelled, is an instance of the Prediction construction. The basic job that the Prediction construction performs is to fill in the predicted-event with the actual prediction, i.e., game cancellation tomorrow. At this point, given a Condition with if and a Prediction, an instance of the Conditional-Prediction construction is formed, and the diagram in Figure 12 is completed. The predicted-event is filled into the conclusion in the primary Conditional-Schema. The alternative Conditional-Schema, previously untouched by If, now gets the negated premise and conclusion. A primary Cause-Effect (ce-Primary) is evoked and placed into the local-content of the primary Conditional-Space, and likewise for the alternative space. The two spaces are then linked as alternatives. Inference and Scalability As we discussed in Section 3, the ECG simulation semantics approach to NLU involves both dynamic simulations and extensive belief propagation. This approach leads to systems that are scalable in semantic depth. For such systems to be practical, we also need them to be scalable in size. In recent work, (Narayanan, submitted) has shown how the dynamic simulations and belief propagation techniques can be tightly coupled in a highly scalable formalism called CPRM, Coordinated Probabilistic Relation Models. This same formalism also provides the tools for a systematic treatment of inference across mental spaces, which correspond to separate threads of simulation. Returning to example (2) in the last section, If it rains tomorrow, the game will be cancelled, we can now make additional inference about the hypothetical scenario and the actions of the participants involved. One can ask, what if the game is cancelled and the participants need to plan for other activities? How will these activities affect their actions today? The CPRM mechanism elegantly handles the transfer of assumptions and inferences needed to answer such questions. While the varying types of mental spaces have different rules of inference, all of the couplings are of only two different types, both of which are handled nicely by CPRM. Any two mental spaces will be related either by some shared assumptions, some Figure 11. The Conditional-Prediction construction fills out the parameterization of both the primary and alternative spaces influence links, or both. Since the CPRM formalism is inherently nested, it is straightforward to have shared spaces. For example, if several people are watching a game and are talking about it, the progress of the game is a (dynamic) shared space that is common ground for all the speakers. Influence links are the central primitive in all belief networks, including CPRM. These encode the effect of one attribute on the conditional probabilities of another attribute. In the mental space context, we employ explicit influence links to encode the dependencies of one space on attributes of another space. In the game cancellation example, the participants can choose to further plan for the scenario where it does rain. They might pick a backup plan, for example going to the theater. If this backup plan requires some resource (e.g. a discount card), there should be an influence link back to the present plan suggesting that the discount card be brought along. More generally, each particular kind of mental space relation will have its own types of shared knowledge and influence links. Some of these can be evoked by particular constructions. For example: Harry never agrees with Bob would set up dependency links between our models of the minds of these two individuals. It should be feasible to use these mechanisms to formalize the informal insights in the Cognitive Linguistics litera-ture and therefore significantly extend the range of NLU. Conclusion In this paper we have provided a computational realization of mental spaces. Within a simulation-based understanding framework, a mental space corresponds to a new thread of simulation, implementable using the Coordinated Probabilistic Relational Model formalism. Cognitive and computational constraints demand that each mental space be represented at two levels of granularity: as a schema (compressed-mental-space) and as a full space. The analyzer, using constructions like the ones shown in the case study, creates a Semantic Specification parameterizing the compressed and uncompressed versions of each mental space. Simulation determines the correct level of granularity to operate on, and builds new mental spaces only when it is necessary to perform inference in the new space. Once a new mental space is built, shared spaces and influence links between any two mental spaces can be defined to allow the transfer of inference between the spaces. Our proposed formalization of mental spaces allows systems to be scalable in both size and semantic depth: (i) Our formalization makes explicit how mental spaces partition contextual knowledge into manageable chunks, Figure 12. If it rains tomorrow, the game will be cancelled. thereby providing significant computational advantage. (ii) By using Embodied Construction Grammar, our formalization provides a compositional approach to parameterizing mental spaces. Compositionality has the advantage of allowing a small grammar to handle a large degree of linguistic variation. (iii) During simulation, new threads of simulation are built only as needed, obeying cognitive capacity constraints as well as making mental spaces computationally tractable. (iv) CPRM provides a tightly coupled, scalable inference mechanism that handles the couplings between mental spaces. Our proposed mental space formalism thus provides a precise and scalable means for handling a rich body of complex linguistics phenomena beyond the reach of current NLU systems. Figure 2 . 2ECG Figure 3 . 3Cause-Effect is a predication that contains a pointer to a Mental-Space Figure 6 .Figure 4 . 64Conditional-Conjunctions evokes two Conditional-Schemas. The If construction identifies the premise with the condition in the primary space If it rains tomorrow, the game will be cancelled. A predictive conditional sets up two spaces Figure 7 . 7The Condition construction fills in the content of the condition role Figure 8 .Figure 10 . 810Predictions are not spacebuilders by themselves. CONSTRUCTION Conditional-Statement CONSTRUCTIONAL cond: Condition statement: Clause MEANING cond.conclusion ↔ statement.m The Conditional-Statement construction puts together a Condition and a Clause CONSTRUCTION Prediction SUBCASE OF Clause CONSTRUCTIONAL time-reference relative-future (viewpointspace) MEANING: ) . )SCHEMA Compressed-Mental-Space ROLES ums: Mental-Space parent-space: Mental-Space status CONSTRAINTS self ↔ ums.cmsSPACE Mental-Space ROLES cms: Compressed-Mental-Space parent: Mental-Space alternatives: Mental-Space local-content CONSTRAINTS parent ↔ cms.parent-space ce-primary.cause ↔ cond.premise ce-primary.effect ↔ cond.conclusion ce-primary.local-to cond.msp alt.msp.local-to cond.msp.alternatives ce-alternative.cause ↔ not(cond.premise) ce-alternative.effect ↔ not(cond.conclusion) ce-alternative alt.msp alt.msp.alternatives cond.msp ce-primary ↔ assertion.ps.predictedevent.ce1CONSTRUCTION Conditional-Prediction SUBCASE OF Conditional-Statement CONSTRUCTIONAL statement: Prediction statement.time-reference relative-future (condition.time-reference) MEANING: EVOKES Cause-Effect AS ce-primary EVOKES Cause-Effect AS ce-alternative alt: Conditional-Schema alt ↔ cond.conj.alt alt.premise ↔ not(cond.premise) alt.conclusion ↔ not(cond.conclusion) alt.epistemic-stance opposite (cond.epistemic-stance) Constructing English Conditionals: Building Mental Spaces in ECG. John Bryant, Eva Mok, Technical ReportJohn Bryant and Eva Mok. 2003. Constructing English Conditionals: Building Mental Spaces in ECG. Tech- nical Report. Scaling Cognitive Linguistics: Formalisms for Language Understanding. Nancy Chang, Jerome Feldman, Robert Porzel, Keith Sanders, First International Workshop on Scalable Natural Language Understanding. Nancy Chang, Jerome Feldman, Robert Porzel and Keith Sanders. 2002. Scaling Cognitive Linguistics: Formalisms for Language Understanding. First In- ternational Workshop on Scalable Natural Language Understanding (SCANALU 2002). From Frames to Inference. Nancy Chang, Srini Narayanan, Miriam R L Petruck, First International Workshop on Scalable Natural Language Understanding. Nancy Chang, Srini Narayanan and Miriam R.L. Petruck. 2002. From Frames to Inference. First In- ternational Workshop on Scalable Natural Language Understanding (SCANALU 2002). Barbara Dancygier, Eve Sweetser, Mental Spaces In Grammar: Conditional Constructions. Cambridge University Press. In PressBarbara Dancygier and Eve Sweetser. 2004. Mental Spaces In Grammar: Conditional Constructions. Cambridge University Press. In Press. Mental spaces: Aspects of meaning construction in natural language. Gilles Fauconnier, MIT PressCambridgeGilles Fauconnier. 1985. Mental spaces: Aspects of meaning construction in natural language. Cam- bridge: MIT Press. Mappings in Thought and Language. Gilles Fauconnier, Cambridge University PressNew YorkGilles Fauconnier. 1997. Mappings in Thought and Language. New York: Cambridge University Press. Moving Right Along: A Computational Model of Metaphoric Reasoning about Events. Srini Narayanan, Proceedings of the National Conference on Artificial Intelligence (AAAI '99). the National Conference on Artificial Intelligence (AAAI '99)Orlando, FloridaAAAI PressSrini Narayanan. 1999. Moving Right Along: A Compu- tational Model of Metaphoric Reasoning about Events. Proceedings of the National Conference on Artificial Intelligence (AAAI '99), Orlando, Florida, July 18-22, 1999, pp 121-128, AAAI Press, 1999.

Inducing Neural Models of Script Knowledge

Induction of common sense knowledge about prototypical sequence of events has recently received much attention (e.g., Chambers and Jurafsky (2008); Regneri et al.(2010)). Instead of inducing this knowledge in the form of graphs, as in much of the previous work, in our method, distributed representations of event realizations are computed based on distributed representations of predicates and their arguments, and then these representations are used to predict prototypical event orderings. The parameters of the compositional process for computing the event representations and the ranking component of the model are jointly estimated. We show that this approach results in a substantial boost in performance on the event ordering task with respect to the previous approaches, both on natural and crowdsourced texts.

Inducing Neural Models of Script Knowledge Association for Computational LinguisticsCopyright Association for Computational LinguisticsJune 26-27 2014. 2014 Ashutosh Modi amodi@mmci.uni-saarland.de Saarland University Germany Ivan Titov titov@uva.nl ILLC University of Amsterdam Netherlands Inducing Neural Models of Script Knowledge Proceedings of the Eighteenth Conference on Computational Language Learning the Eighteenth Conference on Computational Language LearningBaltimore, Maryland USAAssociation for Computational LinguisticsJune 26-27 2014. 2014 Induction of common sense knowledge about prototypical sequence of events has recently received much attention (e.g., Chambers and Jurafsky (2008); Regneri et al.(2010)). Instead of inducing this knowledge in the form of graphs, as in much of the previous work, in our method, distributed representations of event realizations are computed based on distributed representations of predicates and their arguments, and then these representations are used to predict prototypical event orderings. The parameters of the compositional process for computing the event representations and the ranking component of the model are jointly estimated. We show that this approach results in a substantial boost in performance on the event ordering task with respect to the previous approaches, both on natural and crowdsourced texts. Introduction It is generally believed that natural language understanding systems would benefit from incorporating common-sense knowledge about prototypical sequences of events and their participants. Early work focused on structured representations of this knowledge (called scripts (Schank and Abelson, 1977)) and manual construction of script knowledge bases. However, these approaches do not scale to complex domains (Mueller, 1998;Gordon, 2001). More recently, automatic induction of script knowledge from text have started to attract attention: these methods exploit either natural texts Jurafsky, 2008, 2009) or crowdsourced data (Regneri et al., 2010), and, consequently, do not require expensive expert annotation. Given a text corpus, they extract structured representations (i.e. graphs), for example chains (Chambers and Jurafsky, 2008) or more general directed acyclic graphs (Regneri et al., 2010). These graphs are scenario-specific, nodes in them correspond to events (and associated with sets of potential event mentions) and arcs encode the temporal precedence relation. These graphs can then be used to inform NLP applications (e.g., question answering) by providing information whether one event is likely to precede or succeed another. Note that these graphs encode common-sense knowledge about prototypical ordering of events rather than temporal order of events as described in a given text. Though representing the script knowledge as graphs is attractive from the human interpretability perspective, it may not be optimal from the application point of view. More specifically, these representations (1) require a model designer to choose an appropriate granularity of event mentions (e.g., whether nodes in the graph should be associated with verbs, or also their arguments); (2) do not provide a mechanism for deciding which scenario applies in a given discourse context and (3) often do not associate confidence levels with information encoded in the graph (e.g., the precedence relation in Regneri et al. (2010)). Instead of constructing a graph and using it to provide information (e.g., prototypical event ordering) to NLP applications, in this work we advocate for constructing a statistical model which is capable to "answer" at least some of the questions these graphs can be used to answer, but doing this without explicitly representing the knowledge as a graph. In our method, the distributed representations (i.e. vectors of real numbers) of event realizations are computed based on distributed representations of predicates and their arguments, and then the event representations are used in a ranker to predict the prototypical ordering of events. Both the parameters of the compositional process for computing the event representation and the rank-ing component of the model are estimated from texts (either relying on unambiguous discourse clues or natural ordering in text). In this way we build on recent research on compositional distributional semantics (Baroni and Zamparelli, 2011;Socher et al., 2012), though our approach specifically focuses on embedding predicate-argument structures rather than arbitrary phrases, and learning these representation to be especially informative for prototypical event ordering. In order to get an intuition why the embedding approach may be attractive, consider a situation where a prototypical ordering of events the bus disembarked passengers and the bus drove away needs to be predicted. An approach based on frequency of predicate pairs (Chambers and Jurafsky, 2008) (henceforth CJ08), is unlikely to make a right prediction as driving usually precedes disembarking. Similarly, an approach which treats the whole predicate-argument structure as an atomic unit (Regneri et al., 2010) will probably fail as well, as such a sparse model is unlikely to be effectively learnable even from large amounts of unlabeled data. However, our embedding method would be expected to capture relevant features of the verb frames, namely, the transitive use for the predicate disembark and the effect of the particle away, and these features will then be used by the ranking component to make the correct prediction. In previous work on learning inference rules (Berant et al., 2011), it has been shown that enforcing transitivity constraints on the inference rules results in significantly improved performance. The same is likely to be true for the event ordering task, as scripts have largely linear structure, and observing that a ≺ b and b ≺ c is likely to imply a ≺ c. Interestingly, in our approach we learn the model which satisfies transitivity constraints, without the need for any explicit global optimization on a graph. This results in a significant boost of performance when using embeddings of just predicates (i.e. ignoring arguments) with respect to using frequencies of ordered verb pairs, as in CJ08 (76% vs. 61% on the natural data). Our model is solely focusing on the ordering task, and admittedly does not represent all the information encoded by a script graph structure. For example, it cannot be directly used to predict a missing event given a set of events (the narrative cloze task (Chambers and Jurafsky, 2009) T a 2 f (e) a 1 = C(bus) a 2 = C(passenger) p = C(disembark) arg embedding hidden layer h Ah Figure 1: Computation of an event representation for a predicate with two arguments (the bus disembarked passengers), an arbitrary number of arguments is supported by our approach. ertheless, we believe that the framework (a probabilistic model using event embeddings as its component) can be extended to represent other aspects of script knowledge by modifying the learning objective, but we leave this for future work. In this paper, we show how our model can be used to predict if two event mentions are likely paraphrases of the same event. The approach is evaluated in two set-ups. First, we consider the crowdsourced dataset of Regneri et al. (2010) and demonstrate that using our model results in the 13.5% absolute improvement in F 1 on event ordering with respect to their graph induction method (84.1% vs. 70.6%). Secondly, we derive an event ordering dataset from the Gigaword corpus, where we also show that the embedding method beats the frequency-based baseline (i.e. reimplementation of the scoring component of CJ08) by 22.8% in accuracy (83.5% vs. 60.7%). Model In this section we describe the model we use for computing event representations as well as the ranking component of our model. Event Representation Learning and exploiting distributed word representations (i.e. vectors of real values, also known as embeddings) have been shown to be beneficial in many NLP applications (Bengio et al., 2001;Turian et al., 2010;Collobert et al., 2011). These representations encode semantic and syntactic properties of a word, and are normally learned in the language modeling setting (i.e. learned to be predictive of local word context), though they can also be specialized by learning in the context of other NLP applications such as PoS tagging or semantic role labeling (Collobert et al., 2011). More recently, the area of distributional compositional semantics have started to emerge (Baroni and Zamparelli, 2011;Socher et al., 2012), they focus on inducing representations of phrases by learning a compositional model. Such a model would compute a representation of a phrase by starting with embeddings of individual words in the phrase, often this composition process is recursive and guided by some form of syntactic structure. In our work, we use a simple compositional model for representing semantics of a verb frame e (i.e. the predicate and its arguments). We will refer to such verb frames as events. The model is shown in Figure 1. Each word c i in the vocabulary is mapped to a real vector based on the corresponding lemma (the embedding function C). The hidden layer is computed by summing linearly transformed predicate and argument 1 embeddings and passing it through the logistic sigmoid function. We use different transformation matrices for arguments and predicates, T and R, respectively. The event representation f (e) is then obtained by applying another linear transform (matrix A) followed by another application of the sigmoid function. Another point to note in here is that, as in previous work on script induction, we use lemmas for predicates and specifically filter out any tense markers as our goal is to induce common-sense knowledge about an event rather than properties predictive of temporal order in a specific discourse context. We leave exploration of more complex and linguistically-motivated models for future work. 2 These event representations are learned in the context of event ranking: the transformation parameters as well as representations of words are forced to be predictive of the temporal order of events. In our experiments, we also consider initialization of predicate and arguments with the SENNA word embeddings (Collobert et al., 2011). Learning to Order The task of learning stereotyped order of events naturally corresponds to the standard ranking setting. We assume that we are provided with sequences of events, and our goal is to capture this order. We discuss how we obtain this learning material in the next section. We learn a linear ranker (characterized by a vector w) which takes an event representation and returns a ranking score. Events are then ordered according to the score to yield the model prediction. Note that during the learning stage we estimate not only w but also the event representation parameters, i.e. matrices T , R and A, and the word embedding C. Note that by casting the event ordering task as a global ranking problem we ensure that the model implicitly exploits transitivity of the relation, the property which is crucial for successful learning from finite amount of data, as we argued in the introduction and will confirm in our experiments. At training time, we assume that each training example k is a list of events e (k) 1 , . . . , e (k) n (k) pro- vided in the stereotypical order (i.e. e (k) i ≺ e (k) j if i < j), n (k) is the length of the list k. We minimize the L 2 -regularized ranking hinge loss: k i<j≤n (k) max(0, 1−w T f (e (k) i ; Θ)+w T f (e (k) j ; Θ)) + α(w 2 + Θ 2 ), where f (e; Θ) is the embedding computed for event e, Θ are all embedding parameters corresponding to elements of the matrices {R, C, T, A}. We use stochastic gradient descent, gradients w.r.t. Θ are computed using back propagation. Experiments We evaluate our approach in two different set-ups. First, we induce the model from the crowdsourced data specifically collected for script induction by Regneri et al. (2010), secondly, we consider an arguably more challenging set-up of learning the model from news data (Gigaword (Parker et al., 2011)), in the latter case we use a learning scenario inspired by Chambers and Jurafsky (2008) 3.1 Learning from Crowdsourced Data 3.1.1 Data and task Regneri et al. (2010) collected descriptions (called event sequence descriptions, ESDs) of various types of human activities (e.g., going to a restaurant, ironing clothes) using crowdsourcing (Amazon Mechanical Turk), this dataset was also complemented by descriptions provided in the OMICS corpus (Gupta and Kochenderfer, 2004). The datasets are fairly small, containing 30 ESDs per activity type in average (we will refer to different activities as scenarios), but in principle the collection can easily be extended given the low cost of crowdsourcing. The ESDs list events forming the scenario and are written in a bullet-point style. The annotators were asked to follow the prototypical event order in writing. As an example, consider a ESD for the scenario prepare coffee : {go to coffee maker} → {fill water in coffee maker} → {place the filter in holder} → {place coffee in filter} → {place holder in coffee maker} → {turn on coffee maker} Regneri et al. also automatically extracted predicates and heads of arguments for each event, as needed for their MSA system and our compositional model. Though individual ESDs may seem simple, the learning task is challenging because of the limited amount of training data, variability in the used vocabulary, optionality of events (e.g., going to the coffee machine may not be mentioned in a ESD), different granularity of events and variability in the ordering (e.g., coffee may be put in the filter before placing it in the coffee maker). Unlike our work, Regneri et al. (2010) relies on WordNet to provide extra signal when using the Multiple Se-quence Alignment (MSA) algorithm. As in their work, each description was preprocessed to extract a predicate and heads of argument noun phrases to be used in the model. The methods are evaluated on human annotated scenario-specific tests: the goal is to classify event pairs as appearing in a stereotypical order or not (Regneri et al., 2010). 4 The model was estimated as explained in Section 2.2 with the order of events in ESDs treated as gold standard. We used 4 held-out scenarios to choose model parameters, no scenario-specific tuning was performed, and the 10 test scripts were not used to perform model selection. The selected model used the dimensionality of 10 for event and word embeddings. The initial learning rate and the regularization parameter were set to 0.005 and 1.0, respectively and both parameters were reduced by the factor of 1.2 every epoch the error function went up. We used 2000 epochs of stochastic gradient descent. Dropout (Hinton et al., 2012) with the rate of 20% was used for the hidden layers in all our experiments. When testing, we predicted that the event pair (e 1 ,e 2 ) is in the stereotypical order (e 1 ≺ e 2 ) if the ranking score for e 1 exceeded the ranking score for e 2 . Results and discussion We evaluated our event embedding model (EE) against baseline systems (BL , MSA and BS). MSA is the system of Regneri et al. (2010). BS is a hierarchical Bayesian model by Frermann et al. (2014). BL chooses the order of events based on the preferred order of the corresponding verbs in the training set: (e 1 , e 2 ) is predicted to be in the stereotypical order if the number of times the corresponding verbs v 1 and v 2 appear in this order in the training ESDs exceeds the number of times they appear in the opposite order (not necessary at adjacent positions); a coin is tossed to break ties (or if v 1 and v 2 are the same verb). This frequency counting method was previously used in CJ08. 5 We also compare to the version of our model which uses only verbs (EE verbs ). Note that EE verbs is conceptually very similar to BL, as it essentially induces an ordering over verbs. However, this ordering can benefit from the implicit transitivity assumption used in EE verbs (and EE), as we discussed in the introduction. The results are presented in Table 1. The first observation is that the full model improves substantially over the baseline and the previous method (MSA) in F1 (13.5% improvement over MSA and 6.5% improvement over BS). Note also that this improvement is consistent across scenarios: EE outperforms MSA and BS on 9 scenarios out of 10 and 8 out of 10 scenarios in case of BS. Unlike MSA and BS, no external knowledge (i.e. WordNet) was exploited in our method. We also observe a substantial improvement in all metrics from using transitivity, as seen by comparing the results of BL and EE verb (11% improvement in F1). This simple approach already substantially outperforms the pipelined MSA system. These results seem to support our hypothesis in the introduction that inducing graph representations from scripts may not be an optimal strategy from the practical perspective. We performed additional experiments using the SENNA embeddings (Collobert et al., 2011). Instead of randomly initializing arguments and predicate embeddings (vectors), we initialized them with pre-trained SENNA embeddings. We have not observed any significant boost in performance from using the initialization (average F1 of 84.0% for EE). We attribute the lack of significant improvement to the following three factors. First of all, the SENNA embeddings tend to place antonyms / opposites near each other (e.g., come and go, or end and start). However, 'opposite' predicates appear in very different positions in scripts. Additionally, the SENNA embeddings have dimensionality of 50 which appears to be 5 They scored permutations of several events by summing the logarithmed differences of the frequencies of ordered verb pairs. However, when applied to event pairs, their approach would yield exactly the same prediction rule as BL. too high for small crowd-sourced datasets, as it forces us to use larger matrices T and R. Moreover, the SENNA embeddings are estimated from Wikipedia, and the activities in our crowdsourced domain are perhaps underrepresented there. Paraphrasing Regneri et al. (2010) additionally measure paraphrasing performance of the MSA system by comparing it to human annotation they obtained: a system needs to predict if a pair of event mentions are paraphrases or not. The dataset contains 527 event pairs for the 10 test scenarios. Each pair consists of events from the same scenario. The dataset is fairly balanced containing from 47 to 60 examples per scenario. This task does not directly map to any statistical inference problem with our model. Instead we use an approach inspired by the interval algebra of Allen (1983). Our ranking model maps event mentions to positions on the time line (see Figure 2). However, it would be more natural to assume that events are intervals rather than points. In principle, these intervals can be overlapping to encode a rich set of temporal relations (see (Allen, 1983)). However, we make a simplifying assumption that the intervals do not overlap and every real number belongs to an interval. In other words, our goal is to induce a segmentation of the line: event mentions corresponding to the same interval are then regarded as paraphrases. One natural constraint on this segmentation is the following: if two event mentions are from the same training ESD, they cannot be assigned to the same interval (as events in ESD are not supposed to be paraphrases). In Figure 2 arcs link event mentions from the same ESD. We look for a segmentation which produces the minimal number of segments and satisfy the above constraint for event mentions appearing in training data. Though inducing intervals given a set of temporal constraints is known to be NP-hard in general (see, e.g., (Golumbic and Shamir, 1993)), for our constraints a simple greedy algorithm finds an optimal solution. We trace the line from the left maintaining a set of event mentions in the current unfinished interval and create a boundary when the constraint is violated; we repeat the process until we processed all mentions. In Figure 2, we would create the first boundary between arrive in a restaurant and order beverages: order bev-E n t e r a r e s t a u r a n t A r r i v e i n a r e s t a u r a n t ... erages and enter a restaurant are from the same ESD and continuing the interval would violate the constraint. It is not hard to see that this results in an optimal segmentation. First, the segmentation satisfies the constraint by construction. Secondly, the number of segments is minimal as the arcs which caused boundary creation are nonoverlapping, each of these arcs needs to be cut and our algorithm cuts each arc exactly once. This algorithm prefers to introduce a boundary as late as possible. For example, it would introduce a boundary between browse a menu and review options in a menu even though the corresponding points are very close on the line. We modify the algorithm by moving the boundaries left as long as this move does not result in new constraint violations and increases margin at boundaries. In our example, the boundary would be moved to be between order beverages and browse a menu, as desired. The resulting performance is reported in Table 2. We report results of our method, as well as results for MSA, BS and a simple all-paraphrase baseline which predict that all mention pairs in a test set are paraphrases (APBL). 6 We can see that interval induction technique results in a lower F1 than that of MSA or BS. This might be partially due to not using external knowledge (WordNet) in our method. We performed extra analyses on the development scenario doorbell. The analyses revealed that the interval induction approach is not very robust to noise: removing a single noisy ESD results in a dramatic change in the interval structure induced and in a significant increase of F1. Consequently, soft versions of the constraint would be beneficial. Alternatively, event embeddings (i.e. continuous vectors) can be clustered directly. We leave this 6 The results for the random baseline are lower: F1 of 40.6% in average. investigation for future work. Learning from Natural Text In the second set of experiments we consider a more challenging problem, inducing knowledge about the stereotyped ordering of events from natural texts. In this work, we are largely inspired by the scenario of CJ08. The overall strategy is the following: we process the Gigaword corpus with a high precision rule-based temporal classifier relying on explicit clues (e.g., "then", "after") to get ordered pairs of events and then we train our model on these pairs (note that clues used by the classifier are removed from the examples, so the model has to rely on verbs and their arguments). Conceptually, the difference between our approach and CJ08 is in using a different temporal classifier, not enforcing that event pairs have the same protagonist, and learning an event embedding model instead of scoring event sequences based on verb-pair frequencies. We also evaluate our system on examples extracted using the same temporal classifier (but validated manually) which allows us to use much larger tests set, and, consequently, provide more detailed and reliable error analysis. Data and task The Gigaword corpus consists of news data from different news agencies and newspapers. For testing and development we took the AFP (Agence France-Presse) section, as it appeared most different from the rest when comparing sets of extracted event pairs (other sections correspond mostly to US agencies). The AFP section was not used for Accuracy (%) BL 60.7 CJ08 60.1 EE verb 75.9 EE 83.5 Table 3: Results on the Gigaword data for the verb-frequency baseline (BL), the verb-only embedding model (EE verb ), the full model (EE) and CJ08 rules. training. This selection strategy was chosen to create a negative bias for our model which is more expressive than the baseline methods and, consequently, better at memorizing examples. As a rule-based temporal classifier, we used high precision "happens-before" rules from the VerbOcean system (Chklovski and Pantel, 2004). Consider "to verb-x and then verb-y" as one example of such rule. We used predicted collapsed Stanford dependencies (de Marneffe et al., 2006) to extract arguments of the verbs, and used only a subset of dependents of a verb. 7 This preprocessing ensured that (1) clues which form part of a pattern are not observable by our model both at train and test time; (2) there is no systematic difference between both events (e.g., for collapsed dependencies, the noun subject is attached to both verbs even if the verbs are conjoined); (3) no information about the order of events in text is available to the models. Applying these rules resulted in 22,446 event pairs for training, and we split additional 1,015 pairs from the AFP section into 812 for final testing and 203 for development. We manually validated random 50 examples and all 50 of them followed the correct temporal order, so we chose not to hand correct the test set. We largely followed the same training and evaluation regime as for the crowdsourced data. We set the regularization parameter and the learning rate to 0.01 and 5.e − 4 respectively. The model was trained for 600 epochs. The embedding sizes were 30 and 50 dimensions for words and events, respectively. Results and discussion In our experiments, as before, we use BL as a baseline, and EE verb as a verb-only simplified version of our approach. We used another baseline 7 The list of dependencies not considered : aux, auxpass, attr, appos, cc, conj, complm, cop, dep, det, punct, mwe. consisting of the verb pair ordering counts provided by Chambers and Jurafsky (2008). 8 We refer this baseline as CJ08. Note also that BL can be regarded as a reimplementation of CJ08 but with a different temporal classifier. We report results in Table 3. The observations are largerly the same as before: (1) the full model substantially outperforms all other approaches (p-level < 0.001 with the permutation test); (2) enforcing transitivity is very helpful (75.9 % for EE verb vs. 60.1% for BL). Surprisingly CJ08 rules produce as good results as BL, suggesting that maybe our learning set-ups are not that different. However, an interesting question is in which situations using a more expressive model, EE, is beneficial. If these accuracy gains have to do with memorizing the data, it may not generalize well to other domains or datasets. In order to test this hypothesis we divided the test examples in three frequency bands according to the frequency of the corresponding verb pairs in the training set (total, in both orders). There are 513, 249 and 50 event pairs in the bands corresponding to unseen pairs of verbs, frequency ≤ 10 and frequency > 10, respectively. These counts emphasize that correct predictions on unseen pairs are crucial and these are exactly where BL would be equivalent to a random guess. Also, this suggest, even before looking into the results, that memorization is irrelevant. The results for BL, CJ08, EE verb and EE are shown in Figure 3. One observation is that most gains for EE and EE verb are due to an improvement on unseen pairs. This is fairly natural, as both transitivity and information about arguments are the only sources of information available. In this context it is important to note that some of the verbs are light, in the sense that they have little semantic content of their own (e.g., take, get) and the event semantics can only be derived from analyzing their arguments (e.g., take an exam vs. take a detour). On the high frequency verb pairs all systems perform equally well, except for CJ08 as it was estimated from somewhat different data. In order to understand how transitivity works, we considered a few unseen predicate pairs where the EE verb model was correctly predicting their order. For many of these pairs there were no infer- Figure 3: Results for different frequency bands: unseen, medium frequency (between 1 and 10) and high frequency (> 10) verb pairs. ence chains of length 2 (e.g., chain of length 2 was found for the pair accept ≺ carry: accept ≺ get and get ≺ carry but not many other pairs). This observation suggest that our model captures some non-trivial transitivity rules. Related Work Additionally to the work on script induction discussed above Jurafsky, 2008, 2009;Regneri et al., 2010), other methods for unsupervised learning of event semantics have been proposed. These methods include unsupervised frame induction techniques (O'Connor, 2012;Modi et al., 2012). Frames encode situations (or objects) along with their participants and properties (Fillmore, 1976). Events in these unsupervised approaches are represented with categorical latent variables, and they are induced relying primarily on the selectional preferences' signal. The very recent work of Cheung et al. (2013) can be regarded as their extension but Cheung et al. also model transitions between events with Markov models. However, neither of these approaches considers (or directly optimizes) the discriminative objective of learning to order events, and neither of them uses distributed representations to encode semantic properties of events. As we pointed out before, our embedding approach is similar (or, in fact, a simplification of) the phrase embedding methods studied in the recent work on distributional compositional semantics (Baroni and Zamparelli, 2011;Socher et al., 2012). However, they have not specifically looked into representing script information. Approaches which study embeddings of relations in knowledge bases (e.g., Riedel et al. (2013)) bear some similar-ity to the methods proposed in this work but they are mostly limited to binary relations and deal with predicting missing relations rather than with temporal reasoning of any kind. Identification of temporal relations within a text is a challenging problem and an active area of research (see, e.g., the TempEval task (UzZaman et al., 2013)). Many rule-based and supervised approaches have been proposed in the past. However, integration of common sense knowledge induced from large-scale unannotated resources still remains a challenge. We believe that our approach will provide a powerful signal complementary to information exploited by most existing methods. Conclusions We have developed a statistical model for representing common sense knowledge about prototypical event orderings. Our model induces distributed representations of events by composing predicate and argument representations. These representations capture properties relevant to predicting stereotyped orderings of events. We learn these representations and the ordering component from unannotated data. We evaluated our model in two different settings: from crowdsourced data and natural news texts. In both set-ups our method outperformed baselines and previously proposed systems by a large margin. This boost in performance is primarily caused by exploiting transitivity of temporal relations and capturing information encoded by predicate arguments. The primary area of future work is to exploit our method in applications such as question answering. Another obvious applications is discovery of temporal relations within documents (Uz-Zaman et al., 2013) where common sense knowledge implicit in script information, induced from large unannotated corpora, should be highly beneficial. Our current model uses a fairly naive semantic composition component, we plan to extend it with more powerful recursive embedding methods which should be especially beneficial when considering very large text collections. on the crowdsourced data for the verb-frequency baseline (BL), the verb-only embedding model (EE verb ), Regneri et al. (2010) (MSA), Frermann et al. (2014)(BS) and the full model (EE). Figure 2 : 2Events on the time line, dotted arcs link events from the same ESD. 2 : 2Paraphrasing results on the crowdsourced data for Regneri et al. (2010) (MSA), Frermann et al. (2014)(BS) and the all-paraphrase baseline (APBL) and using intervals induced from our model (EE). ). Nev-disembarked passengers bus predicate embedding event embedding arg embedding T a 1 Rp Table Only syntactic heads of arguments are used in this work. If an argument is a coffee maker, we will use only the word maker.2 In this study, we apply our model in two very different settings, learning from crowdsourced and natural texts. Crowdsourced collections are relatively small and require not over-expressive models. Details about downloading the data and models are at: http://www.coli.uni-saarland.de/projects/smile/docs/nmReadme.txt The event pairs are not coming from the same ESDs making the task harder as the events may not be in any temporal relation. 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Extracting Events from Industrial Incident Reports August 5-6, 2021 Nitin Ramrakhiyani nitin.ramrakhiyani@tcs.com TCS Research India Swapnil Hingmire swapnil.hingmire@tcs.com TCS Research India Sangameshwar Patil sangameshwar.patil@tcs.com TCS Research India Alok Kumar TCS Research India Girish K Palshikar gk.palshikar@tcs.com TCS Research India Extracting Events from Industrial Incident Reports Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)August 5-6, 202158 Incidents in industries have huge social and political impact and minimizing the consequent damage has been a high priority. However, automated analysis of repositories of incident reports has remained a challenge. In this paper, we focus on automatically extracting events from incident reports. Due to absence of event annotated datasets for industrial incidents we employ a transfer learning based approach which is shown to outperform several baselines. We further provide detailed analysis regarding effect of increase in pre-training data and provide explainability of why pre-training improves the performance. Introduction The industrial revolution 1 has had a profound effect on the socio-political fabric of the world. Economic progress of societies has been highly correlated with their degree of industrialization. However, one of the flip sides of this progress has been the cost of large industrial accidents in terms of injuries to workers, damage to material and property as well as the irreparable loss of innocent human lives. Such major industrial incidents have had large social and political impacts and have prompted policy makers to devise multiple regulations towards prevention of such incidents. As an instance, the huge social uproar after the Bhopal Gas Leakage tragedy 2 had many political ramifications and resulted in creation of many new acts, rules and institutions in India and internationally. Governmental agencies in-charge of industrial safety (OSHA; MINERVA) as well as the industrial enterprises themselves try and minimize the possibility of recurrence of industrial incidents. For this 1 https://en.wikipedia.org/wiki/ Industrial_Revolution 2 https://en.wikipedia.org/wiki/Bhopal_ disaster On February 1, 2014, at approximately 11:37 a.m., a 340 ft.-high guyed telecommunication tower, suddenly collapsed during upgrading activities. Four employees were working on the tower removing its diagonals. In the process, no temporary supports were installed. As a result of the tower 's collapse , two employees were killed and two others were badly injured. Table 1: Sample Incident Report summary from Construction Domain purpose, they carry out detailed investigations of incidents that have previously occurred to identify root causes and suggest preventive actions. In most cases, reports summarizing the incidents as well as their investigation are maintained in incident document repositories 3 . For example, Table 1 shows a sample incident report summary in the construction domain. However, most of these investigative studies are carried out manually. There is little work towards automated processing of repositories of incident reports. Automated processing of incident reports requires us to solve multiple sub-problems such as identification of domain-specific entities, events, different states or conditions, relations between the events, resolving coreferences etc. As an example, we show the entities, events and states marked in red, blue and green respectively in Table 1. In this paper, we focus on an important stage from the above pipeline -extraction of events from incident reports. Event identification is central to the automated processing of incident reports because they pithily capture what exactly happened during an incident. Identification of events is also an important task required for down the line applications such as narrative understanding and visualization through knowledge representations such as Message Se-quence Charts (MSC) (Palshikar et al., 2019;Hingmire et al., 2020) and event timelines (Bedi et al., 2017). Further, most of the work in event detection has focused on events in general domain such as ACE (Linguistic Data Consortium, 2005) and ECB (Bejan and Harabagiu, 2010). Little attention has been paid in the literature towards automated event extraction and analysis from industrial incident reports. To the best of our knowledge, there is no dataset of incident reports comprising of annotations for event identification (spans and attributes). This motivates us to experiment with unsupervised or weakly supervised approaches. In addition to experimenting with unsupervised baselines, we propose a transfer learning approach to extract events which first learns the nature of events in general domain through pre-training and then requires posttraining with minimal training data in the domain of incidents. We consider incident reports from two industriescivil aviation and construction and focus on identifying events involving risk-prone machinery or vehicles, common causes, human injuries and casualties and remedial measures, if any. We show that on both domains, the proposed transfer learning based approach outperforms several unsupervised and weakly supervised baselines. We further supplement the results with detailed analysis regarding effect of increase in pre-training data and explainability of pre-training through a novel clustering based approach. We discuss relevant related work in Section 2. In Section 3, we cover the event extraction process detailing the annotation guidelines and proposed approach. In Section 4, we explain the experimental setup, evaluation and analysis. We finally conclude in Section 5. Related Work This section discusses important related work on two important aspects -automated analysis of textual incident reports/descriptions and unsupervised or weakly supervised event extraction approaches. As per the best of our knowledge, this is the first work on labelling and predicting events (a token level object) from incident report text. However, there are multiple papers which analyze incident reports at the document or sentence level for various tasks such as classification, cause-effect extraction and incident similarity. Tanguy et al.(2016) use NLP techniques to analyze aviation safety re-ports. The authors focus on classification of reports into different categories as well as use probabilistic topic models to analyze different aspects of incidents. The authors also propose the timePlot system to identify similar incident reports. Similar to (Tanguy et al., 2016), (Pence et al., 2020) perform text classification of event reports in nuclear power plants. However, both (Tanguy et al., 2016) and (Pence et al., 2020) do not focus on extraction of specific events from incident reports. Dasgupta et al. (2018) use neural network techniques to extract occupational health and safety related information from News articles related to industrial incidents. Specifically, they focus on extraction of target organization, safety issues, geographical location of the incident and penalty mentioned in the article. In the context of event extraction approaches, multiple state-of-the-art supervised approaches have been proposed in the literature recently. However, the complex neural network architectures demand significant amounts of training data which is not available in the current scenario of event extraction in incident reports. Hence, we discuss two event extraction approaches which are weakly supervised in nature. In (Palshikar et al., 2019), the authors propose a rule based approach which considers all past tense verbs as events with a WordNet based filter retaining only "action" or "communication" events. There is no support for extraction of nominal events proposed by the authors. (Araki and Mitamura, 2018) propose an Open Domain Event Extraction approach which uses linguistic resources like Word-Net and Wikipedia to generate training data in a distantly supervised manner and then train a BiL-STM based supervised event detection model using this data. Wang et al.(2019) propose a weakly supervised approach for event detection. The authors first construct a large-scale event-related candidate set and then use an adversarial training mechanism to identify events. We use the first two approaches - (Palshikar et al., 2019) and (Araki and Mitamura, 2018) as our baselines and discuss them in detail in Section 4. The third approach (Wang et al., 2019) based on adversarial training is evaluated on closeddomain datasets and hence it would be difficult to tune it and use it as a baseline for an open-domain event extraction task like ours. Event Extraction in Incident Reports Events are specific occurrences that appear in the text to denote happenings or changes in states of the involved participants. Multiple guidelines defining events and their extents in text are proposed in the literature (Linguistic Data Consortium, 2005;Mitamura et al., 2017). It is important to note that no event annotated data is available for any incident text dataset and this compels us to consider event extraction approaches which are either unsupervised or involve minimal training data. We make a two fold contribution in this regard. Firstly, we annotate a moderately sized incident text dataset 4 for evaluation and weak supervision. Secondly, we propose a transfer learning approach based on the standard BiLSTM sequence labelling architecture and compare with three baselines from literature. Describing and Annotating Events in Incidents Reports For incident reports, we define events to be specific verbs and nouns which describe pre-incident, incident and post-incident happenings. Though the semantics of the events are specific to this domain, the nature and function of verbs and nouns representing events in standard domains is preserved. In this paper, we focus on extraction of event triggers i.e. the primary verb/noun token indicative of an event, as against an event phrase spanning multiple tokens. Identification of the event triggers is pivotal to the event extraction problem and once an event trigger is identified it is straightforward to construct an event span by collecting specific dependency children of the trigger. We present a set of examples of sentences and their event triggers we focus on extracting in Table 2. The pilot <EVENT>pulled</EVENT> the collective to <EVENT>control</EVENT> the <EVENT>descent</EVENT>. The helicopter <EVENT>crashed</EVENT> in the field and <EVENT>sustained</EVENT> substantial <EVENT>damage</EVENT>. Keeping in mind the domain specific semantics of the events, we choose the Open Event extraction guidelines proposed by (Araki, 2018). We differ with these guidelines at a few places and suitably modify them before guiding our annotators for the task. The details of the differences are described as follows: • (Araki, 2018) suggests labelling of individual adjectives and adverbs as events. Based on our observations of incident text data, we rarely find adjectives or adverbs being "eventive". Hence, we restrict our events to be either verbs (verbbased) or nouns (nominal). • (Araki, 2018) suggests labelling of states and conditions as events. In the current work, we only focus on extraction of instantaneous events and do not extract events describing long-going state-like situations or general factual information. For example, we do not extract had in the sentence The plane had three occupants as an event as it only gives information about the plane but we extract all events such as crashed in the sentence The plane crashed in the sea. • (Araki, 2018) suggests considering light verb constructions (such as "make a turn") as a single combined event. However, we saw a need to consider more such combined verb formulations. As an example, consider the events scheduled and operate in the sentence The plane was scheduled to operate a sight seeing flight. To better capture the complete event semantics, we do not consider these words as separate events but as a single combined event scheduled to operate. Proposed Transfer Learning approach Event extraction can be posed as a supervised sequence labelling problem and a standard BiLSTM-CRF based sequence labeller (Lample et al., 2016) can be employed. However, we reiterate that, as a large event annotated dataset specific to the domain of incident reports is not available, it would be difficult to train such a sequence labeller with high accuracy. We hypothesize that pre-training the BiLSTM-CRF sequence labeller with event labelled data from the general domain would help the network know about the general nature of verbbased and nominal events ("eventiveness"). Later as part of a transfer learning procedure (Yang et al., 2017), post-training of the network on a small event labelled dataset in incidents will provide us with an enriched incident event labeller. The proposed approach is based on this hypothesis and the transfer learnt model is then used to predict event triggers while testing. Table 3. Baselines As the first baseline (B1), we consider the approach proposed in (Palshikar et al., 2019). The authors extract Message Sequence Charts (MSC) from textual narratives which depict messages being passing between actors (entities) in the narrative. Their message extraction approach forms the basis for this event extraction baseline. The approach first identifies past tense verbs and then considers flowing the past tense to its children present tense verbs. It then classifies all identified verbs as either an "action" or "communication" using WordNet hypernyms of the verb itself or its nominal forms and ignores all verbs which are neither actions nor communications (mental events such as thought, envisioned). The approach doesn't extract nominal events, so we supplement this baseline with a simple nominal event extraction technique. We first consider a NomBank (Meyers et al., 2004) based approach which checks each noun for its presence in the NomBank and if found marks it as a nominal event. We also consider another approach based on the deverbal technique proposed by Gurevich et al. (Gurevich et al., 2008), which checks if a candidate noun is the deverbal of any verb in the VerbNet (Palmer et al.). It tags the noun as a nominal event, if such a verb is found. We take a union of the output of the two approaches and filter it using the WordNet to remove obvious false positives (such as entities, etc.) and obtain a final set of nominal events from the given incident report. As the second baseline (B2), we consider on Open Domain Event Extraction technique proposed in (Araki and Mitamura, 2018). Most prior work on extraction of events is restricted to (i) closed domains such as ACE 2005 event ontology and (ii) limited syntactic types. In this paper, the authors highlight a need for open-domain event extraction where events are not restricted to a domain or a syntactic type and hence this becomes a suitable baseline. The authors propose a distant supervision method to identify events. The method comprises of two steps: (i) training data generation, and (ii) event detection. In the first step of distantly supervised data creation, candidate events are identified and filtered using WordNet to disambiguate for their eventiveness. Further, Wikipedia is used to identify events mentioned using proper nouns such as "Hurricane Katrina". Both these steps help to generate lots of good quality (but not gold) training data. In the second step, BiLSTM based supervised event detection model is trained on this distantly generated training data. The experimental results show that the distant supervision improves event detection performance in various domains, without any need for manual annotation of events. As the third baseline (B3), we use the standard BiLSTM based sequence labelling neural network (Lample et al., 2016) employed frequently in information extraction tasks such as Named Entity Recognition (NER). We use the small labelled training dataset to train this BiLSTM based sequence labeller for event identification and use it to extract events while testing. Experimentation Details Word Embeddings For representing the text tokens as input in the proposed neural network approaches, we experiment with the standard static embeddings (GloVe (Pennington et al., 2014)) and the more recent con- Neural Network Design and Tuning The neural network architecture we use for baseline B3 and the proposed transfer learning approach is based on the BiLSTM-CRF architecture proposed by (Lample et al., 2016) for sequence labelling. It is shown in the Figure 1a. As part of the input we concatenate the word embeddings by 20 dimensional learnable POS and NER embeddings. We store these learnt embeddings alongwith the model and reload them during inference. An important aspect to note is that large amount of training data is not available and hence the number of parameters which the network needs to learn should be as minimum as possible to avoid high bias. In particular the connection between the input layer which is 140 dimensional (in case of GloVe embeddings, 100 + 20 P OS + 20 N ER) and the BiLSTM layer (with hidden units 140) is 140 × 140 × 2. In case of 768-dimensional BERT/RoBERTa based representations it blows up about 6 times to 768 × 768 × 2, assuming the LSTM hidden units are also 768. The network fails to learn while training using the limited data in case of 768-dimensional embeddings. So we devise a small change to the input layer to support learning in this case. We introduce a dense layer just after the 768-dimensional BERT/RoBERTa input with a linear activation function to map the 768-dimensional input into a smaller dimensional space, as shown in Figure 1b. Due to the linear activation, this layer behaves like a linear transformation of a high dimensional input vector to a lower dimensional input vector. Additionally, we concatenate the previously mentioned POS and NER learnable embeddings to the transformed input embeddings as the final input to the network. We employ 5-fold cross-validation on the small training dataset for tuning the hyperparameters of the neural network separately for both domains and embedding types. We found minimal difference in hyperparameter values across both Aviation and Construction datasets and hence, we use similar parameters in both cases. The tuned hyperparameters with their values are shown in Table 4. Hyperparameter GloVe based model (Fig. 1a) BERT/ RoBERTa based model (Fig. 1b Implementation Baseline B1 is unsupervised and is implemented and used directly. Code for baseline B2 is made available by the authors 7 and we install and use it without any change. The BiLSTM-CRF sequence labelling networks, used for baseline (B3) and the transfer learning approach, is implemented using keras in python 3. These approaches are trained on the small training data shown in Table 3. To handle randomness in neural network weight initialization and to ensure robustness of the results, we run every neural network experiment (both hyperparameter tuning as well as final test experiments) five times and report an average of the five runs. We were able to observe standard deviation in the precision, recall and F1 of these runs to be as low as 1-2%. With respect to the pre-training data for the transfer learning approach, we use the event annotations from the ECB dataset (Bejan and Harabagiu, 2010). It is a dataset for Event Coreference tasks and has comprehensive event annotations (about 8.8K labelled events in about 9.7K sentences). Evaluation and Analysis As we can observe in Table 5, the proposed transfer learning approach (TL) outperforms the other baselines (B1, B2 and B3) in performance irrespective of static or contextual embeddings. Further, as expected the BiLSTM based baseline B3 shows lower recall than the transfer learning approach in which we see significantly improved recall particularly for the Construction dataset for all embedding types. We observe a similar boost in recall particularly for BERT representations on the Aviation dataset. An important point to note here is that the amount of pre-training data, leading to best results, varies between 40% to 60% for combinations of dataset and embedding type. In Table 5, we report the performance for best amount of pre-training data and present a detailed analysis on effect on increasing pre-training data in Section 4.4.1. As part of the analysis, we first measure the effect of increase in the amount of pre-training data in the transfer learning approach and find out what amount of pre-training leads to the best results. Secondly, we try to explain why the pre-training works through a novel clustering methodology over the BiLSTM learnt context representations of the input embeddings. And thirdly, we present an ensem-7 https://bitbucket.org/junaraki/ coling2018-event Table 5: Evaluation -Event Extraction ble approach considering a practical standpoint of using these systems in real-life use cases. Amount of pre-training data As an important part of the analysis, we measure what is the effect of increase in pre-training data in the transfer learning approach. We hypothesize that the performance would rise till a certain point with increasing pre-training data and would then stabilize and change minimally. This is based on the notion that pre-training positions the network weights in a better space from where the training on domain specific data should begin. However, beyond a certain amount of pre-training the initialization may not lead to any better initial values for the weights. To check the validity of this hypothesis, we pretrained the network with varied amounts of pretraining data (1%, 5%, 10%, 20%, 30%, ..., 100%) and checked the performance on test data. Figure 2 and Figure 3 show the obtained F1 curves for these pre-training settings for Aviation and Construction datasets respectively. As with other experiments, each point in the graphs is an average of performance for 5 runs of training and testing. It can be seen that with increasing pre-training data, the performance improves and reaches a peak between 30% to 70% of pre-training data available, varying for different input embedding types. We observe a small dip in performance when amounts near complete pre-training data are used. Interestingly, BERT based representations start showing promise with even 1% of pre-training data for the Aviation dataset. Explanability of Pre-training To explain why the pre-training is helping, we need to have an understanding of what the network is learning about the input embeddings of the tokens and their context from the bidirectional LSTM. It would be helpful if one could analyze the token- wise output of the BiLSTM layer, which incorporates both the input embeddings and the context information and feeds these representations to the CRF layer as features for sequence learning/inference (See Figure 1a). However, internal representations in a neural network are a set of numbers not comprehensible in a straightforward manner and would require an indirect observation to decipher what is captured by them. One such indirect analysis of these internal representations involves performing their clustering and observing if representations with similar semantics cluster together and rarely cluster with dissimilar representations. In this case, the desired semantics would mean capture of the "eventiveness" property in event tokens. We perform such a clustering based analysis on extractions in the Construction dataset. We consider all tokens which are marked as events in the gold and are also correctly predicted as events by the transfer learnt model (TL) such as tokens t1 and t2 in Table 6. We obtain the BiLSTM output representations for these tokens by passing their sentences through the TL model truncated till the input of the CRF layer and collect these representations (r t1 T L and r t2 T L ) in a set R T L . As observed from the results, the baseline model B3 has a lower recall than the TL model and for tokens such as t1 and t2, we can categorize the predictions of the B3 model into either 'correctly predicted as events' or 'missed and marked as non-events'. We divide these tokens into the correct and incorrect sets as per their baseline model predictions. We obtain the BiLSTM output representations for these tokens from the B3 model in the similar way as earlier and respectively collect these representations (r t1 B3 and r t2 B3 ) in two sets R B3C (B3 corrects) and R B3I (B3 incorrects). We hypothesize that all the representations which lead to a correct event prediction should belong to a subspace of "eventive" representations and should be far from the representations which lead to an incorrect prediction. Hence, representations in the set R T L and R B3C should cluster differently from the representations in the set R B3I . So, in the context of the example tokens of Table 6, representations r t1 T L , r t2 T L and r t1 B3 should cluster differently from r t2 B3 . On performing agglomerative clustering on the above representations with a maximum distance of 0.3 (standard similarity of 0.7), we find that the representations R T L and R B3C belong to multiple clusters which are highly separate from clusters housing the representations in R B3I . This validates our hypothesis and highlights positioning of R T L and R B3C representations closer to the required "eventiveness" subspace and far from the R B3I representation which lead to incorrect predictions. We further strengthen the claim by computing purity (Manning et al., 2008) of the representation clusters. The purity of a clustering gives a measure of the extent to which clusters contains instances of a single class. In case of predictions based on GloVe embeddings models, we observe a purity of 0.9781 and in case of BERT embeddings models, we observe a purity of 0.9832. Practical standpoint We also performed a detailed analysis with regard to the errors in verb-based and nominal event predictions. It was observed that the deep learning approaches miss important verb-based events leading to low recall particularly for the verb-based events, but identify nominal events correctly in most cases. The rule based baseline B1, captures all the verb-based events mostly as it designates most past tense verbs as events. However, the rule based approach fails to identify nominal events correctly as it doesn't observe the context of a noun while deciding its event nature. This observation prompted us to perform a novel ensemble where we create a union of all verb-based event predictions of the rule based approach and all nominal event predictions of the transfer learning based approach using glove embeddings. We believe this ensemble approach holds value from a practical standpoint in two ways. Firstly, using GloVe embeddings eases compute and maintenance requirements in deployment environments, which are higher for handling BERT/RoBERTa based contextual models. Further, as seen from the results in Table 5, GloVe embeddings perform at par with contextual representations. Secondly, when showing a user predictions of events from an incident report, she might get perturbed more because of incorrect nominal events than some extra verbal events. As seen in Table 5, this ensemble approach (row marked as ENS) shows a respectable increase in precision over the Transfer learning approach in both datasets and may be useful to employ in real life incident event identification systems. Conclusion and Future Work In this paper we focused on extracting events from reports on incidents in Aviation and Construction domains. As there is no dataset of incident reports comprising of annotations for event extraction, we contributed by proposing modifications to a set of existing event guidelines and accordingly preparing a small annotated dataset. Keeping in mind the limited data settings, we proposed a transfer learning approach over the existing BiLSTM-CRF based sequence labelling approach and experimented with different static and contextual embeddings. We observed that pretraining improves performance of event extraction for all combinations of domains and embeddings. As part of the analysis, we showed the impact of employing varying amounts of pretraining data. We also performed a novel clustering based analysis to explain why pretraining improves performance of event extraction. We also propose a novel ensemble approach motivated from a practical viewpoint. As future work, we plan to pursue other important stages of the incident report analysis pipeline such as (i) entity/actor identification which involves finding the important participants in an incident, (ii) event argument identification which involves finding participants which are agents or experiencers of the event, (iii) state/condition identification which involve finding expressions describing long-running state-like conditions and (iv) eventevent relation identification which involves establishing of relation links between events. Figure 1 : 1BiLSTM-CRF network models textual embeddings (BERT(Devlin et al., 2018) and RoBERTa). We consider 100-dimensional GloVe embeddings and 768dimensional contextual BERT and RoBERTa representations for the experiments. Figure 2 :Figure 3 : 23Increase Increase in Pre-training Data -Construction Table 2 : 2Examples of event triggers Table 3 : 3Annotated Dataset Statistics from the CONSTRUCTION dataset for both events and event temporal ordering. We treat 10 reports in AVATION and 15 in CONSTRUCTION as a small labelled training dataset. The annotated dataset statistics are presented in4 Experimentation and Evaluation 4.1 Dataset We base our experimentation on incidents from two domains -AVIATION and CONSTRUCTION. To develop the AVIATION dataset, we crawled all the 54 reports about civil aviation incidents 5 recorded in India between 2003 and 2011. For the CONSTRUCTION dataset, we crawled 67 incident report summaries 6 of some major construction in- cidents in New York (May 1990 to July 2019). We annotate 40 incident reports from AVIATION and 45 Table 4 : 4Tuned Hyperparameters Table 6 : 6Example Tokens and Predictions https://www.osha.gov/data the dataset can be obtained through an email request to the authors https://dgca.gov.in/digigov-portal/ ?page=IncidentReports 6 https://www.osha.gov/construction/ engineering Extraction of Event Structures from Text. Jun Araki, Carnegie Mellon UniversityPh.D. thesisJun Araki. 2018. Extraction of Event Structures from Text. Ph.D. thesis, Carnegie Mellon University. Open-Domain Event Detection using Distant Supervision. Jun Araki, Teruko Mitamura, Proceedings of the 27th International Conference on Computational Linguistics (COLING). the 27th International Conference on Computational Linguistics (COLING)Santa Fe, NM, USAJun Araki and Teruko Mitamura. 2018. Open-Domain Event Detection using Distant Supervision. 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Multilingual Affect Polarity and Valence Prediction in Metaphor-Rich Texts

Metaphor is an important way of conveying the affect of people, hence understanding how people use metaphors to convey affect is important for the communication between individuals and increases cohesion if the perceived affect of the concrete example is the same for the two individuals. Therefore, building computational models that can automatically identify the affect in metaphor-rich texts like "The team captain is a rock.", "Time is money.", "My lawyer is a shark." is an important challenging problem, which has been of great interest to the research community.To solve this task, we have collected and manually annotated the affect of metaphor-rich texts for four languages. We present novel algorithms that integrate triggers for cognitive, affective, perceptual and social processes with stylistic and lexical information. By running evaluations on datasets in English, Spanish, Russian and Farsi, we show that the developed affect polarity and valence prediction technology of metaphor-rich texts is portable and works equally well for different languages.

Multilingual Affect Polarity and Valence Prediction in Metaphor-Rich Texts August 4-9 Zornitsa Kozareva kozareva@isi.edu USC Information Sciences Institute 4676 Admiralty Way Marina del Rey90292-6695CA Multilingual Affect Polarity and Valence Prediction in Metaphor-Rich Texts Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics the 51st Annual Meeting of the Association for Computational LinguisticsSofia, BulgariaAugust 4-9 Metaphor is an important way of conveying the affect of people, hence understanding how people use metaphors to convey affect is important for the communication between individuals and increases cohesion if the perceived affect of the concrete example is the same for the two individuals. Therefore, building computational models that can automatically identify the affect in metaphor-rich texts like "The team captain is a rock.", "Time is money.", "My lawyer is a shark." is an important challenging problem, which has been of great interest to the research community.To solve this task, we have collected and manually annotated the affect of metaphor-rich texts for four languages. We present novel algorithms that integrate triggers for cognitive, affective, perceptual and social processes with stylistic and lexical information. By running evaluations on datasets in English, Spanish, Russian and Farsi, we show that the developed affect polarity and valence prediction technology of metaphor-rich texts is portable and works equally well for different languages. Introduction Metaphor is a figure of speech in which a word or phrase that ordinarily designates one thing is used to designate another, thus making an implicit comparison (Lakoff and Johnson, 1980;Martin, 1988;Wilks, 2007). For instance, in "My lawyer is a shark" the speaker may want to communicate that his/her lawyer is strong and aggressive, and that he will attack in court and persist until the goals are achieved. By using the metaphor, the speaker actually conveys positive affect because having an aggressive lawyer is good if one is being sued. There has been a substantial body of work on metaphor identification and interpretation (Wilks, 2007;. However, in this paper we focus on an equally interesting, challenging and important problem, which concerns the automatic identification of affect carried by metaphors. Building such computational models is important to understand how people use metaphors to convey affect and how affect is expressed using metaphors. The existence of such models can be also used to improve the communication between individuals and to make sure that the speakers perceived the affect of the concrete metaphor example in the same way. The questions we address in this paper are: "How can we build computational models that can identify the polarity and valence associated with metaphor-rich texts?" and "Is it possible to build such automatic models for multiple languages?". Our main contributions are: • We have developed multilingual metaphorrich datasets in English, Spanish, Russian and Farsi that contain annotations of the Positive and Negative polarity and the valence (from −3 to +3 scale) corresponding to the intensity of the affect conveyed in the metaphor. • We have proposed and developed automated methods for solving the polarity and valence tasks for all four languages. We model the polarity task as a classification problem, while the valence task as a regression problem. • We have studied the influence of different information sources like the metaphor itself, the context in which it resides, the source and target domains of the metaphor, in addition to contextual features and trigger word lists developed by psychologists (Tausczik and Pennebaker, 2010). • We have conducted in depth experimental evaluation and showed that the developed methods significantly outperform baseline methods. The rest of the paper is organized as follows. Section 2 describes related work, Section 3 briefly talks about metaphors. Sections 4 and 5 describe the polarity classification and valence prediction tasks for affect of metaphor-rich texts. Both sections have information on the collected data for English, Spanish, Russian and Farsi, the conducted experiments and obtained results. Finally, we conclude in Section 6. Related Work A substantial body of work has been done on determining the affect (sentiment analysis) of texts (Kim and Hovy, 2004;Strapparava and Mihalcea, 2007;Wiebe and Cardie, 2005;Yessenalina and Cardie, 2011;Breck et al., 2007). Various tasks have been solved among which polarity and valence identification are the most common. While polarity identification aims at finding the Positive and Negative affect, valence is more challenging as it has to map the affect on a [−3, +3] scale depending on its intensity (Polanyi and Zaenen, 2004;Strapparava and Mihalcea, 2007). Over the years researchers have developed various approaches to identify polarity of words (Esuli andSebastiani, 2006), phrases (Turney, 2002;Wilson et al., 2005), sentences (Choi and Cardie, 2009) even documents (Pang and Lee, 2008). Multiple techniques have been employed, from various machine learning classifiers, to clustering and topic models. Various domains and textual sources have been analyzed such as Twitter, Blogs, Web documents, movie and product reviews (Turney, 2002;Kennedy and Inkpen, 2005;Niu et al., 2005;Pang and Lee, 2008), but yet what is missing is affect analyzer for metaphor-rich texts. While the affect of metaphors is well studied from its linguistic and psychological aspects (Blanchette et al., 2001;Tomlinson and Love, 2006;Crawdord, 2009), to our knowledge the building of computational models for polarity and valence identification in metaphor-rich texts is still a novel task (Smith et al., 2007;Veale, 2012;Veale and Li, 2012;Reyes and Rosso, 2012;Reyes et al., 2013). Little (almost no) effort has been put into multilingual computational affect models of metaphor-rich texts. Our research specifically targets the resolution of these problems and shows that it is possible to build such computational models. The experimental result provide valuable contributions and fundings, which could be used by the research community to build upon. Metaphors Although there are different views on metaphor in linguistics and philosophy (Black, 1962;Lakoff and Johnson, 1980;Gentner, 1983;Wilks, 2007), the common among all approaches is the idea of an interconceptual mapping that underlies the production of metaphorical expressions. There are two concepts or conceptual domains: the target (also called topic in the linguistics literature) and the source (or vehicle), and the existence of a link between them gives rise to metaphors. The texts "Your claims are indefensible." and "He attacked every weak point in my argument." do not directly talk about argument as a war, however the winning or losing of arguments, the attack or defense of positions are structured by the concept of war. There is no physical battle, but there is a verbal battle and the structure of an argument (attack, defense) reflects this (Lakoff and Johnson, 1980). As we mentioned before, there has been a lot of work on the automatic identification of metaphors (Wilks, 2007; and their mapping into conceptual space (Shutova, 2010a;Shutova, 2010b), however these are beyond the scope of this paper. Instead we focus on an equally interesting, challenging and important problem, which concerns the automatic identification of affect carried by metaphors. To conduct our study, we use human annotators to collect metaphor-rich texts (Shutova and Teufel, 2010) and tag each metaphor with its corresponding polarity (Positive/Negative) and valence [−3, +3] scores. The next sections describe the affect polarity and valence tasks we have defined, the collected and annotated metaphor-rich data for each one of the English, Spanish, Russian and Farsi languages, the conducted experiments and obtained results. Task A: Polarity Classification Problem Formulation Task Definition: Given metaphor-rich texts annotated with Positive and Negative polarity labels, the goal is to build an automated computational affect model, which can assign to previously unseen metaphors one of the two polarity classes. a tough pill to swallow values that gave our nation birth Clinton also came into office hoping to bridge Washington's partisan divide. Thirty percent of our mortgages are underwater. The administration, in fact, could go further with the budget knife by eliminating the V-22 Osprey aircraft the 'things' are going to make sure their ox doesn't get gored Figure 1 illustrates the polarity task in which the metaphors were classified into Positive or Negative. For instance, the metaphor "tough pill to swallow" has Negative polarity as it stands for something being hard to digest or comprehend, while the metaphor "values that gave our nation birth" has a Positive polarity as giving birth is like starting a new beginning. Classification Algorithms We model the metaphor polarity task as a classification problem in which, for a given collection of N training examples, where m i is a metaphor and c i is the polarity of m i , the objective is to learn a classification function f : m i → c i in which 1 stands for positive polarity and 0 stands for negative polarity. We tested five different machine learning algorithms such as Nave Bayes, SVM with polynomial kernel, SVM with RBF kernel, AdaBoost and Stacking, out of which AdaBoost performed the best. In our experimental study, we use the freely available implementations in Weka (Witten and Frank, 2005). Evaluation Measures: To evaluate the goodness of the polarity classification algorithms, we calculate the f-score and accuracy on 10-fold cross validation. Data Annotation To conduct our experimental study, we have used annotated data provided by the Language Computer Corporation (LCC) 1 , which developed anno-1 http://www.languagecomputer.com/ tation toolkit specifically for the task of metaphor detection, interpretation and affect assignment. They hired annotators to collect and annotate data for the English, Spanish, Russian and Farsi languages. The domain for which the metaphors were collected was Governance. It encompasses electoral politics, the setting of economic policy, the creation, application and enforcement of rules and laws. The metaphors were collected from political speeches, political websites, online newspapers among others (Mohler et al., 2013). The annotation toolkit allowed annotators to provide for each metaphor the following information: the metaphor, the context in which the metaphor was found, the meaning of the metaphor in the source and target domains from the perspective of a native speaker. For example, in the Context: And to all nations, we will speak for the values that gave our nation birth.; the annotators tagged the Metaphor: values that gave our nation birth; and listed as Source: mother gave birth to baby; and Target: values of freedom and equality motivated the creation of America. The same annotators also provided the affect associated with the metaphor. The agreements of the annotators as measured by LCC are: .83, .87, .80 and .61 for the English, Spanish, Russian and Farsi languages. In our study, the maximum length of a metaphor is a sentence, but typically it has the span of a phrase. The maximum length of a context is three sentences before and after the metaphor, but typically it has the span of one sentence before and after. In our study, the source and target domains are provided by the human annotators who agree on these definitions, however the source and target can be also automatically generated by an interpretation system or a concept mapper. The generation of source and target information is beyond the scope of this paper, but studying their impact on affect is important. At the same time, we want to show that if the technology for source/target detection and interpretation is not yet available, then how far can one reach by using the metaphor itself and the context around it. Later depending on the availability of the information sources and toolkits one can decide whether to integrate such information or to ignore it. In the experimental sections, we show how the individual information sources and their combination affects the resolution of the metaphor polarity and valence prediction tasks. N-gram Evaluation and Results N-gram features are widely used in a variety of classification tasks, therefore we also use them in our polarity classification task. We studied the influence of unigrams, bigrams and a combination of the two, and saw that the best performing feature set consists of the combination of unigrams and bigrams. In this paper, we will refer from now on to n-grams as the combination of unigrams and bigrams. Figure 2 shows a study of the influence of the different information sources and their combination with n-gram features for English. For each information source (metaphor, context, source, target and their combinations), we built a separate n-gram feature set and model, which was evaluated on 10-fold cross validation. The results from this study show that for English, the more information sources one combines, the higher the classification accuracy becomes. Previously LIWC was successfully used to analyze the emotional state of bloggers and tweeters (Quercia et al., 2011) and to identify deception and sarcasm in texts (Ott et al., 2011;González-Ibáñez et al., 2011). When LIWC analyzes texts it generates statistics like number of words found in category C i divided by the total number of words in the text. For our metaphor polarity task, we use LIWC's statistics of all 64 categories and feed this information as features for the machine learning classifiers. LIWC repository contains conceptual categories (dictionaries) both for the English and Spanish languages. LIWC Evaluation and Results: In our experiments LIWC is applied to English and Spanish metaphor-rich texts since the LIWC category dictionaries are available for both languages. Table 3 shows the obtained accuracy and f-score results in English and Spanish for each one of the information sources. Table 3: LIWC features, Accuracy and F-scores on 10-fold validation for English and Spanish !"#$ !%#$ !&#$ !'#$ !(#$ !)#$ !*#$ !+#$ !,#$ %$ - . / The best performances are reached with individual information sources like metaphor, context, source or target instead of their combinations. The classifiers obtain similar performance for both languages. LIWC Category Relevance to Metaphor Polarity: We also study the importance and relevance of the LIWC categories for the metaphor polarity task. We use information gain (IG) to measure the amount of information in bits about the polarity class prediction, if the only information available is the presence of a given LIWC category (feature) and the corresponding polarity class distribution. IG measures the expected reduction in entropy (uncertainty associated with a random feature) (Mitchell, 1997). Figure 3 illustrates how certain categories occur more with the positive (in red color) vs negative (in green color) class. With the positive metaphors we observe the LIWC categories for present tense, social, affect and family, while for the negative metaphors we see LIWC categories for past tense, inhibition and anger. For metaphor texts, these categories are I, conjuntion, anger, discrepancy, swear words among others; for contexts the categories are pronouns like I, you, past tense, friends, affect and so on. Our study shows that some of the LIWC categories are important across all information sources, but overall different triggers activate depending on the information source and the length of the text used. Figure 5 shows a comparison of the accuracy of our best performing approach for each language. For English and Spanish these are the LIWC models, while for Russian and Farsi these are the ngram models. We compare the performance of the algorithms with a majority baseline, which assigns the majority class to each example. For instance, in English there are 3529 annotated examples, of which 2086 are positive and 1443 are negative. Since the positive class is the predominant one for this language and dataset, a majority classifier would have .59 accuracy in returning the positive class as an answer. Similarly, we compute the majority baseline for the rest of the languages. As we can see from Figure 5 that all classifiers significantly outperform the majority base-line. For Farsi the increment is +11.90, while for English the increment is +39.69. This means that the built classifiers perform much better than a random classifier. Comparative study Lessons Learned To summarize, in this section we have defined the task of polarity classification and we have presented a machine learning solution. We have used different feature sets and information sources to solve the task. We have conducted exhaustive evaluations for four different languages namely English, Spanish, Russian and Farsi. The learned lessons from this study are: (1) for n-gram usage, the larger the context of the metaphor, the better the classification accuracy becomes; (2) if present source and target information can further boost the performance of the classifiers; (3) LIWC is a useful resource for polarity identification in metaphor-rich texts; (4) analyzing the usages of tense like past vs. present and pronouns are important triggers for positive and negative polarity of metaphors; (5) some categories like family, social presence indicate positive polarity, while others like inhibition, anger and swear words are indicative of negative affect; (6) the built models significantly outperform majority baselines. Task B: Valence Prediction Problem Formulation Task Definition: Given metaphor-rich texts annotated with valence score (from −3 to +3), where −3 indicates strong negativity, +3 indicates strong positivity, 0 indicates neural, the goal is to build a model that can predict without human supervision the valence scores of new previously unseen metaphors. Thirty percent of our mortgages are underwater. Figure 6: Valence Prediction Figure 6 shows an example of the valence prediction task in which the metaphor-rich texts must be arranged by the intensity of the emotional state provoked by the texts. For instance, −3 corresponds to very strong negativity, −2 strong negativity, −1 weak negativity (similarly for the positive classes). In this task we also consider metaphors with neutral affect. They are annotated with the 0 label and the prediction model should be able to predict such intensity as well. For instance, the metaphor "values that gave our nation birth", is considered by American people that giving birth sets new beginning and has a positive score +1, but "budget knife" is more positive +3 since tax cut is more important. As any sentiment analysis task, affect assignment of metaphors is also a subjective task and the produced annotations express the values, believes and understanding of the annotators. &%# Regression Model We model the valence task a regression problem, in which for a given metaphor m, we seek to predict the valence v of m. We do this via a parametrized function f : v = f (m; w), where w ∈ R d are the weights. The objective is to learn w from a collection of N training examples {< m i , v i >} N i=1 , where m i are the metaphor examples and v i ∈ R is the valence score of m i . Support vector regression (Drucker et al., 1996) is a well-known method for training a regression model by solving the following optimization problem: min w∈R s 1 2 ||w|| 2 + C N N i=1 max(0, |vi − f (mi; w)| − ) -insensitive loss function where C is a regularization constant and controls the training error. The training algorithm finds weights w that define a function f minimizing the empirical risk. Let h be a function from seeds into some vector-space representation ⊆ R d , then the function f takes the form: f (m; w) = h(m) T w = N i=1 α i K(m, m i ), where f is re-parameterized in terms of a polynomial kernel function K with dual weights α i . K measures the similarity between two metaphoric texts. Full details of the regression model and its implementation are beyond the scope of this paper; for more details see (Schölkopf and Smola, 2001;Smola et al., 2003). In our experimental study, we use the freely available implementation of SVM in Weka (Witten and Frank, 2005). Evaluation Measures: To evaluate the quality of the valence prediction model, we compare the actual valence score of the metaphor given by human annotators denoted with y against those valence scores predicted by the regression model denoted with x. We estimate the goodness of the regression model calculating both the correlation coef- ficient cc x,y = n x i y i − x i y i √ n x 2 i −( x i ) 2 √ n y 2 i −( y i ) 2 and the mean squared error mse x,y = n i=i (x−x) n . The two evaluation measures should be interpreted in the following manner. Intuitively the higher the correlation score is, the better the correlation between the actual and the predicted valence scores will be. Similarly the smaller the mean squared error rate, the better the regression model fits the valence predictions to the actual score. Data Annotation To conduct our valence prediction study, we used the same human annotators from the polarity classification task for each one of the English, Spanish, Russian and Farsi languages. We asked the annotators to map each metaphor on a [−3, +3] scale depending on the intensity of the affect associated with the metaphor. Table 4 shows the distribution (number of examples) for each valence class and for each language. -3 -2 -1 0 +1 +2 +3 ENGLISH 1057 817 212 582 157 746 540 SPANISH 106 65 27 17 40 132 262 RUSSIAN 118 42 308 13 202 149 67 FARSI 147 117 120 49 91 63 98 Table 4: Valence Score Distribution for Each Language Empirical Evaluation and Results For each language and information source we built separate valence prediction regression models. We used the same features for the regression task as we have used in the classification task. Those include n-grams (unigrams, bigrams and combination of the two), LIWC scores. Table 5 shows the obtained correlation coefficient (CC) and mean squared error (MSE) results for each one of the four languages (English, Spanish, Russian and Farsi) using the dataset described in Table 4. The Farsi and Russian regression models are based only on n-gram features, while the English and Spanish regression models have both n-gram and LIWC features. Overall, the CC for English and Spanish is higher when LIWC features are used. This means that the LIWC based valence regression model approximates the predicted values better to those of the human annotators. The better valence prediction happens when the metaphor itself is used by LIWC. The MSE for English and Spanish is the lowest, meaning that the prediction is the closest to those of the human annotators. In Russian and Farsi the lowest MSE is when the combined metaphor, source and target information sources are used. For English and Spanish the smallest MSE or so called prediction error is 1.52 and 1.30 respectively, while for Russian and Farsi is 1.62 and 2.13 respectively. Lessons Learned To summarize, in this section we have defined the task of valence prediction of metaphor-rich texts and we have described a regression model for its solution. We have studied different feature sets and information sources to solve the task. We have conducted exhaustive evaluations in all four languages namely English, Spanish, Russian and Farsi. The learned lessons from this study are: (1) valence prediction is a much harder task than polarity classification both for human annotation and for the machine learning algorithms; (2) the obtained results showed that despite its difficulty this is still a plausible problem; (3) similarly to the polarity classification task, valence prediction with LIWC is improved when shorter contexts (the metaphor/source/target information source) are considered. Conclusion People use metaphor-rich language to express affect and often affect is expressed through the usage of metaphors. Therefore, understanding that the metaphor "I was boiling inside when I saw him." has Negative polarity as it conveys feeling of anger is very important for interpersonal or multicultural communications. In this paper, we have introduced a novel corpus of metaphor-rich texts for the English, Spanish, Russian and Farsi languages, which was manually annotated with the polarity and valence scores of the affect conveyed by the metaphors. We have studied the impact of different information sources such as the metaphor in isolation, the context in which the metaphor was used, the source and target domain meanings of the metaphor and RUSSIAN N-gram FARSI N-gram ENGLISH N- their combination in order to understand how such information helps and impacts the interpretation of the affect associated with the metaphor. We have conducted exhaustive evaluation with multiple machine learning classifiers and different features sets spanning from lexical information to psychological categories developed by (Tausczik and Pennebaker, 2010). Through experiments carried out on the developed datasets, we showed that the proposed polarity classification and valence regression models significantly improve baselines (from 11.90% to 39.69% depending on the language) and work well for all four languages. From the two tasks, the valence prediction problem was more challenging both for the human annotators and the automated system. The mean squared error in valence prediction in the range [−3, +3], where −3 indicates strong negative and +3 indicates strong positive affect for English, Spanish and Russian was around 1.5, while for Farsi was around 2. The current findings and learned lessons reflect the properties of the collected data and its annotations. In the future we are interested in studying the affect of metaphors for domains different than Governance. We want to conduct studies with the help of social sciences who would research whether the tagging of affect in metaphors depends on the political affiliation, age, gender or culture of the annotators. Not on a last place, we would like to improve the built valence prediction models and to collect more data for Spanish, Russian and Farsi. Figure 1 : 1Polarity Classification Figure 2 : 2Influence of Information Sources for Metaphor Polarity Classification of English Texts Figure 3 :Figure 4 : 34LIWC category relevance to Metaphor Polarity In addition, we show in Figure 4 examples of the top LIWC categories according to IG ranking for each one of the information sources. Example of LIWC Categories and Words Figure 5 : 5Best Accuracy Model and Comparison against a Majority Baseline for Metaphor Polarity Classification Table 1 1shows the positive and negative class distribution for each one of the four languages.Negative Positive ENGLISH 2086 1443 SPANISH 196 434 RUSSIAN 468 418 FARSI 384 252 Table 1 : 1Polarity Class Distribution for Four Languages The majority of the the annotated examples are for English. However, given the difficulty of finding bilingual speakers, we still managed to collect around 600 examples for Spanish and Farsi, and 886 examples for Russian. Table 2 2shows the influence of the information sources for Spanish, Russian and Farsi with the ngram features. The best f-scores for each language are shown in bold. For Farsi and Russian high performances are obtained both with the context and with the combination of the context, source and target information. While for Spanish they reach similar performance.SPANISH RUSSIAN FARSI Metaphor 71.6 71.0 62.4 Source 67.1 62.4 55.4 Target 68.9 67.2 62.4 Context 73.5 77.1 67.4 S+T 76.6 68.7 62.4 M+S+T 76.0 75.4 64.2 C+S+T 76.5 76.5 68.4 Table 2 : 2N-gram features, F-scores on 10-fold val- idation for Spanish, Russian and Farsi 4.5 LIWC as a Proxy for Metaphor Polarity LIWC Repository: In addition to the n-gram features, we also used the Linguistic Inquiry and Word Count (LIWC) repository (Tausczik and Pennebaker, 2010), which has 64 word categories corresponding to different classes like emotional states, psychological processes, personal concerns among other. Each category contains a list of words characterizing it. For instance, the LIWC category discrepancy contains words like should, could among others, while the LIWC category in- hibition contains words like block, stop, constrain. 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Pharmacological Substances, Compounds Víctor Suárez-Paniagua vspaniag@inf.uc3m.es Computer Science Department Carlos III University of Madrid 28911Leganés, MadridSpain Pharmacological Substances, Compounds 10.5281/zenodo.2560316Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 16-20 Hong Kong, China, November 4, 2019. 16 VSP at PharmaCoNER 2019: Recognition of This paper presents the participation of the VSP team for the PharmaCoNER Tracks from the BioNLP Open Shared Task 2019. The system consists of a neural model for the Named Entity Recognition of drugs, medications and chemical entities in Spanish and the use of the Spanish Edition of SNOMED CT term search engine for the concept normalization of the recognized mentions. The neural network is implemented with two bidirectional Recurrent Neural Networks with LSTM cells that creates a feature vector for each word of the sentences in order to classify the entities. The first layer uses the characters of each word and the resulting vector is aggregated to the second layer together with its word embedding in order to create the feature vector of the word. In addition, a Conditional Random Field layer classifies the vector representation of each word in one of the mention types. The system obtains a performance of 76.29%, and 60.34% in F1 for the classification of the Named Entity Recognition task and the Concept indexing task, respectively. This method presents good results with a basic approach without using pretrained word embeddings or any hand-crafted features. Introduction Nowadays, the task of finding the essential data about the patients in medical records is very difficult because of the highly increasing amount of unstructured documents generated by the doctors. Thus, the automatic extraction of the mentions related with drugs, medications and chemical entities in the clinical case studies can reduces the time of healthcare professionals expend reviewing these medical documents in order to retrieve the most relevant information. Previously, some Natural Language Processing (NLP) shared tasks were organized in order to promote the develop of automatic systems given the importance of this task. The i2b2 shared task was the first NLP challenge for identifying Protected Health Information in the clinical narratives (Özlem Uzuner et al., 2007). The CHEMDNER task was focused on the Named Entity Recognition (NER) of chemical compounds and drug names in PubMed abstracts and chemistry journals (Krallinger et al., 2015). The goal of the BioNLP Open Shared Task 2019 is to create NLP challenges for developing systems in order to extract information from biomedical corpora. Concretely, the Pharma-CoNER Task is focusing on the recognition of pharmacological substance, compound and protein mentions from Spanish medical texts. Currently, deep learning approaches overcome traditional machine learning systems on the majority of NLP tasks, such as text classification (Kim, 2014), language modeling (Mikolov et al., 2013) and machine translation (Cho et al., 2014). Moreover, these models have the advantage of automatically learn the most relevant features without defining rules by hand. Concretely, the LSTM-CRF Model proposed by (Lample et al., 2016) improves the performance of a CRF with handcrafted features for different biomedical NER tasks (Habibi et al., 2017). The main idea of this system is to create a word vector representation using a bidirectional Recurrent Neural Network with LSTM cells (BiLSTM) with character information encoded in another BiL-STM layer in order to classify the tag of each word in the sentences with a CRF classifier. Following this approach, the system proposed in (Dernoncourt et al., 2016) uses a BiLSTM-CRF Model with character and word levels for the de-identification of patient notes using the i2b2 dataset that overcomes the previous systems in this task. This paper presents the participation of the author, as VSP team, at the tasks proposed by PharmaCoNER about the classification of pharmacological substances, compounds and proteins and the Concept Indexing of the recognized mentions from clinical cases in Spanish. The proposed system follows the same approaches of (Lample et al., 2016) and (Dernoncourt et al., 2016) for the NER task with some modifications for the Spanish language implemented with NeuroNER tool (Dernoncourt et al., 2017) because the architecture obtains good performance for the recognition of biomedical entities. In addition, a simple SNOMED CT term search engine is implemented for the concept normalization. Dataset The corpus of the PharmaCoNER task contains 1,000 clinical cases derived from the Spanish Clinical Case Corpus (SPACCC) 1 with manually annotated mentions such as pharmacological substances, compounds and proteins by clinical documentalists. The documents are randomly divided into the training, validation and test sets for creating, developing and ranking the different systems, respectively. The corpus contains four different entity types: • NORMALIZABLES : they are chemicals that can be normalized to a unique concept identifier. • NO NORMALIZABLES : they are chemicals that cannot be normalized. These mentions were used for training the system, but they were not taken into consideration for the results in the task of NER or Concept Indexing. • PROTEINAS : this entity type refers to mentions of proteins and genes following the annotation schema of BioCreative GPRO (Pérez-Pérez et al., 2017). Method This section presents the Neural architecture for the classification of the entity types and the concept normalization method in Spanish clinical cases. Figure 1 presents the process of the NER task using two BiLSTMs for the character and token levels in order to create each word representation until its classification by a CRF. Data preprocessing The first step is a preprocessing of the sentences in the corpus, which prepares the inputs for the neural model. Firstly, the clinical cases are separated into sentences using a sentence splitter and the words of these sentences are extracted by a tokenizer, both were adapted for the Spanish language. For the experiments, the previous processes were performed by the spaCy tool in Python (Explosion AI, 2017). Once the sentences were divided into word, the BIOES tag schema encodes each token with an entity type (B tag is the beginning token, I tag is the inside token, E tag is the ending token, S tag is the single token and O tag is the outside token). In many previous NER tasks, using this codification is better than the BIO tag scheme (Ratinov and Roth, 2009), but the number of labels increases because there are two additional tags for each class. Thus, the number of possible classes are the 4 tags times the 4 entity types and the O tag for the Phar-maCoNER corpus. BiLSTM layers RNNs are very effective in feature learning when the inputs are sequences. Concretely, the Long Short-Term Memory cell (LSTM) (Hochreiter and Schmidhuber, 1997) defines four gates for creating the representation of each input taking the information of the current and previous cells. Thus, each output is a combination of the current and the previous cell states. Furthermore, another LSTM can be applied in the other direction from the end of the sequence to the start in order to extract the relevant features of each input in both directions. Character level The first layer takes each word of the sentences individually. These tokens are decomposed into characters that are the input of the BiL-STM. Once all the inputs are computed by the network, the last output vectors of both directions are concatenated in order to create the vector representation of the word according to its characters. Token level The second layer takes the embedding of each word in the sentence and concatenates them with the outputs of the first BiLSTM with the character representation. In addition, a Dropout layer is applied to the word representation in order to prevent overfitting in the training phase. In this case, the outputs of each direction in one token are concatenated for the classification layer. Contional Random Field Classifier CRF (Lafferty et al., 2001) is the sequential version of the Softmax that aggregates the label predicted in the previous output as part of the input. In NER tasks, CRF shows better results than Softmax because it adds a higher probability to the correct labelled sequence. For instance, the I tag cannot be before a B tag or after a E tag by definition. For the proposed system, the CRF classifies the output vector of the BiLSTM layer with the token information in one of the classes. Concept Indexing After the NER task, the concept indexing is applied to all recognized entities in the sentences for the term normalization. To this end, the Spanish Edition of the SNOMED CT International Browser 2 searches each mention and gives its normalization term. Moreover, The Spanish Medical Abbreviation DataBase (AbreMES-DB) 3 is used in order to disambiguate the acronyms and the resulting term is searched in the SNOMED CT International Browser. In the cases where there are more than one normalization concept for a term, a very naive approach is followed where the first node in the term list is chosen as the final output. Results and Discussion The architecture was trained over the training set during 100 epochs with shuffled minibatches and choosing the best performance over the validation set via stopping criteria. The values of the two BiLSTM and CRF parameters for generating the prediction of the test set are presented in Table 1. Additionally, a gradient clipping keeps the weight of the network in a low range preventing the exploding gradient problem. The embeddings of the characters and words are randomly initialized and learned during the training of the network. The main goal of this work is to test the performance of the proposed neural model on this dataset without using pretrained word embeddings or any hand-crafted features. In future work, the impact of different pretrained word embeddings will be covered. The results were measured with precision (P), recall (R) and F-measure (F1) using the True Positives (TP), False Positives (FP) and False Negatives (FN) for its calculation. Table 2 presents the results of the system over the test set of the PharmaCoNER tasks. The performance for the entity type classification and the performance for the Concept Indexing task are 76.29% and 60.34% in F1, respectively. Table 3 presents the results of the NER task for each entity type independently. It can be observed that the number of FN is higher than FP in all the classes giving better results in Precision than in Recall. The performance of the classes are directly proportional of the number of instances in the training set. In order to alleviate this problem, the use of oversampling techniques will be tackled in future works to increase the number of examples of the less representative classes and making this dataset more balanced. Conclusions and Future work This paper presents a model where a neural model classifies mentions from clinical texts in Spanish and the Concept Indexing uses the SNOMED CT search engine for their normalization. The neural architecture is based on RNNs in both direction of the sentences using LSTM for the computation of the outputs. Finally, a CRF classifier performs the classification for tagging the entity types. The results shows a performance of 76.29% in F1 for the classification of the pharmacological substances, compounds and proteins in the Phar-maCoNER corpus and the normalization system reaches to 60.34% in F1. In spite of the basic approaches, the results are very promising in both tasks. As future work, it is proposed to pretrain the word embeddings with collections of biomedical documents and the aggregation of other embeddings such as Partof-Speech tags, syntactic parse trees or semantic tags, that could increase the representation of each word in order to improve its classification. Moreover, fine-tuning the parameters of the model according to the Pharma-CoNER corpus will be useful in order to increase the performance of the method. Furthermore, adding more layers to each BiLSTM is proposed to be included in the architecture. In addition, other complex concept indexing rules could be applied to chose the best nor- malization term in the cases that they are multiple possibilities. Figure 1 : 1Neural model for the recognition of mentions in Spanish clinical cases using the PharmaCoNER task 2019 corpus. Table 1 : 1The parameters of the neural model and their values used for the PharmaCoNER results.Parameter Value Character embeddings dimension 25 Character-level LSTM hidden units 25 Word embeddings dimension 300 Word-level LSTM hidden units 256 Optimizer SGD Learning rate 0.001 Dropout rate 0.5 Gradient clipping 5 Table 2 : 2Official results of the neural Model for the two tasks of the PharmaCoNER.Task R P F1 NER 71.61% 81.62% 76.29% Concept Indexing 55.22% 66.5% 60.34% Table 3 : 3Performance of the neural model for each category in the Named Entity Recognition Task of the PharmaCoNER.Label TP FN FP R P F1 NORMALIZABLES 707 266 94 72.66% 88.26% 79.71% PROTEINAS 612 247 203 71.25% 75.09% 73.12% UNCLEAR 20 14 6 58.82% 76.92% 66.67% https://doi.org/10.5281/zenodo.2560316 • UNCLEAR: these mentions are cases of general substances, such as pharmaceutical formulations, general treatments, chemotherapy programs, vaccines and a predefined set of general substances.Additionally, all mentions without the NO NORMALIZABLES tag are annotated with its corresponding SNOMED CT normalization concept. https://prod-browser-exten.ihtsdotools. org/ https://zenodo.org/record/2207130# .XHPEFYUo85k Learning phrase representations using RNN encoder-decoder for statistical machine translation. Kyunghyun Cho, Caglar Bart Van Merrienboer, Dzmitry Gulcehre, Fethi Bahdanau, Holger Bougares, Yoshua Schwenk, Bengio, 10.3115/v1/D14-1179Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)Doha, QatarAssociation for Computational LinguisticsKyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine trans- lation. In Proceedings of the 2014 Confer- ence on Empirical Methods in Natural Language Processing (EMNLP), pages 1724-1734, Doha, Qatar. Association for Computational Linguis- tics. NeuroNER: an easy-to-use program for named-entity recognition based on neural networks. 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The FLaReNet Thematic Network: A Global Forum for Cooperation

The aim of this short paper is to present the FLaReNet Thematic Network for Language Resources and Language Technologies to the Asian Language Resources Community. Creation of a wide and committed community and of a shared policy in the field of Language Resources is essential in order to foster a substantial advancement of the field. This paper presents the background, overall objectives and methodology of work of the project, as well as a set of preliminary results.

The FLaReNet Thematic Network: A Global Forum for Cooperation 2009. August 2009. 2009 Nicoletta Calzolari nicoletta.calzolari@ilc.cnr.it Consiglio Nazionale delle Ricerche Istituto di Linguistica Computazionale "A. Zampolli" Consiglio Nazionale delle Ricerche Istituto di Linguistica Computazionale "A. Zampolli" Claudia Soria claudia.soria@ilc.cnr.it Consiglio Nazionale delle Ricerche Istituto di Linguistica Computazionale "A. Zampolli" Consiglio Nazionale delle Ricerche Istituto di Linguistica Computazionale "A. Zampolli" The FLaReNet Thematic Network: A Global Forum for Cooperation ACL and AFNLP the 7th Workshop on Asian Language ResourcesSuntec, Singapore, 6-72009. August 2009. 2009 The aim of this short paper is to present the FLaReNet Thematic Network for Language Resources and Language Technologies to the Asian Language Resources Community. Creation of a wide and committed community and of a shared policy in the field of Language Resources is essential in order to foster a substantial advancement of the field. This paper presents the background, overall objectives and methodology of work of the project, as well as a set of preliminary results. Introduction The field of Language Resources and Technologies has been developing for years to reach now a stable and consolidated status, attaining the right to be considered a discipline in itself, and as testified by the number of conferences and publications explicitly dedicated to the topic. Even if Language Resources (in the widest sense, i.e. spoken, written and multimodal resources and basic related tools) have a rather short history, they are nowadays recognized as one of the pillars of NLP. The availability of adequate Language Resources for as many languages as possible is a pre-requisite for the development of a truly multilingual Information Society. At the same time, however, the discipline has seen considerable fragmentation during those years of fast and enthusiast development, and the landscape is now composed by a kaleidoscope of different, often conflicting initiatives that vary as for research directions, theoretical approaches, implementation choices, distribution and access policies, languages, domain and modalities covered, etc. The growth of the field in the last years should be now complemented by a common reflection and by an effort that identifies synergies and overcomes fragmentation. The consolidation of the area is a pre-condition to enhance competitiveness at EU level and worldwide. There is the need of working together to define common strategies and to identify priorities for the field to advance. Multiple concurring signs are now indicating that time is ripe for establishing an open language infrastructure, something that many of us have been pushing since some time and that is now increasingly recognized in the community at large as a necessary step for building on each other achievements. Such an open infrastructure can only be realized if the Language Resources community is cohesive enough to be able to focus on a number or priority targets and collectively work towards them, and, at the same time, whether it is powerful enough to permeate the user community, the industry, and the policy-makers. Why FLaReNet Creation of the necessary conditions for the development of such an infrastructure cannot rely on research activities only and even more cannot rely on the initiative of individual groups. Instead, strategic actions are crucial, such as making contacts with and involving all interested parties, sensitize the policy makers and institutional bodies, involve associations and consortia, disseminating widely the results of common efforts. Only by mobilizing this wide and heterogeneous panorama of actors can such an ambitious goal be attained. FLaReNet -Fostering Language Resources Network -is a Thematic Network funded by the European Commission under the eContentPlus framework (ECP-2007-LANG-617001) 1 . The FLaReNet Thematic Network was born with the specific aim -as required by the European Commission itself -to enhance European competitiveness in the field of Language Resources and Technologies, especially by consolidating a common vision and fostering a European strategy for the future. A major, longterm objective -as well as a powerful means for community creation -is creating the preparatory environment for making an open language infrastructure a reality. Objectives The objectives of FLaReNet are threefold: • The creation and mobilization of a unified and committed community in the field of Language Resources and Technologies; • The identification of a set of priority themes on which to stimulate action, under the form of a roadmap for Language Resources and Technologies; • The elaboration of a blueprint of priority areas for actions in the field and a coherent set of recommendations for the policy-makers (funding agencies especially), the business community and the public at large. Creation of a community FLaReNet has the challenging task of creating a network of people around the notion of Language Resources and Technologies. To this end, FLaReNet is bringing together leading experts of research institutions, academies, companies, funding agencies, with the specific purpose of creating consensus around short, medium and long-term strategic objectives. It is of foremost importance that the FLaReNet Network be composed of the as widest as possible representation of experiences, practices, research lines, industrial and political strategies; this in order to derive an overall picture of the field of Language Resources and Technologies that is not limited to the European scenario, but can also be globally inspired. The Network is currently composed of around 200 individuals belonging to academia, research institutes, industries and government. Such a community also needs to be constantly increased in a concentric way that starts from the core disciplines but gradually projects itself towards "neighboring" ones, such as cognitive science, semantic web, etc. Identification of priority themes Language technologies and language resources are the necessary ingredients for the development of applications that will help bridging language barriers in a global single information space, in a variety of means (the Web as well as communication devices) and for a variety of channels (spoken and written language alike). It is of utmost importance, however, to identify priorities as well as short, medium, and longterm strategic objectives in order to avoid scattered or conflicting efforts. The major players in the field of Language Resources and Technologies need to consensually work together and indicate a clear direction and priorities for the next years. Elaboration of a blueprint of actions However, whatever action cannot be implemented on a long term without the help of the necessary financial and political framework to sustain them. This is even most true for actions regarding Language Resources that typically imply a sustained effort at national level. To this end, the FLaReNet Network must propose the priority themes under the form of consensual recommendations and a plan of action for EC Member States, other Europeanwide decision makers, companies, as well as non-EU and International organizations. FLaReNet goals are very ambitious and its objectives are to be seen in a more global framework. Although they are shaped by the European landscape of the field of LR&T, its mission is therefore inherently cross-boundary: in order to attain such goals getting a global view is fundamental. To this end, it is important that FLaReNet is known by the Asian community, and it knows the Asian community. Some Asian community players are already members of the Network. How FLaReNet works Work in FLaReNet is inherently collaborative. Its means are the following: • Working groups Organization of workshops and meetings Meetings and events lie at the core of FLaReNet action plan and dissemination strategies. They can either be specifically oriented to the dissemination of results and recommendations (content-pushing events) or, rather, to their elicitation (content-pulling events). Three types of meetings are envisaged: • Annual Workshops, such as the "European Language Resources and Technologies Forum" held in Vienna, February 2009 • Thematic Workshops related to the work of Working Groups • Liaison meetings (e.g. those with NSF-SILT, CLARIN, ISO and other projects as the need may arise). Annual workshops are targeted to gather the broad FLaReNet community together. They are conceived as big events, and they aim at becoming major events in the Language Resources and Technology community of the kind able to attract a considerable audience. Given the success of the formula exploited for the FLaReNet "Vienna Event" 2 , it is likely that Annual workshops will be organized along the same lines. However, this type of event cannot be repeated on a frequent schedule. At the same time, more focused events centered on specific topics and with extensive time allocated for discussion are essential. To this end, Annual Workshops will be complemented by many Thematic workshops, i.e. more focused, dedicated meetings with a more restricted audience. These are directly linked to the work being carried out by the various Working Groups and are organized in a de-centralized manner, by direct initiative of the Working Group or Work package Leaders. In an attempt to increase FLaReNet sensitivity to hot issues, selection of topics and issues to be addressed will be also based on a bottom-up approach: FLaReNet members and subscribers are invited to submit topics of interest either freely or as a consequence of "Call for topics" related to particular events. Finally, liaison meetings are those elicited by FLaReNet to make contact and create synergies with national and international projects that are partially overlapping with FLaReNet in either their objectives or the target audience. Examples of these are the FLaReNet-CLARIN and the FLaReNet-SILT liaison meetings. External liaisons For a Network like FLaReNet, whose aim is the development of strategies and recommendations for the field of Language Resources and Technologies, coordination of actions at a worldwide level is of utmost importance. To this end, FLaReNet is planning to establish contacts and liaisons with national and international associations and consortia, such as LDC, ISO, ALTA, AFNLP, W3C, TEI, COCOSDA, Oriental-COCOSDA. Specific actions of this kind have started already, such as the International Cooperation Round Table that took place in Vienna. The members of the International Cooperation Round Table will form the initial nucleus of the FLaReNet International Advisory Board. First results and recommendations More than a hundred players worldwide gathered at the latest FLaReNet Vienna Forum, with the specific purpose of setting up a brainstorming force to make emerge the technological, market and policy challenges to be faced in a multilingual digital Europe. Over a two-day programme, the participants to the Forum had the opportunity to start assessing the current conditions of the LR&T field and to propose emerging directions of intervention. Some messages recurred repeatedly across the various sessions, as a sign both of a great convergence around these ideas and also of their relevance in the field. A clear set of priorities thus emerged for fostering the field of Language Resources and Language Technology. Language Resource Creation. The effort required to build all needed language resources and common tools should impose on all players a strong cooperation at the international level and the community should define how to enhance current coordination of language resource collection between all involved agencies and ensure efficiency (e.g. through interoperability). With data-driven methods dominating the current paradigms, language resource building, annotation, cataloguing, accessibility, availability is what the research community is calling for. Major institutional translation services, holding large volumes of useful data, seem to be ready to share their data and FLaReNet could possibly play a facilitating role. More efforts should be devoted to solve how to automate the production of the large quantity of resources demanded, and of enough quality to get acceptable results in industrial environments. Standards and Interoperability. In the long term, interoperability will be the cornerstone of a global network of language processing capabilities. The time and circumstances are ripe to take a broad and forward-looking view in order to establish and implement the standards and technologies necessary to ensure language resource interoperability in the future. This can only be achieved through a coordinated, community-wide effort that will ensure both comprehensive coverage and widespread acceptance. Coordination of Language Technology Evaluation. Looking at the way forward, it clearly appears that language technology evaluation needs coordination at international level: in order to ensure the link between technologies and applications, between evaluation campaigns and projects, in order to conduct evaluation campaigns (for ensuring synchrony or for addressing the influence of a component on a system on the same data), in order to produce language resources from language technology evaluation, or to port an already evaluated language technology to other languages (best practices, tools, metrics, protocols…), in order to avoid "reinventing the wheel", while being very cautious that there are language and cultural specificities which have to be taken into account (tone languages, oral languages with no writing system, etc). Availability of Resources, Tools and Information. Infrastructure building seems to be one of the main messages for FLaReNet. For a new worldwide language infrastructure the issue of access to Language Resources and Technologies is a critical one that should involve -and have impact on -all the community. There is the need to create the means to plug together different Language Resources & Language Technologies, in an internet-based resource and technology grid, with the possibility to easily create new workflows. Related to this is openness and availability of information. The related issues of access rights and IPR also call for cooperation. Join FLaReNet In order to constantly increase the community of people involved in FLaReNet, as well as to ensure their commitment to the objectives of the Network, a recruiting campaign is always open. People wishing to join the Network can do so by filling an appropriate web form available on the FLaReNet web site. The FLaReNet Network is open to participation by public and private, research and industrial organizations. Conclusions The field of Language Resources and Technologies needs a strong and coherent international cooperation policy to become more competitive and play a leading role globally. It is crucial to discuss future policies and priorities for the field of Language Resources and Technologies -as in the mission of FLaReNetnot only on the European scene, but also in a worldwide context. Cooperation is an issue that needs to be prepared. FLaReNet may become one of the privileged places where these -and future -initiatives get together to discuss and promote collaboration actions. Harmonization of formats and standards• Definition of evaluation and validation protocols and procedures • Methods for the automatic construction and processing of Language Resources.• Organization of workshops and meetings • External liaisons 4.1 Working Groups Working Groups are intended as "think-tanks" of experts (researchers and users) who jointly reflect on selected topics and come up with conclusions and recommendations. The Working Groups are clustered in thematic areas and carry out their activities through workshops, meetings, and via a collaborative Wiki platform. The FLaReNet Thematic Areas are: • The Chart for the area of Language Resources and Technologies in its different dimensions • Methods and models for Language Resource building, reuse, interlinking, maintenance, sharing, and distribution • http://www.flarenet.eu http://www.flarenet.eu/?q=Vienna09, see the Event Program to get an idea of the event structure.

Improved statistical measures to assess natural language parser performance across domains

We examine the performance of three dependency parsing systems, in particular, their performance variation across Wikipedia domains. We assess the performance variation of (i) Alpino, a deep grammar-based system coupled with a statistical disambiguation versus (ii) MST and Malt, two purely data-driven statistical dependency parsing systems. The question is how the performance of each parser correlates with simple statistical measures of the text (e.g. sentence length, unknown word rate, etc.). This would give us an idea of how sensitive the different systems are to domain shifts, i.e. which system is more in need for domain adaptation techniques. To this end, we extend the statistical measures used byZhang and Wang (2009)for English and evaluate the systems on several Wikipedia domains by focusing on a freer word-order language, Dutch. The results confirm the general findings of Zhang and Wang (2009), i.e. different parsing systems have different sensitivity against various statistical measure of the text, where the highest correlation to parsing accuracy was found for the measure we added, sentence perplexity.

Improved statistical measures to assess natural language parser performance across domains Barbara Plank b.plank@rug.nl Faculty of Arts Alfa informatica University of Groningen The Netherlands Improved statistical measures to assess natural language parser performance across domains We examine the performance of three dependency parsing systems, in particular, their performance variation across Wikipedia domains. We assess the performance variation of (i) Alpino, a deep grammar-based system coupled with a statistical disambiguation versus (ii) MST and Malt, two purely data-driven statistical dependency parsing systems. The question is how the performance of each parser correlates with simple statistical measures of the text (e.g. sentence length, unknown word rate, etc.). This would give us an idea of how sensitive the different systems are to domain shifts, i.e. which system is more in need for domain adaptation techniques. To this end, we extend the statistical measures used byZhang and Wang (2009)for English and evaluate the systems on several Wikipedia domains by focusing on a freer word-order language, Dutch. The results confirm the general findings of Zhang and Wang (2009), i.e. different parsing systems have different sensitivity against various statistical measure of the text, where the highest correlation to parsing accuracy was found for the measure we added, sentence perplexity. Introduction Natural Language Parsing has become an essential part for many Natural Language Processing task. For instance, Question Answering or Machine Translation. Yet, parsing system are very sensitive to the domain they were trained on, as their performance might drop dramatically when the system gets input from another text domain (Gildea, 2001). This is the problem of domain adaptation. Although the problem exists ever since the emerge of supervised Machine Learning, it has started to get attention only in recent years. Studies on supervised domain adaptation (where there are limited amounts of annotated resources in the new domain) have shown that straightforward baselines (e.g. models based on source only, target only, or the union of the data) achieve a relatively high performance level and are "surprisingly difficult to beat" (Daumé III, 2007). In contrast, semi-supervised adaptation (i.e. no annotated resources in the new domain) is a much more realistic situation but is clearly also considerably more difficult. Current studies on semi-supervised approaches show very mixed results. report on "frustrating" results on the CoNLL 2007 semi-supervised adaptation task for dependency parsing, i.e. "no team was able to improve target domain performance substantially over a state-of-theart baseline". On the other hand, there have been positive results as well. For instance, McClosky et al. (2006) improved a statistical consituency parser by self-training. Structural Correspondence Learning (Blitzer et al., 2006) was effective for PoS tagging and Sentiment Classification (Blitzer et al., 2006;, while modest gains were obtained for structured output tasks like Parsing. The question addressed in this study is: How does parser performance for Dutch correlate to simple statistical measures of the text? We assess the performance variation of two different kinds of parsing systems on various Wikipedia domains. This can be seen as a first step towards examining the question of how sensitive a parsing system is to the text domain, i.e. which parsing system (hand-crafted versus purely statistical) is more affected by domains shifts, and thus more in need for adaptation techniques. Zhang and Wang (2009) examined several state-of-theart parsing systems for English, and showed that parsing models correlate on different levels to the three statistical measures examined (average sentence length, unknown word ratio and unknown part-of-speech trigram ratio) when tested on the Brown corpus. We start here from their work and examine dependency parser performance for a freer word-order language, Dutch, by examining parsing performance on various Wikipedia subdomains. A related study is Ravi et al. (2008), who build a parser performance predictor system for English constituency parsing. Related Work Parsing Systems We examine two kinds of parsing systems for Dutch: a grammar-based system coupled with a statistical disambiguation system (Alpino) and two data-driven system (MST and Malt). Details about the parsers are given in the sequel. (1) Alpino (van Noord, 2006) is a deep-grammar based parser for Dutch that outputs dependency structure. The system consists of approximately 800 grammar rules in the tradition of HPSG, and a large hand-crafted lexicon, that together with a left-corner parser constitutes the generation component. For words that are not in the lexicon, the system applies a large variety of unknown word heuristics (van Noord, 2006), which among others attempt to deal with number-like expressions, compounds and proper names. The second stage of Alpino is a statistical disambiguation component based on Maximum Entropy. Thus, training the parser requires estimating parameters for the disambiguation component. (2) MST Parser (McDonald et al., 2005) is a languageindependent graph-based dependency parser. The system couples a minimum spanning tree search procedure with a separate second stage classifier to label the dependency edges. (3) Malt Parser (Nivre et al., 2007) is a languageindependent transition-based dependency parser. Malt parser uses SVMs to learn a classifier that predicts the next parsing action. Instances represent parser configurations and the label to predict determines the next parser action. Both data-driven parsers (MST and Malt) are thus not specific for the Dutch Language, however, they can be trained on a variety of languages given that the training corpus complies with the column-based format introduced in the 2006 CoNLL shared task (Buchholz and Marsi, 2006). Additionally, both parsers implement projective and nonprojective parsing algorithms, where the latter will be used in our experiments on the relatively free word-order language Dutch. Despite that, we train the data-driven parsers using their default settings (e.g. first order features for MST, SVM with polynomial kernel for Malt). Datasets and Treebank conversion We train the MST and Malt Parser as well as the disambiguation component of Alpino on cdb, the standard Alpino Treebank. For our cross-domain evaluation, we consider various Wikipedia articles from the Dutch Wikipedia project. Cdb The cdb (Alpino Treebank) consists of 7,136 sentences from the Eindhoven corpus (newspaper text). It is a collection of text fragments from 6 Dutch newspapers (het Nieuwsblad van het Noorden, de Telegraaf, de Tijd, Trouw, het Vrije Volk, Nieuwe Rotterdamse Courant). The collection has been annotated according to the guidelines of CGN (Oostdijk, 2000) and stored in XML format. It is the standard Treebank used to train the disambiguation component of the Alpino parser. Wikipedia We use 95 Dutch Wikipedia articles which were annotated in the course of the LASSY project. 1 They are mostly about Belgium issues, i.e. locations, politics, sports, arts, etc. We have grouped them into ten subdomains, as specified in Table 1, which also gives an overview of the size of these datasets. CoNLL2006 This is the test file for Dutch that has been used in the CoNLL 2006 shared task on multi-lingual dependency parsing. The file consists of 386 sentences from an institutional brochure (about 'Jeugdgezondheidszorg'/youth healthcare). We use this file to check our datadriven models against state-of-the-art performance. Alpino to CoNLL format In order to train the MST parser and evaluate it on the various Wikipedia articles, we needed to convert the Alpino Treebank format into the tabular CoNLL format. To this end, we adapted the treebank conversion software developed by Erwin Marsi for the CoNLL 2006 shared task on multi-lingual dependency parsing. Instead of using the PoS tagger and tagset used in the CoNLL shared task (to which we did not have access to), we replaced the PoS tags with more fine-grained tags obtained by parsing the data with the Alpino parser. 2 Features and Evaluation We follow Zhang and Wang (2009) and look at this stage at simple characteristics of the dataset without looking at syntactic annotation. We are interested how they correlate to parsing performance for the three parsing systems: Alpino, MST and Malt parser. We depart from their feature set (Zhang and Wang, 2009) and add a perplexity feature estimated from a trigram Language Model. Sentence Length (l) measures the average sentence length. Intuitively, longer sentences should be more difficult to parse than shorter ones. Simple Unknown Word Rate (sUWR) calculates how many words (tokens) in the dataset have not been observed before, i.e. are not in the cdb corpus. For the Alpino parser, we use the percentage of words that are not in the lexicon (aUWR, Alpino Unknown Word Rate). Unknown PoS Trigram Ration (UPTR) calculates the number of unknown PoS trigrams with respect to the original cdb training data. Perplexity (ppl) is the perplexity score assigned by a word-trigram language model estimated from the original cdb training data. This feature, also used by (Ravi et al., 2008), is intended as a refinement of the unknown word rate feature. Evaluation In contrast to Zhang and Wang (2009), we evaluate each parser with the same evaluation metric: Labeled Attachment Score (LAS). That is, performance is determined by the percentage of tokens with the correct dependency edge and label. To compute LAS, we use the CoNLL 2007 evaluation script 3 with punctuation tokens excluded from scoring (as was the default setting in CoNLL 2006). Note that the standard metric for Alpino would be a variant of LAS, which allows for a discrepancy between expected and returned dependencies. Such a discrepancy can occur, for instance, because the syntactic annotation of Alpino allows words to be dependent on more than a single head ('secondary edges') (van Noord, 2006). However, such edges are ignored in the CoNLL format; just a single head per token is allowed. Furthermore, there is another simplification. As the Dutch tagger used in the CoNLL 2006 shared task did not have the concept of multiwords, the organizers chose to treat them as a single token (Buchholz and Marsi, 2006). We here follow the CoNLL 2006 task setup. Experimental Results First of all, we performed a sanity check and trained the MST and Malt parser on the cdb corpus converted into the retagged CoNLL format, and tested on CoNLL 2006 test data (also retagged). As seen in table 2, the performance level corresponds to state-of-the-art performance for statistical parsing on Dutch, and is actually even higher. We believe this increase in performance can be attributed to two sources: (a) the more fine-grained PoS tagset obtained by parsing the data with the deep grammar; (b) improvements in the Alpino treebank itself over the course of the years. We now turn to the various statistical measures. The parsers were all evaluated on the 95 Wikipedia articles. Figure 1 plots the correlation between each parser's performance and the four measures: average sentence length (l), simple unknown word rate (sUWR, as well as aUWR for Alpino), unknown pos trigram rate (UPTR) and perplexity (ppl). The first row shows the Alpino parser, the second row shows the MST parser and the third row the Malt parser. Model Pre-result Three datasets immediately catch our eyes (the red crossed dots; cf. the graphs about sentence length or perplexity in Figure 1): these are three sports (SPO) articles about bike races. By inspecting them we see that they contain a long list of winners from the various race years (on average 86% of the articles constitute this 'winner list'). Thus, despite the average short sentence length (6.03 words per sentence; in contrast to an average sentence length over all Wikipedia articles of 13.68 words), the parsers exhibit very different performance levels on these datasets. Alpino, who includes various unknown word heuristics and a named entity tagger, is rather robust against the very high unknown word rate and reaches a very high accuracy level on these datasets. The Malt parser also reaches a high performance level on this special datasets. In contrast, the MST parser is more influenced by unknown words, and the performance on these articles drops actually to its lowest level. These three sports articles thus form 'outliers' and we exclude them from the remaining experiments. Results Figure 2 depicts parser performance against the four statistical measures of the text on the Wikipedia data with the three aforementioned sports articles removed. All parsers are robust to average sentence length (leftmost graphs in Figure 2). They basically do not show any correlation with this measure. This is in line with the results of Zhang and Wang (2009) for MST and Malt. It is different for the grammar-based parsing system. Their grammarbased parser (ERG) is highly sensitive to average sentence length (correlation coefficient of −0.61 on their datasets), as longer sentences "lead to a sharp drop in parsing coverage of ERG" (Zhang and Wang, 2009). This is not the case for the Alpino parser. The system suffers less from coverage problems and is thus not so sensitive against increasing sentence length. For Unknown Word Rate (UWR), the data-driven parsers show a high correlation with this measure (correlation of −0.39 and −0.28), which is in line with previous findings (Zhang and Wang, 2009). This is not the case for Alpino: again, its very good handling of unknown words make the system robust to UWR. Note that for Alpino the unknown word rate is measured in a slightly different way (i.e. words not in the lexicon). However, if we would apply the same simple unknown word rate (sUWR) measure to Alpino, it would also result in a weak negative correlation only (sU W R = −0.07). Thus, Alpino does not seem to be sensitive to this measure. No parser does show any correlation with the third measure, Unknown Part-of-Speech Trigram Rate (UPTR). This is contrary to previous results (Zhang and Wang, 2009), most probably due to the usage of a different tagset and the freer word-order language. Our last measure, sentence perplexity, exhibits the highest correlation to parsing performance: all parsers show the highest sensitivity against this measure, with the datadriven parsers being slightly more sensitive (cor = −0.67 and cor = −0.57) than the grammar-driven parser Alpino (cor = −0.33). Note that this still holds if we would remove two other possible 'outliers', the turquoise diamond and grey star on the right bottom of Figure 2 (right-most graphs), resulting in a correlation coefficient of: Alpino cor = −0.12, MST cor = −0.57 and Malt cor = −0.34. Moreover, also on another corpus (DPC, the Dutch Parallel Corpus 4 ) sentence perplexity gave us the highest correlation to parsing performance. Finally, because evaluation metrics are directly comparable, the figures show that the Alpino parser, tailored to the language, reaches an overall higher performance level (between 80 and 100% LAS) than the data-driven counterparts (between 50 and 95% LAS). Conclusion and Future work We evaluated a deep grammar-based system coupled with a statistical disambiguation system (Alpino) and two datadriven parsers (MST and Malt) for dependency parsing of Dutch. The empirical evaluation was performed on Wikipedia domains. By looking at four simple statistical measure of the text and their correlation to parsing performance, we could confirm the general result found by Zhang and Wang (2009): different parsing systems have different sensitivity against statistical measures of the text. While they evaluated parsing systems for English, we here looked at dependency parsing for a freer word-order language as Dutch. Both data-driven parsers show a high correlation to unknown word rate, while this is not the case for the grammarbased system. The highest correlation with parsing accuracy was found for the measure we added, sentence perplexity. This is true for both kinds of parsing systems, grammar-based and data-driven, but especially for the statistical parsers MST and Malt. This might first seem counterintuitive, as a grammar-based system usually suffers more from coverage problems. However, Alpino successfully implements a set of unknown word heuristics to achieve robustness. For instance, on the 'bike winners list' sports domain, which we could identify through these simple statistical measures, Alpino and MST indeed exhibit a very different performance level, showing that the grammar-based system suffered less from the peculiarities of that domain. In future, we would like to extend this line of work. The most immediate step is to integrate more statistical measures of the text and go towards building a 'parse performance predictor'. We might see that as a proxy for domain difference, i.e. to give us a rough estimate on how far or difficult a given text is for a parsing system. Figure 1 : 1Pre-results (on 95 Wikipedia articles): Parser performance on a per-article basis against statistical measure on the text, including correlation coefficient. Figure 2 : 2Results: Parser performance on a per-article basis against statistical measure on the text (93 Wikipedia articles -3 sports articles removed). 1 LASSY (Large Scale Syntactic Annotation of written Dutch), ongoing project. Corpus version 17905, obtained from http:// www.let.rug.nl/vannoord/Lassy/corpus/Domain Wikipedia articles (excerpt) # art. # sents # words BUS (business) Algemeen Belgisch Vakverbond 9 405 4440 COM (comics) Suske en Wiske 3 380 4000 HIS (history) Geschiedenis van België 3 468 8396 HOL (holidays) Feest van de Vlaamse Gemeenschap 4 43 524 KUN (arts) School van Tervuren 11 998 17073 LOC (location) België, Brussel (stad) 31 2190 25259 MUS (music) Sandra Kim, Urbanus (artiest) 3 89 1296 NOB (nobility) Albert II van België 6 277 4179 POL (politics) Belgische verkiezingen 2003 16 983 15107 SPO (sports) Spa-Francorchamps, Kim Clijsters 9 877 9713 Total 95 6710 89987 Table 1 : 1Overview Wikipedia corpus including number of articles (art.), sentences (sents) and words. Table 2 : 2Performance of the data-driven parsers versus state-of-the-art performance on the CoNLL 2006 test set. The datasets in retagged CoNLL format are available at http://www.let.rug.nl/bplank/alpino2conll. 3 http://nextens.uvt.nl/depparse-wiki/SoftwarePage http://www.kuleuven-kortrijk.be/dpc Domain adaptation with structural correspondence learning. John Blitzer, Ryan Mcdonald, Fernando Pereira, Conference on Empirical Methods in Natural Language Processing. Sydney, AustraliaJohn Blitzer, Ryan McDonald, and Fernando Pereira. 2006. Domain adaptation with structural correspondence learn- ing. In Conference on Empirical Methods in Natural Language Processing, Sydney, Australia. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. John Blitzer, Mark Dredze, Fernando Pereira, Association for Computational Linguistics. Prague, Czech RepublicJohn Blitzer, Mark Dredze, and Fernando Pereira. 2007. Biographies, bollywood, boom-boxes and blenders: Do- main adaptation for sentiment classification. In Associ- ation for Computational Linguistics, Prague, Czech Re- public. Conll-x shared task on multilingual dependency parsing. Sabine Buchholz, Erwin Marsi, Proc. of CoNLL. of CoNLLSabine Buchholz and Erwin Marsi. 2006. Conll-x shared task on multilingual dependency parsing. In In Proc. of CoNLL, pages 149-164. Frustratingly easy domain adaptation. Hal Daumé, Iii , Conference of the Association for Computational Linguistics (ACL). Prague, Czech RepublicHal Daumé III. 2007. Frustratingly easy domain adapta- tion. In Conference of the Association for Computational Linguistics (ACL), Prague, Czech Republic. Frustratingly hard domain adaptation for parsing. Mark Dredze, John Blitzer, Kuzman Pratha Pratim Talukdar, Joao Ganchev, Fernando Graca, Pereira, Proceedings of the CoNLL Shared Task Session -Conference on Natural Language Learning. the CoNLL Shared Task Session -Conference on Natural Language LearningPrague, Czech RepublicMark Dredze, John Blitzer, Pratha Pratim Talukdar, Kuz- man Ganchev, Joao Graca, and Fernando Pereira. 2007. Frustratingly hard domain adaptation for parsing. In Proceedings of the CoNLL Shared Task Session -Con- ference on Natural Language Learning, Prague, Czech Republic. Corpus variation and parser performance. Daniel Gildea, Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing (EMNLP). the 2001 Conference on Empirical Methods in Natural Language Processing (EMNLP)Daniel Gildea. 2001. Corpus variation and parser per- formance. In Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing (EMNLP). Effective self-training for parsing. David Mcclosky, Eugene Charniak, Mark Johnson, Proceedings of the Human Language Technology Conference of the NAACL, Main Conference. the Human Language Technology Conference of the NAACL, Main ConferenceNew York CityAssociation for Computational LinguisticsDavid McClosky, Eugene Charniak, and Mark Johnson. 2006. Effective self-training for parsing. In Proceedings of the Human Language Technology Conference of the NAACL, Main Conference, pages 152-159, New York City. Association for Computational Linguistics. Non-projective dependency parsing using spanning tree algorithms. Ryan Mcdonald, Fernando Pereira, Kiril Ribarov, Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing. Human Language Technology Conference and Conference on Empirical Methods in Natural Language ProcessingRyan McDonald, Fernando Pereira, Kiril Ribarov, and Jan Hajič. 2005. Non-projective dependency parsing us- ing spanning tree algorithms. In In Proceedings of Hu- man Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pages 523-530. Integrating graph-based and transition-based dependency parsers. Joakim Nivre, Ryan Mcdonald, Proceedings of ACL-08: HLT. ACL-08: HLTOhioAssociation for Computational LinguisticsJoakim Nivre and Ryan McDonald. 2008. Integrating graph-based and transition-based dependency parsers. In Proceedings of ACL-08: HLT, pages 950-958, Colum- bus, Ohio, June. Association for Computational Linguis- tics. Maltparser: A languageindependent system for data-driven dependency parsing. Joakim Nivre, Johan Hall, Jens Nilsson, Atanas Chanev, Gülsen Eryigit, Sandra Kübler, Svetoslav Marinov, Erwin Marsi, Natural Language Engineering. 13Joakim Nivre, Johan Hall, Jens Nilsson, Atanas Chanev, Gülsen Eryigit, Sandra Kübler, Svetoslav Marinov, and Erwin Marsi. 2007. Maltparser: A language- independent system for data-driven dependency parsing. Natural Language Engineering, 13:95-135. The Spoken Dutch Corpus: Overview and first evaluation. Nelleke Oostdijk, Proceedings of Second International Conference on Language Resources and Evaluation (LREC). Second International Conference on Language Resources and Evaluation (LREC)Nelleke Oostdijk. 2000. The Spoken Dutch Corpus: Overview and first evaluation. In Proceedings of Sec- ond International Conference on Language Resources and Evaluation (LREC), pages 887-894. Automatic prediction of parser accuracy. Sujith Ravi, Kevin Knight, Radu Soricut, EMNLP '08: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Morristown, NJ, USAAssociation for Computational LinguisticsSujith Ravi, Kevin Knight, and Radu Soricut. 2008. Au- tomatic prediction of parser accuracy. In EMNLP '08: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 887-896, Morris- town, NJ, USA. Association for Computational Linguis- tics. At Last Parsing Is Now Operational. Gertjan Van Noord, TALN 2006 Verbum Ex Machina, Actes De La 13e Conference sur Le Traitement Automatique des Langues naturelles. LeuvenGertjan van Noord. 2006. At Last Parsing Is Now Operational. In TALN 2006 Verbum Ex Machina, Actes De La 13e Conference sur Le Traitement Automatique des Langues naturelles, pages 20-42, Leuven. Correlating natural language parser performance with statistical measures of the text. Yi Zhang, Rui Wang, Proceedings of KI 2009. KI 2009Paderborn, GermanyYi Zhang and Rui Wang. 2009. Correlating natural lan- guage parser performance with statistical measures of the text. In In Proceedings of KI 2009, Paderborn, Germany.

The relative divergence of Dutch dialect pronunciations from their common source: an exploratory study

In this paper we use the Reeks Nederlandse Dialectatlassen as a source for the reconstruction of a 'proto-language' of Dutch dialects. We used 360 dialects from locations in the Netherlands, the northern part of Belgium and French-Flanders. The density of dialect locations is about the same everywhere. For each dialect we reconstructed 85 words. For the reconstruction of vowels we used knowledge of Dutch history, and for the reconstruction of consonants we used well-known tendencies found in most textbooks about historical linguistics. We validated results by comparing the reconstructed forms with pronunciations according to a proto-Germanic dictionary(Köbler, 2003). For 46% of the words we reconstructed the same vowel or the closest possible vowel when the vowel to be reconstructed was not found in the dialect material. For 52% of the words all consonants we reconstructed were the same. For 42% of the words, only one consonant was differently reconstructed. We measured the divergence of Dutch dialects from their 'proto-language'. We measured pronunciation distances to the protolanguage we reconstructed ourselves and correlated them with pronunciation distances we measured to proto-Germanic based on the dictionary. Pronunciation distances were measured using Levenshtein distance, a string edit distance measure. We found a relatively strong correlation (r=0.87).

The relative divergence of Dutch dialect pronunciations from their common source: an exploratory study Association for Computational LinguisticsCopyright Association for Computational LinguisticsJune 2007. 2007 Wilbert Heeringa w.j.heeringa@rug.nl Department of Humanities Computing Department of Linguistics University of Groningen Groningen The Netherlands Brian Joseph bjoseph@ling.ohio-state.edu The Ohio State University Columbus OhioUSA The relative divergence of Dutch dialect pronunciations from their common source: an exploratory study Proceedings of Ninth Meeting of the ACL Special Interest Group in Computational Morphology and Phonology Ninth Meeting of the ACL Special Interest Group in Computational Morphology and PhonologyPragueAssociation for Computational LinguisticsJune 2007. 2007 In this paper we use the Reeks Nederlandse Dialectatlassen as a source for the reconstruction of a 'proto-language' of Dutch dialects. We used 360 dialects from locations in the Netherlands, the northern part of Belgium and French-Flanders. The density of dialect locations is about the same everywhere. For each dialect we reconstructed 85 words. For the reconstruction of vowels we used knowledge of Dutch history, and for the reconstruction of consonants we used well-known tendencies found in most textbooks about historical linguistics. We validated results by comparing the reconstructed forms with pronunciations according to a proto-Germanic dictionary(Köbler, 2003). For 46% of the words we reconstructed the same vowel or the closest possible vowel when the vowel to be reconstructed was not found in the dialect material. For 52% of the words all consonants we reconstructed were the same. For 42% of the words, only one consonant was differently reconstructed. We measured the divergence of Dutch dialects from their 'proto-language'. We measured pronunciation distances to the protolanguage we reconstructed ourselves and correlated them with pronunciation distances we measured to proto-Germanic based on the dictionary. Pronunciation distances were measured using Levenshtein distance, a string edit distance measure. We found a relatively strong correlation (r=0.87). In this paper we use the Reeks Nederlandse Dialectatlassen as a source for the reconstruction of a 'proto-language' of Dutch dialects. We used 360 dialects from locations in the Netherlands, the northern part of Belgium and French-Flanders. The density of dialect locations is about the same everywhere. For each dialect we reconstructed 85 words. For the reconstruction of vowels we used knowledge of Dutch history, and for the reconstruction of consonants we used well-known tendencies found in most textbooks about historical linguistics. We validated results by comparing the reconstructed forms with pronunciations according to a proto-Germanic dictionary (Köbler, 2003). For 46% of the words we reconstructed the same vowel or the closest possible vowel when the vowel to be reconstructed was not found in the dialect material. For 52% of the words all consonants we reconstructed were the same. For 42% of the words, only one consonant was differently reconstructed. We measured the divergence of Dutch dialects from their 'proto-language'. We measured pronunciation distances to the protolanguage we reconstructed ourselves and correlated them with pronunciation distances we measured to proto-Germanic based on the dictionary. Pronunciation distances were measured using Levenshtein distance, a string edit distance measure. We found a relatively strong correlation (r=0.87). Introduction In Dutch dialectology the Reeks Nederlandse Dialectatlassen (RND), compiled by Blancquaert & Pée (1925-1982 is an invaluable data source. The atlases cover the Dutch language area. The Dutch area comprises The Netherlands, the northern part of Belgium (Flanders), a smaller northwestern part of France, and the German county of Bentheim. The RND contains 1956 varieties, which can be found in 16 volumes. For each dialect 139 sentences are translated and transcribed in phonetic script. Blancquaert mentions that the questionnaire used for this atlas was conceived of as a range of sentences with words that illustrate particular sounds. The design was such that, e.g., various changes of older Germanic vowels, diphthongs and consonants are represented in the questionnaire (Blancquaert 1948, p. 13). We exploit here the historical information in this atlas. The goals of this paper are twofold. First we aim to reconstruct a 'proto-language' on the basis of the RND dialect material and see how close we come to the protoforms found in Gerhard Köbler's neuhochdeutsch-germanisches Wörterbuch (Köbler, 2003). We recognize that we actually reconstruct a stage that would never have existed in prehistory. In practice, however, we are usually forced to use incomplete data, since data collections --such as the RND -are restricted by political boundaries, and often some varieties are lost. In this paper we show the usefulness of a data source like the RND. Second we want to measure the divergence of Dutch dialects compared to their proto-language. We measure the divergence of the dialect pronunciations. We do not measure the number of changes that happened in the course of time. For example if a [u] changed into a [y] and then the [y] changed into a [u], we simply compare the [u] to the proto-language pronunciation. However, we do compare Dutch dialects to both the proto-language we reconstruct ourselves, which we call Proto-Language Reconstructed (PLR), and to the Protolanguage according to the proto-Germanic Dictionary, which we call Proto-Germanic according to the Dictionary (PGD). Reconstructing the proto-language From the nearly 2000 varieties in the RND we selected 360 representative dialects from locations in the Dutch language area. The density of locations is about the same everywhere. In the RND, the same 141 sentences are translated and transcribed in phonetic script for each dialect. Since digitizing the phonetic texts is timeconsuming on the one hand, and since our procedure for measuring pronunciation distances is a word-based method on the other hand, we initially selected from the text only 125 words. Each set represents a set of potential cognates, inasmuch as they were taken from translations of the same sentence in each case. In Köbler's dictionary we found translations of 85 words only; therefore our analyses are based on those 85 words. We use the comparative method (CM) as the main tool for reconstructing a proto-form on the basis of the RND material. In the following subsections we discuss the reconstruction of vowels and consonants respectively. Vowels For the reconstruction of vowels we used knowledge about sound developments in the history of Dutch. In Old Dutch the diphthongs / / and / / turned into monophthongs / / and / / respectively (Quak & van der Horst 2002, p. 32). Van Bree (1996) mentions the tendencies that lead / / and / / to change into / / and / / respectively. From these data we find the following chains: To get evidence that the / / has raised to / / (and probably later to / /) in a particular word, we need evidence that the / / was part of the chain. Below we discuss another chain where the / / has lowered to / /, and where the / / is missing in the chain. To be sure that the / / was part of the chain, we consider the frequency of the / /, i.e. the number of dialects with / / in that particular word. The frequency of / / should be higher than the frequency of / / and/or higher than the frequency of / /. Similarly for the change from / / to / / we consider the frequency of / /. Another development mentioned by Van Bree is that high monophthongs diphthongize. In the transition from middle Dutch to modern Dutch, the monophthong / / changed into / /, and the monophthong / / changed into either / / or / / ( Van der Wal, 1994). According to Van Bree (1996, p. 99), diphthongs have the tendency to lower. This can be observed in Polder Dutch where / / and / / are lowered to / / and / / (Stroop 1998). We recognize the following chains: → → → → → →→ → → / → → → Different from the chains mentioned above, we do not find the / / and / / respectively in these chains. To get evidence for these chains, the frequency of / / should be lower than both the frequency of / / and / /, and the frequency of / / should be lower than both / / and / /. Sweet (1888, p. 20) observes that vowels have the tendency to move from back to front. Back vowels favour rounding, and front vowels unrounding. From this, we derive five chains: Sweet (1888, p. 22) writes that the dropping of unstressed vowels is generally preceded by various weakenings in the direction of a vowel close to schwa. In our data we found that the word mijn 'my' is sometimes [ ] and sometimes [ ]. A noncentral unstressed vowel might change into a central vowel which in turn might be dropped. In general we assume that deletion of vowels is more likely than insertion of vowels. ← ← ← ← ← ← ← ← ← ← ← Most words in our data have one syllable. For each word we made an inventory of the vowels used across the 360 varieties. We might recognize a chain in the data on the basis of vowels which appear at least two times in the data. For 37 words we could apply the tendencies mentioned above. In the other cases, we reconstruct the vowel by using the vowel found most frequently among the 360 varieties, working with Occam's Razor as a guiding principle. When both monophthongs and diphthongs are found among the data, we choose the most frequent monophthong. Sweet (1888, p. 21) writes that isolative diphthongizaton "mainly affects long vowels, evidently because of the difficulty of prolonging the same position without change." Consonants For the reconstruction of consonants we used ten tendencies which we discuss one by one below. Initial and medial voiceless obstruents become voiced when (preceded and) followed by a voiced sound. Hock & Joseph (1996) write that weakening (or lenition) "occurs most commonly in a medial voiced environment (just like Verner's law), but may be found in other contexts as well." In our data set zes 'six' is pronounced with a initial [ ] in most cases and with an initial [ ] in the dialects of Stiens and Dokkum. We reconstructed [ ]. 2 Final voiced obstruents of an utterance become voiceless. Sweet (1888, p. 18) writes that the natural isolative tendency is to change voice into unvoiced. He also writes that the "tendency to unvoicing is shown most strongly in the stops." Hock & Joseph (1996, p. 129) write that final devoicing "is not confined to utterance-final position but applies word-finally as well." 3 In our data set we found that for example the word-final consonant in op 'on' is sometimes a [p] and sometimes a [b]. Based on this tendency, we reconstruct the [b]. Plosives become fricatives between vowels, before vowels or sonorants (when initial), or after vowels (when final). Sweet writes that the "opening of stops generally seems to begin between vow-els…" (p. 23). Somewhat further he writes that in Dutch the g has everywhere become a fricative while in German the initial g remained a stop. For example goed 'good' is pronounced as [ ] in Frisian dialects, while other dialects have initial [ ] or [ ]. Following the tendency, we consider the [ ] to be the older sound. Related to this is the pronunciation of words like schip 'ship' and school 'school'. As initial consonants we found [sk], [sx] and [ ]. In cases like this we consider the [sk] as the original form, although the [k] is not found between vowels, but only before a vowel. Oral vowels become nasalized before nasals. Sweet (1888) writes that "nothing is more common than the nasalizing influence of a nasal on a preceding vowels" and that there "is a tendency to drop the following nasal consonant as superfluous" when "the nasality of a vowel is clearly developed" and "the nasal consonant is final, or stands before another consonant." (p. 38) For example gaan 'to go' is pronounced as [ ] in the dialect of Dok- ' Sweet (1888) writes that in "the change of p into f, w into v, we may always assume an intermediate [&], ['], the latter being the Middle German w" (p. 26), and that the "loss of back modification is shown in the frequent change of (w) into (v) through ['], as in Gm." Since vmeant as "voiced lip-to-teeth fricative" -is close to [(] -lip-to-teeth sonorant -we reconstruct [)] if both [)] and [(] are found in the dialect pronunciations. This happens for example in the word wijn 'wine'. The cluster ol+d/t diphthongizes to ou + d/t. For example English old and German alt have a /l/ be-fore the /d/ and /t/ respectively. In Old Dutch ol changed into ou (Van Loey 1967, p. 43, Van Bree 1987. Therefore we reconstruct the /l/ with preceding / / or / /. The proto-language according to the dictionary The dictionary of Köbler (2003) provides Germanic proto-forms. In our Dutch dialect data set we have transcriptions of 125 words per dialect. We found 85 words in the dictionary. Other words were missing, especially plural nouns, and verb forms other than infinitives are not included in this dictionary. For most words, many proto-Germanic forms are given. We used the forms in italics only since these are the main forms according to the author. If different lexical forms are given for the same word, we selected only variants of those lexical forms which appear in standard Dutch or in one of the Dutch dialects. The proto-forms are given in a semi-phonetic script. We converted them to phonetic script in order to make them as comparable as possible to the existing Dutch dialect transcriptions. This necessitated some interpretation. We made the following interpretation for monophthongs: Lehmann (2005Lehmann ( -2007 writes that in the early stage of Proto-Germanic "each of the obstruents had the same pronunciation in its various locations…". "Later, /b d g/ had fricative allophones when medial between vowels. Lehmann (1994) writes that in Gothic "/b, d, g/ has stop articulation initially, finally and when doubled, fricative articulation between vowels." We adopted this scheme, but were restricted by the RND consonant set. The fricative articulation of /,/ would be ['] or [ ]. We selected the [ ] since this sound is included in the RND set. The fricative articulation of /-/ would be [/], but this consonant is not in the RND set. We therefore used the [-] which we judge perceptually to be closer to the [/] than to the [ ]. The fricative articulation of / / is / / which was available in the RND set. p #* f * m "* b ,, * * * n , * t * s * ng * d -* z * w ) k !* h , $* r * g , * l .* j * We Several words end in a '-' in Köbler's dictionary, meaning that the final sounds are unknown or irrelevant to root and stem reconstructions. In our transcriptions, we simply note nothing. Measuring divergence of Dutch dialect pronunciations with respect to their proto-language Once a protolanguage is reconstructed, we are able to measure the divergence of the pronunciations of descendant varieties with respect to that protolanguage. For this purpose we use Levenshtein distance, which is explained in Section 4.1. In Sections 4.2 the Dutch dialects are compared to PLR and PGD respectively. In Section 4.3 we compare PLR with PGD. Levenshtein distance In 1995 Kessler introduced the Levenshtein distance as a tool for measuring linguistic distances between language varieties. The Levenshtein distance is a string edit distance measure, and Kessler applied this algorithm to the comparison of Irish dialects. Later the same technique was successfully applied to Dutch (Nerbonne et al. 1996;Heeringa 2004: 213-278). Below, we give a brief explanation of the methodology. For a more extensive explanation see Heeringa (2004: 121-135). Algorithm Using the Levenshtein distance, two varieties are compared by measuring the pronunciation of words in the first variety against the pronunciation of the same words in the second. We determine how one pronunciation might be transformed into the other by inserting, deleting or substituting sounds. Weights are assigned to these three operations. In the simplest form of the algorithm, all operations have the same cost, e.g., 1. Assume the Dutch word hart 'heart' is pronounced as [$ ] in the dialect of Vianen (The Netherlands) and as [+ ] in the dialect of Nazareth (Belgium). Changing one pronunciation into the other can be done as follows: * $ delete $ 1 subst. /+ 1 + insert 1 + *  3 In fact many string operations map [$ ] to [+ ]. The power of the Levenshtein algorithm is that it always finds the least costly mapping. To deal with syllabification in words, the Levenshtein algorithm is adapted so that only a vowel may match with a vowel, a consonant with a consonant, the [j] or [w] with a vowel (or opposite), the [i] or [u] with a consonant (or opposite), and a central vowel (in our research only the schwa) with a sonorant (or opposite). In this way unlikely matches (e.g. a [p] with an [a]) are prevented. 4 The longest alignment has the greatest number of matches. In our example we thus have the following alignment: $* * * * * * +* * * *  1 1 1 Operations weights The simplest versions of this method are based on a notion of phonetic distance in which phonetic overlap is binary: non-identical phones contribute to phonetic distance, identical ones do not. Thus the pair [ ,1] counts as different to the same degree as [ , ]. The version of the Levenshtein algorithm which we use in this paper is based on the comparison of spectrograms of the sounds. Since a spectrogram is the visual representation of the acoustical signal, the visual differences between the spectrograms are reflections of the acoustical differences. The spectrograms were made on the basis of recordings of the sounds of the International Phonetic Alphabet as pronounced by John Wells and Jill House on the cassette The Sounds of the International Phonetic Alphabet from 1995. 5 The different sounds were isolated from the recordings and monotonized at the mean pitch of each of the two speakers with the program PRAAT 6 (Boersma & Weenink, 2005). Next, for 4 Rather than matching a vowel with a consonant, the algorithm will consider one of them as an insertion and another as a deletion. 5 See http://www.phon.ucl.ac.uk/home/wells/cassette.htm. 6 The program PRAAT is a free public-domain program developed by Paul Boersma and David Weenink at each sound a spectrogram was made with PRAAT using the so-called Barkfilter, a perceptually oriented model. On the basis of the Barkfilter representation, segment distances were calculated. Inserted or deleted segments are compared to silence, and silence is represented as a spectrogram in which all intensities of all frequencies are equal to 0. We found that the [2] is closest to silence and the [ ] is most distant. This approach is described extensively in Heeringa (2004, pp. 79-119). In perception, small differences in pronunciation may play a relatively strong role in comparison to larger differences. Therefore we used logarithmic segment distances. The effect of using logarithmic distances is that small distances are weighted relatively more heavily than large distances. Processing RND data The RND transcribers use slightly different notations. In order to minimize the effect of these differences, we normalized the data for them. The consistency problems and the way we solved them are extensively discussed in Heeringa (2001) and Heeringa (2004). Here we mention one problem which is highly relevant in the context of this paper. In the RND the ee before r is transcribed as [ ] by some transcribers and as [ ] by other transcribers, although they mean the same pronunciation as appears from the introductions of the different atlas volumes. A similar problem is found for oo before r which is transcribed either as [ ] Especially suprasegmentals and diacritics might be used diffferently by the transcribers. We process the diacritics voiceless, voiced and nasal only. For details see Heeringa (2004, p. 110-111). The distance between a monophthong and a diphthong is calculated as the mean of the distance between the monophthong and the first element of the Institute of Pronunciation Sciences of the University of Amsterdam and is available at http://www.fon.hum.uva.nl/praat. the diphthong and the distance between the monophthong and the second element of the diphthong. The distance between two diphthongs is calculated as the mean of the distance between the first elements and the distance between the second elements. Details are given in Heeringa (2004, p. 108). Measuring divergence from the protolanguages The Levenshtein distance enables us to compare each of the 360 Dutch dialects to PLR and PGD. Since we reconstructed 85 words, the distance between a dialect and a proto-language is equal to the average of the distances of 85 word pairs. Figures 1 and 2 show the distances to PLR and PGD respectively. Dialects with a small distance are represented by a lighter color and those with a large distance by a darker color. In the map, dialects are represented by polygons, geographic dialect islands are represented by colored dots, and linguistic dialect islands are represented by diamonds. The darker a polygon, dot or diamond, the greater the distance to the proto-language. The two maps show similar patterns. The dialects in the Northwest (Friesland), the West (Noord-Holland, Zuid-Holland, Utrecht) and in the middle (Noord-Brabant) are relatively close to the proto-languages. More distant are dialects in the Northeast (Groningen, Drenthe, Overijssel), in the Southeast (Limburg), close to the middle part of the Flemish/Walloon border (Brabant) and in the southwest close to the Belgian/French state border (West-Vlaanderen). According to Weijnen (1966), the Frisian, Limburg and West-Flemish dialects are conservative. Our maps shows that Frisian is relatively close to proto-Germanic, but Limburg and West-Flemish are relatively distant. We therefore created two maps, one which shows distances to PGD based on vowel substitutions in stressed syllables only, and another showing distances to PGD on the basis of consonant substitutions only. 7 Looking at the map based on vowel substitutions we find the vowels of the Dutch province of Limburg and the eastern part of the province Noord-Brabant relatively close to PGD. Looking at the map based on consonant substitutions we find the consonants of the Limburg varieties distant to 7 The maps are not included in this paper. PGD. The Limburg dialects have shared in the High German Consonant Shift. Both the Belgium and Dutch Limburg dialects are found east of the Uerdinger Line between Dutch ik/ook/-lijk and High German ich/auch/-lich. The Dutch Limburg dialects are found east of the Panninger Line between Dutch sl/sm/sn/sp/st/zw and High German schl/schm/schn/schp/scht/schw (Weijnen 1966 8 Besides, Frisian is characterized by its falling diphthongs, which are an innovation as well. When we consulted the map based on consonant substitutions, we found the Frisian consonants close to PGD. For example the initial /g/ is still pronounced as a plosive as in most other Germanic varieties, but in Dutch dialects -and in standard Dutch -as a fricative. When we consider West-Flemish, we find the vowels closer to PGD than the consonants, but they are still relatively distant to PGD. PLR versus PGD When correlating the 360 dialect distances to PLR with the 360 dialect distances to PGD, we obtained a correlation of r=0.87 (p<0.0001) 9 . This is a significant, but not perfect correlation. Therefore we compared the word transcriptions of PLR with those of PGD. Figure 1. Distances of 360 Dutch dialects compared to PLR. Dialects are represented by polygons, geographic dialect islands are represented by colored dots, and linguistic dialect islands are represented by diamonds. Lighter polygons, dots or diamonds represent more conservative dialects and darker ones more innovative dialects. First we focus on the reconstruction of vowels. We find 28 words for which the reconstructed vowel of the stressed syllable was the same as in PGD 10 . In 15 cases, this was the result of applying the tendencies discussed in Section 2.1. In 13 cases this was the result of simply choosing the vowel found most frequently among the 360 word pronunciations. When we do not use tendencies, but simply always choose the most frequent vowel, we obtain a correlation which is significantly lower (r=0.74, p=0). We found 29 words for which vowel was reconstructed different from the one in PGD, although the PGD vowel was found among at least two dialects. For 28 words the vowel in the PGD form was not found among the 360 dialects, or only one time. For 11 of these words, the closest vowel found in the inventory of that word, was reconstructed. For example the vowel in ook 'too' is [ ] in PGD, while we reconstructed [ ]. Figure 2. Distances of 360 Dutch dialects compared to PGD. Dialects are represented by polygons, geographic dialect islands are represented by colored dots, and linguistic dialect islands are represented by diamonds. Lighter polygons, dots or diamonds represent more conservative dialects and darker ones more innovative dialects. Looking at the consonants, we found 44 words which have the same consonants as in PGD. 11 For 36 words only one consonant was different, where most words have at least two consonants. This shows that the reconstruction of consonants works much better than the reconstruction of vowels. Conclusions In this paper we tried to reconstruct a 'protolanguage' on the basis of the RND dialect material and see how close we come to the protoforms found in Köbler's proto-Germanic dictionary. We reconstructed the same vowel as in PGD or the closest possible vowel for 46% of the words. Therefore, the reconstruction of vowels still needs to be improved further. The reconstructions of consonants worked well. For 52% of the words all consonants reconstructed are the same as in PGD. For 42% of the words, only one consonant was differently reconstructed. And, as a second goal, we measured the divergence of Dutch dialects compared to their proto-language. We calculated dialect distances to PLR and PGD, and found a correlation of r=0.87 between the PLR distances and PGD distances. The high correlation shows the relative influence of wrongly reconstructed sounds. When we compared dialects to PLR and PGD, we found especially Frisian close to proto-Germanic. When we distinguished between vowels and consonants, it appeared that southeastern dialects (Dutch Limburg and the eastern part of Noord-Brabant) have vowels close to proto-Germanic. Frisian is relatively close to proto-Germanic because of its consonants. interpreted the h as [$] in initial position, and as [ ] in medial and final positions. An n before k, g or h is interpreted as [ ], and as [ ] in all other cases. The should actually be interpreted as [0], but this sound in not found in the RND set. Just as we use [-] for [/], analogously we use [ ] for [0]. We interpret double consonants are geminates, and transcribe them as single long consonants. For example nn becomes [ ]. An example is twee 'two' which has the vowel [ ] in 11% of the dialects, the [ ] in 14% of the dialects, the [ ] in 43% of the dialects and the [ ] in 20% of the dialects. 1 According to the neuhochdeutsch-germanisches Wörterbuch the [ ] or [ ] is the original sound. Our data show that simply reconstructing the most frequent sound, which is the [ ], would not give the original sound, but using the chain the original sound is easily found. kum, and as [ ] in the dialect of Stiens. The nasalized [ ] in the pronunciation of Stiens already indicates the deletion of a following nasal.Consonants become palatalized before front vowels. According to Campbell (2004) "palatalization often takes place before or after i and j or before other front vowels, depending on the language, although unconditioned palatalization can also take place." An example might be vuur which is pronouncedlike [ ] in Frisian varieties, while most other varieties have initial [ ] or [ ] followed by [ ] or [ ]. Superfluous sounds are dropped. Sweet (1888) introduced this principle as one of the principles of economy (p. 49). He especially mentioned that in [ ] the superfluous [ ] is often dropped (p. 42). In our data we found that krom 'curved' is pronounced [! "] in most cases, but as [! "#] in the dialect of Houthalen. In the reconstructed form we posit the final [#]. Medial [h] deletes between vowels, and initial [h] before vowels. The word hart 'heart' is sometimes pronounced with and sometimes without initial [$]. According to this principle we reconstruct the [$]. [r] changes to [ ]. According to Hock and Joseph (1996) the substitution of uvular [%] for trilled (post-)dental [ ] is an example of an occasional change apparently resulting from misperception. In the word rijp 'ripe' we find initial [ ] in most cases and [%] in the dialects of Echt and Baelen. We reconstructed [ ]. Syllable initial [w] changes in [ ]. Under 'Lip to Lip-teeth or [ ], and the eu before r which is transcribed as [ ] or [ ]. Since similar problems may occur in other contexts as well, the best solution to overcome all of these problems appeared to replace all [ ]'s by [ ]'s, all [ ]'s by [ ]'s, and all [ ]'s by [ ]'s, even though meaningful distinctions get lost. ). The Limburg dialects are also characterized by the uvular [%] while most Dutch dialects have the alveolar [ ]. All of this shows that Limburg consonants are innovative. The map based on vowel substitutions shows that Frisian vowels are not particularly close to PGD. Frisian is influenced by the Ingvaeonic sound shift. Among other changes, the [ ] changed into [ ], which in turn changed into [ ] in some cases (Dutch dun 'thin' is Frisian tin) (Van Bree 1987, p. 69). The sounds mentioned may be either monophthongs or diphthongs. In essence, in this and other such cases, a version of the manuscript-editing principle of choosing the "lectio difficilior" was our guiding principle.3 We do feel, however, that word-final devoicing, even though common cross-linguistically, is, as Hock 1976 emphasizes, not phonetically determined but rather reflects the generalization of utterance-final developments into word-final position, owing to the overlap between utterance-finality and word-finality. The Ingvaeonic sound shift affected mainly Frisian and English, and to a lesser degree Dutch. We mention here the phenomenon found in our data most frequently. 9 For finding the p-values we used with thanks:VassarStats: Website for Statistical Computation at: http://faculty.vassar.edu/lowry/VassarStats.html. For some words PGD gives multiple pronunciations. We count the number of words which has the same vowel in at least one of the PGD pronunciations. When PGD has multiple pronunciations, we count the number of words for which the consonants are the same as in at least one of the PGD pronunciations. AcknowledgementsWe thank Peter Kleiweg for letting us use the programs which he developed for the representation of the maps. We would like to thank Prof. Gerhard Köbler for the use of his neuhochdeutschgermanisches Wörterbuch and his explanation about this dictionary and Gary Taylor for his explanation about proto-Germanic pronunciation. We also thank the members of the Groningen Dialectometry group for useful comments on a earlier version of this paper. We are grateful to the anonymous reviewers for their valuable suggestions. This research was carried out within the framework of a talentgrant project, which is supported by a fellowship (number S 30-624) from the Netherlands Organisation of Scientific Research (NWO). Reeks Nederlandse Dialectatlassen. De Sikkel, Antwerpen. 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Brett Kessler, Proceedings of the 7th Conference of the European Chapter of the Association for Computational Linguistics. the 7th Conference of the European Chapter of the Association for Computational LinguisticsDublinEACLBrett Kessler. 1995. Computational dialectology in Irish Gaelic. Proceedings of the 7th Conference of the European Chapter of the Association for Computa- tional Linguistics, 60-67. EACL, Dublin. Neuhochdeutsch-germanisches Wörterbuch. Gerhard Köbler, Gerhard Köbler. 2003. Neuhochdeutsch-germanisches Wörterbuch. Available at: http:// www.koeblergerhard.de/germwbhinw.html. Ghotic and the Reconstruction of Proto-Germanic. Winfred P Lehmann, The Germanic Languages. Ekkehard König & Johan van der AuweraRoutledge, London & New YorkWinfred P. Lehmann. 1994. Ghotic and the Reconstruc- tion of Proto-Germanic. In: Ekkehard König & Johan van der Auwera, eds. The Germanic Languages, 19- 37. Routledge, London & New York. A Grammar of Proto-Germanic. Winfred P Lehmann, Jonathan SlocumWinfred P. Lehmann. 2005-2007. A Grammar of Proto- Germanic. Online books edited by Jonathan Slocum. Available at: http://www.utexas.edu/cola/centers/lrc/ books/pgmc00.html. Inleiding tot de historische klankleer van het Nederlands. C H Adolphe, Van Loey, N.V. W.J. Thieme & CieZutphenAdolphe C. H. van Loey. 1967. Inleiding tot de histori- sche klankleer van het Nederlands. N.V. W.J. Thie- me & Cie, Zutphen. Phonetic Distance between Dutch Dialects. CLIN VI, Papers from the sixth CLIN meeting. Gert Durieux & Walter Daelemans & Steven GillisAntwerpenUniversity of Antwerp, Center for Dutch Language and SpeechJohn Nerbonne & Wilbert Heeringa & Erik van den Hout & Peter van der Kooi & Simone Otten & Wil- lem van de Vis. 1996. Phonetic Distance between Dutch Dialects. In: Gert Durieux & Walter Daele- mans & Steven Gillis, eds. CLIN VI, Papers from the sixth CLIN meeting, 185-202. University of Antwerp, Center for Dutch Language and Speech, Antwerpen. Inleiding Oudnederlands. Arend Quak, &amp; Johannes Martinus Van Der, Horst , Leuven University PressLeuvenArend Quak & Johannes Martinus van der Horst. 2002. Inleiding Oudnederlands. Leuven University Press, Leuven. Poldernederlands; Waardoor het ABN verdwijnt. Jan Stroop, Bakker, AmsterdamJan Stroop. 1998. Poldernederlands; Waardoor het ABN verdwijnt, Bakker, Amsterdam. 1888. A History of English Sounds from the Earliest Period. Henry Sweet, Clarendon PressOxfordHenry Sweet. 1888. A History of English Sounds from the Earliest Period. Clarendon Press, Oxford. Marijke van der Wal together with Cor van Bree. Het Spectrum. Geschiedenis van het Nederlands. Aula-boeken. nd editionMarijke van der Wal together with Cor van Bree. 1994. Geschiedenis van het Nederlands. Aula-boeken. Het Spectrum, Utrecht, 2 nd edition. Nederlandse dialectkunde. Antonius A Weijnen, Studia Theodisca. Van Gorcum. nd editionAntonius A. Weijnen. 1966. Nederlandse dialectkunde. Studia Theodisca. Van Gorcum, Assen, 2 nd edition.

plWordNet 3.0 -Almost There

It took us nearly ten years to get from no wordnet for Polish to the largest wordnet ever built. We started small but quickly learned to dream big. Now we are about to release plWordNet 3.0-emo -complete with sentiment and emotions annotatedand a domestic version of Princeton Word-Net, larger than WordNet 3.1 by nearly ten thousand newly added words. The paper retraces the road we travelled and talks a little about the future.

plWordNet 3.0 -Almost There Maciej Piasecki amaciej.piasecki@pwr.wroc.pl A Stan Szpakowicz School of Electrical Engineering and Computer Science University of Ottawa OttawaOntarioCanada Marek Maziarz A Ewa Rudnicka ewa.rudnicka78@gmail.com A A G4 Department of Computational Intelligence Wrocław 19 Research Group University of Technology WrocławPoland plWordNet 3.0 -Almost There It took us nearly ten years to get from no wordnet for Polish to the largest wordnet ever built. We started small but quickly learned to dream big. Now we are about to release plWordNet 3.0-emo -complete with sentiment and emotions annotatedand a domestic version of Princeton Word-Net, larger than WordNet 3.1 by nearly ten thousand newly added words. The paper retraces the road we travelled and talks a little about the future. Wordnet makers' ambition A respectable wordnet ought to be a fair model of the lexical-semantic system of the language it represents; a nearly comprehensive model is a dream worth pursuing. A wordnet linked to other wordnets, and to world knowledge, is a dream come true. This paper tells the story of plWordNet, a resource for Polish built over a decade of concentrated effort. Our wordnet is well published, but we are reaching a really large milestone, so we want take a bird's eye view of that decade. We began cautiously. Our starting point in 2005 was a list of 10,000 most frequent lemmas in the IPI PAN Corpus of Polish, a mere quarter billion words from not quite balanced sources (Przepiórkowski, 2004). More than 30 personyears later, we are but a small step from completing the work on plWordNet 3.0-emo. With 177,003 lemmas,255,733 lexical units,193,286 synsets and more than 550,000 instances of relations, it is -in numbers -the largest wordnet created to date. Practically all its elements are in place, and the rollout is imminent. We think that it is an opportunity to present a synthetic picture of the whole endeavour. The paper first recalls the initial fundamental assumptions, which have held astonishingly well, even if they had, inevitably, to be adjusted as our wordnet grew. We discuss the central lessons learned, and present the structure and statistics of plWordNet 3.0-emo. Finally, there is an overview of applications, and plans for the future. Assumptions We based the development of plWordNet on several unique assumptions, formulated a priori. They have been discussed at length in previous publications, notably (Piasecki et al., 2009;Maziarz et al., 2013c), so we will only recapitulate them briefly just to ease into the further discussion. First and foremost, we believe that lexicosemantic systems of different languages differ in deep -and interesting -ways. That is why plWordNet, meant as a precise description of the Polish lexical system, had to be built in a way that avoided widespread influence of the material and structure of other wordnets. We were aware of the high cost of not simply translating Princeton WordNet, the only resource large enough for our ambition, but it felt most important to be faithful to the complex reality of our language. 1 When the project began, there were no publicdomain and no open-licence large electronic lexico-semantic resource for Polish. 2 We opted for a corpus-based wordnet development process. A very large corpus, the main knowledge source, is supplemented by a variety of linguistic substitution tests, mono-lingual dictionaries and other semantic language resources, encyclopaedias, discussions among linguists, and the wordnet editors' linguistic and lexicographic intuition. Corpus-based work, unaided by specialised software, would necessarily be rather slow. We assumed large-scale software support for semiautomatic wordnet construction, predicated on the availability of support tools for editing. Such tools were designed and built (and then perfected over the years) in parallel with fully manual construction of a small wordnet core to serve as the springboard for further expansion. This ensured much reduced workload for the editors and improved exploration of the corpus data. In many cases, the editor needs only to conform the support system's suggestions. 3 It soon became clear that there were significant problems with making the usual synset definition operational, and with the consistency of the editors' decisions. We chose a smaller-grain basic element for plWordNet: the lexical unit. 4 A synset was then defined indirectly as a set of lexical units which share a number of constitutive lexicosemantic relations and features (Maziarz et al., 2013c). Relations between synsets are a notational abbreviation for the shared relations between lexical units grouped into those synsets. Constitutive relations, which define the structure of the wordnet, are complemented by relations which only link lexical units. Three categories of constitutive features are lexical registers, semantic classes of verbs and adjectives, and aspect. In this model, synonymy is also a derived concept: constitutive relation-and feature-sharing lexical units grouped into a synset are understood to be synonymous. Finally, in the construction of plWordNet we tried to follow the principle of a minimal commitment, that is to say, to keep the number of assumptions small, to make plWordNet transparent to linguistics theories of meaning, and to shape it in a close relation to language data. 3 Lessons learned 3.1 Tools and organisation of work Ten years of continuous wordnet development gave us a lot of practical experience which confirmed the initial assumptions. 3 Software support has also greatly assisted in the mapping between plWordNet and Princeton WordNet. Likewise, a mapping to knowledge resources, notably to ontologies, had to be built semi-automatically from scratch. 4 A lexical unit is understood here as a triple: (lemma, part of speech, sense identifier). A lemma is the basic morphological form of a word. Each lexical unit represents a unique word sense. The building of plWordNet was what can be termed a corpus-based wordnet development process. It starts with the lemmatisation of a large corpus and the extraction of the lemma frequency ranking. A top sublist of new lemmas, those not yet included in plWordNet, is selected for the given iteration of wordnet expansion. Typically, 6000-9000 new lemmas selected for an iteration meant 3-6 months of work. Each iteration processed lemmas in the same part of speech. We tried to "sanitise" every list by removing obvious non-words (mostly proper names), but serious cleaning would double the workload: it requires searching corpora and identifying potential senses. Several tools examine the corpus to extract knowledge sources which help merge a new batch of lemmas with what is already in plWordNet: a Measure of Semantic Relatedness (MSR) and lists of lemma pairs potentially linked by hypernymy. The LexCSD system (Broda and Piasecki, 2011) extracts usage examples for the new lemmas. The extracted MSR was next used to cluster lemmas into semantically motivated groups we call packages, each package assigned to one editor. A package is clearly homogenous; usually, 2-3 domains are most prominent (lemmas were grouped by dominating senses), so the editor can stay focused. The acquired knowledge sources were input to the WordnetWeaver system (Piasecki et al., 2009) which, for each new lemma, automatically suggests the number and location in the network of lexical units. The suggestions are visually presented in the wordnet editing system Wordnet-Loom (Piasecki et al., 2010). The plWordNet team consists of rank-and-file editors and coordinators. 5 Before tackling lemmas in any of four parts of speech, we prepared guidelines with detailed relation definitions and substitution tests. A coordinator entered the definitions and tests into WordnetLoom, and trained the editors. The coordinator assigns lemmas to editors in batches, performs selective verification, answers questions, refines the guidelines, and monitors the pace and progress of the editors' work. For frequent lemmas, the editor uses supporting tools in a specified order of importance: Word-netWeaver suggestions; corpus browsers; usage examples generated automatically by LexCSD and the induced senses they represent; lists of highly related lemmas according to MSR; existing electronic dictionaries, lexicons, encyclopaedias; and, last but not least, the linguistic intuition of the editor and the team. The importance of Word-netWeaver and MSR dropped for lower-frequency lemmas. In the case of nouns editors tend to use dictionaries as the main source, but still remember the other sources. Adjectives and adverbs are much less richly described in the existing dictionaries, so LexCSD examples and corpus browsers became the primary tools. Before adding any relation instance to the wordnet, WordnetLoom presents the appropriate substitution test with the variable slots filled by the lexical units of the two synsets. The instantiated substitution test reminds about the constraints included in the relation definition, likely improving the consistency of the editors' definitions. Similarly, consistency increases with the use of the same supporting tools in the same order. The role of corpora Corpus-based development is surely slower and more costly than the merge method based on the previously existing lexical resources, but it is the only method which allows going beyond the existing dictionaries, often closely related. Corpusbased development also promotes a wordnet's better coverage of lemmas described and lexical units, assuming that the procedure recapped above is carefully followed. Obviously, a lot depends on the type of corpus. We aimed at building a comprehensive wordnet, so we tried to acquire or collect as large a corpus as possible. We made a practical assumption that the larger the corpus and the more diverse its text sources, the more balanced and representative the corpus becomes. The development of plWordNet 1.0 relied on the IPI PAN Corpus (IPIC) (Przepiórkowski, 2004), ca. 260 million tokens, the first publicly available large corpus of Polish. 6 IPIC represents a range of genres, biased towards parliamentary documents and scientific literature. That is why we put much effort into collecting corpora and texts, and combining them with IPIC. The work on plWordNet 2.1 built upon a 6 Oddly, it is even now the only freely available corpus of Polish. It is a pity that the newer and larger National Corpus of Polish (Przepiórkowski et al., 2012) is not all in the public domain (http://nkjp.pl/). plWordNet corpus of 1.8 billion tokens, recently expanded to almost 4 billion tokens. This merged corpus encompasses IPIC, the corpus of text from the newspaper Rzeczpospolita (Weiss, 2008) and Polish Wikipedia; it is complemented by texts collected from the Internet, filtered according to the percentage of unrecognised words by Morfeusz (Woliński, 2006), with duplicates removed with respect to the whole corpus. Finally, plWordNet 3.0 describes all lemmas with 30+ occurrences in 1.8 billion words, as well as a significant number of those less frequent. 7 At the final stage of work on plWordNet 3.0, we plan to add missing lemmas with the frequency 30+ from the 4-billion-token corpus. The underlying model The strategy of making the lexical unit the basic building block helped us formulate definitions of relations, and substitution tests for those relations, so they refer primarily to language data and the distribution of lemmas in use examples. We could also refer to the linguistic tradition in defining lexico-semantic relations better matching the background of our editors. We are convinced that the use of elaborate relation definitions, substitution tests and the procedure of lexicographic work have improved the mutual understanding of the plWordNet model among the members of the linguistic team, as well as the consistency of editing decisions across the pool of editors. The model of plWordNet, based on the sharing of constitutive relations and features, allowed us to write up and implement an operational definition of the synset. Still, specific leaves deep in the wordnet hypernymy tree often could not be easily separated into different synsets without referring to some notion of synonymy (or -more important in practice -to the absence of synonymy). We "pinned it down" as a combination of two parallel hyponymy relations. We think that the need for synonymy in wordnet editors' everyday work can be reduced in the future as the list of relations grows. That was what happened with verbs, adjectives and adverbs, for which we introduced, e.g., several cross-categorial constitutive relations. The progress of work We deliberately avoided putting non-lexical elements in plWordNet, a lexical resource par ex-cellence. For example, we only included proper names from which frequent lexical units are derived; other proper names are kept in a separate large lexicon mapped onto plWordNet. We have also developed an elaborate procedure for assessing the lexicality of multiword expressions. We made an exception for "artificial" (non-lexical) synsets first proposed for GermaNet (Hamp and Feldweg, 1997). They usually make a wordnet's hypernymy structure more readable for humans. The added artificial nodes also help editors maintain the hypernymy structure. Consequently, a significant number of artificial lexical units (language expressions) have been placed in singleton synsets. Such synsets and lexical units, clearly marked, can be removed or made transparent, if needed. They are not treated as part of the lexical system described by the wordnet. The WordnetWeaver system implements a complex frequency-based method of wordnet expansion 8 (Piasecki et al., 2013). The method worked fine in the first phase of plWordNet development, for frequent lemmas, mostly nouns. With the move to less frequent lemmas, the importance of WordnetWeaver waned. Its Measure of Semantic Relatedness (MSR), an essential knowledge source, proves useful for lemmas occurring 200+ times (an observed empirical rule); below 100 occurrences, it begins to produce many accidental associations. The thresholds are even higher for verbs, if the description of their occurrences is not based on the output of a reliable parser. While we abandoned WordnetWeaver for less frequent lemmas, several of its components remain in use. Most important, even if the MSR's quality decreases, it helps automated semantic clustering of lemmas in aid of assigning work to individual editors. Semantically motivated packages for this purpose, even if imperfect, handily beat such schemas as alphabetic order. Also, the LexCSD system automatically extracts use examples meant to represent various senses of a new lemma. LexCSD clusters all occurrences of the lemma, and tries first to identify occurrence groups representing different senses, and then to find the most prominent example in each group. Examples extracted by LexCSD are also presented in WordnetLoom. Such examples have become the first knowledge source which plWord-Net editors consult when they work on adjectives 8 automated, but subject to editors' final approval and adverbs. Existing Polish dictionaries neglect both categories, so we rely on corpus-derived examples. Lexico-syntactic patterns used for the extraction of lemma pairs potentially linked by a given relation also apply to less frequent words; the practice shows, however, that they are also less frequent in language expressions matching the patterns. Automated methods were very helpful in expanding derivational relations in plWordNet (Piasecki et al., 2012a;Piasecki et al., 2012b). Regardless of which automatic method was used, the results were always verified by human editors and revised if necessary. The manual mapping of plWordNet onto Princeton WordNet has incurred a high labour cost, even though we deliberately stayed away -for now -from the opposite direction (Rudnicka et al., 2012). We built an automated system to suggest inter-lingual links (Kędzia et al., 2013). Its precision is acceptable, but too low to let the results stand without intervention. We have also introduced several inter-lingual relations (Rudnicka et al., 2012) in order to cope with nontrivial differences between the two wordnets. All that investment was worth the price. The bilingual resource we now have is unique in scale (two largest wordnets, over 150,000 interlingual links between synsets) and nature (two wordnets based on slightly different models). The mapping opens many interesting paths for further exploration. Early on, we assumed tacitly that glosses were not part of the relational model of language which our wordnet represented. We still think that it is better first to invest in building a larger gloss-free wordnet than to construct a much smaller but more lexicographically complete resource. 9 A wordnet describes the meaning of a lexical unit via its network of lexico-semantic relations. Inevitably, though, as plWordNet gained popularity (through its Web page and mobile application), we soon noted that glosses help non-specialist users understand the meaning of wordnet entries. It is a technicality, perhaps, but glosses also help wordnet editors see clearly the editing decisions made by other members of the team: glosses serve as a form of control information. Similarly, use examples help, and appear more important for Natural Language Engineering applications of plWordNet. 4 The structure of plWordNet 3.0-emo Maziarz et al. (2013a) presented plWordNet 2.1. In most ways, plWordNet 3.0 is just better and larger, as planned two years ago (Maziarz et al., 2014). In comparison to version 2.1: • noun and adjective sub-databases have grown very substantially -see the statistics in Section 5; the verb, already a large list, have been only amended; • the set of adjective relations has been revised, while only minor changes were introduced for nouns and verbs; • a new adverb sub-database has been constructed from scratch with the help of a semiautomatic method based on exploring derivational relations and mapping between adjective relations and adverb relations; • an elaborate procedural definition of Multiword Lexical Units was designed (Maziarz et al., 2015), together with a work procedure supported by the semi-automatic system for collocation extraction and their further editing as potential candidates; • the plWordNet-to-WordNet mapping has been very significantly expanded to adjectives, with coverage vastly increased to 151,200 interlingual links of various types (38,471 I-synonymy links); • the constructed bilingual mapping was used to build a rule-based automated procedure of mapping plWordNet to SUMO (Pease, 2011;Kędzia and Piasecki, 2014). Mapping to WordNet To this planned development, we added two derived resources. While mapping onto Princeton WordNet, we observed that the most frequent inter-lingual relation is I-hyponymy (over twice more frequent than I-synonymy). That is to say, there were no counterparts in WordNet 3.1 for many specific lexical units in plWordNet. The cause: differences in coverage between both wordnets rather than any major differences in lexicalisation between Polish and English (Maziarz et al., 2013a), even though we dutifully checked English dictionaries and corpora for direct translations. Now, I-hyponymy is more vague -gives us less useful information for language processing -than I-synonymy. That is why we decided to add material to WordNet 3.1. The result is a resource we call enWordNet 0.1, included in the plWord-Net distribution as a large bilingual system. It has been built by adding to WordNet 3.1 about 8,000 new noun lemmas (9,000 noun lexical units). 10 We aimed to improve the mapping of plWord-Net (by adding to WordNet the missing corresponding entries), and then to replace I-hyponymy with I-synonymy as much as possible. This could be done simply by translating plWordNet synsets into English and putting the translations in en-WordNet, 11 but we resisted that temptation. We decided to let I-hyponymy guide expansion. The lemmas of all plWordNet 'leaf' synsets linked by I-hyponymy to WordNet synsets were automatically translated by a large cascade dictionary. The translations were then filtered by the existing WordNet lemmas and divided into three groups, lemmas for which the dictionary found: (i) equivalents whose lemmas were absent from WordNet; (ii) no equivalents; (iii) equivalents whose lemmas were already present in WordNet. Editors started with the first group, carefully verifying the suggestions with corpora, especially BNC (BNC, 2007) and ukWaC (Ferraresi et al., 2008), and all available resources. For the second group, they tried to find equivalents on their own (in all available resources). Finally, they investigated the third group, checking the existing mapping relations. Whenever editors started work with a particular WordNet 'nest', they were encouraged to look for its possible extensions on their own, not just limit themselves to the cascade dictionary suggestions. We began with nouns. That segment of Princeton WordNet figures in applications more often than other parts of speech. Also, our experience with developing plWordNet suggested that adding to the nouns in WordNet would be relatively easy. We used the same set of relations as in Princeton WordNet but, following the plWordNet practice, the relations have been specified by definitions and substitution tests in the WordnetLoom editing system. The editor team consisted of graduates of English philology and native speakers. In the first phase, we used bilingual dictionaries to select from the list those lemmas which appeared to be missing translation equivalents for plWordNet synsets lacking I-synonymy. Even so, the processing of the selected lemmas was in-dependent of their potential Polish counterparts. Only after new lexical units had been added to en-WordNet would the interlingual mapping be modified or expanded. For each English lemma, the editors identified its senses by searching for use examples in the corpora. We allowed into enWord-Net only lexical units with 5+ occurrences, supported by examples. In the second phase, we used the rest of the lemma list extracted from the corpora going through the lemmas of decreasing frequency. Sentiment and emotions Section 6 shows how plWordNet has become an important resource for language engineering applications in Polish. A notable exception were applications in sentiment analysis, despite their growing importance among research and commercial systems. That is why we decided to annotate manually a substantial part of plWordNet with sentiment polarity, basic emotions and fundamental values (Zaśko-Zielińska et al., 2015). The suffix "-emo" in the name of this plWordNet version signals the presence of this annotation. All in all, 19,625 noun lexical units and 11,573 adjective lexical units received two manual annotations. The team consisted of linguists and psychologists, whose coordinator was tasked with breaking ties. Each lexical unit was annotated with: • its sentiment polarity (positive, negative, ambiguous) and its intensity (strong, weak); • basic emotions associated with it: joy, trust, fear, surprise, sadness, disgust, anger, anticipation (Plutchik, 1980); • fundamental human values associated with it: użyteczność 'utility', dobro drugiego człowieka 'another's good', prawda 'truth', wiedza 'knowledge', piękno 'beauty', szczęście 'happiness' (all of them positive), nieużyteczność 'futility', krzywda 'harm', niewiedza 'ignorance', błąd 'error', brzydota 'ugliness', nieszczęście 'misfortune' (all negative) (Puzynina, 1992). The annotation of nouns encompassed those hypernymy sub-hierarchies which we expected to include lexical units with non-neutral sentiment polarity. Those were the sub-hierarchies for affect, feelings and emotions, nouns describing people, features of people and animals, artificial lexical unit events rated negatively, evaluated as negative and the sub-hierarchy of entertainment. The adjectival part of plWordNet was in major expansion during that time, so we only annotated the parts for which the expansion had been completed. It is worth emphasizing that the amount of manual annotation is several times higher than in other wordnets annotated with sentiment. This pilot study can be a good starting point for semiautomated annotation of the whole plWordNet. Statistics Wordnets are treated as basic lexical resources, so their sizes matter a lot for potential applications. See Table 1 for the general statistics in plWord-Net 3.0-beta-emo and a comparison with the other very large wordnets. We note that plWordNet has been consistently expanded in all parts of speech (PoS). The ratio between the size of plWordNet and Princeton WordNet is roughly the same for all PoS. The development of enWordNet has been intentionally concentrated on nouns. Moreover, plWordNet has become larger than all modern dictionaries of general Polish in terms of the entries included: 130k (Zgółkowa, 1994(Zgółkowa, 2005, 125k [180k lexical units] (Doroszewski, 1963(Doroszewski, 1969, 100k [150k lexical units] (Dubisz, 2004), 45k [100k lexical units] (Bańko, 2000. One of the main reasons is that those dictionaries do not contain many specialised words and senses from science, technology, culture and so on. Such material, however, is appropriate for a wordnet due to its applications in processing of texts of many genres coming from different sources, including the Internet. We could also observe that lemma lists added to plWordNet (based on the corpus) included quite a few words that are now frequent, but not described in those dictionaries. The largest ever Polish dictionary, from the early 1900s, has 280k entries (Karłowicz et al., 1900(Karłowicz et al., 1927Piotrowski, 2003, p. 604) and is still much larger than plWordNet, but it contains many archaic words, perhaps useful in the processing of texts from specialised domains. The achieved size of plWordNet has already exceeded the target size estimated for it considering a corpus of 1.8 billion words (Maziarz et al., 2014). Lexico-semantic relations are the primary means of description of lexical meanings represented in a wordnet by synsets. The average number of relation links per synset, which is called relation density, tells us about the average amount of information provided by the wordnet for a single lexical meaning. Table 2 compares the relation density in Princeton WordNet and plWordNet for different parts of speech (obligatory inverse relations have been excluded from the count). 12 The relation density is higher in plWordNet for all parts of speech. We can name two reasons for this difference: smaller synsets in plWordNet on average, see Table 1, and the assumed way of defining synsets by the constitutive relations -more relations are needed to distinguish different synsets (i.e., lexical meanings). However, plWordNet has a rich set of relations (more than 40 main types and 90 sub-types). Some of them have originated from the derivational relations. That can also increase the relation density. If a wordnet is treated as a reference source, we expect to find in it most of the lemmas from the processed text. The complete coverage is not possible, but the higher it is, the more information a wordnet provides for the analysed text. Table 3 compares the coverage of Princeton Word-Net and plWordNet for two corpora of a comparable size. From both corpora, two lemma fre- 12 The relation structures differ among the parts of speech, so we do not show relation density for the whole wordnets. quency lists were extracted. Both corpora were first morphosyntactically tagged and only lemmas of the parts of speech described in wordnets were taken into account. For Polish, we worked with the plWordNet corpus (version 7) of ≈1.8 billion words from several available corpora (see section 3.2), supplemented by texts collected from the Internet. As an English corpus, we took texts from the English Wikipedia, ≈1.2 billion words, a size similar to that of the plWordNet corpus. 13 The coverage is much higher for plWordNet, but the corpora differ. Many more specialised and rare words appear in English Wikipedia than in the Polish corpus. Even so, the statistics bode well for plWordNet's potential in applications. The coverage for the most frequent words (≥ 1000) is not 100% because the list includes many proper names and misspelled words recognised by the tagger as common words. In comparison with plWordNet 2.1 (Maziarz et al., 2013b), the coverage of less frequent words increased significantly, because the development of plWordNet moves towards the bottom of the frequency ranking list. The average polysemy -the ratio of lexical units to lemmas -is higher in plWordNet than in WordNet both for nouns (1.32 vs 1.24) and adjectives (1.71 vs 1.38). The difference is lower than in plWordNet 2.1: we added more specific monosemous lemmas as a result of the focus given to lexical units and the tendency to describe exhaustively all existing lexical units for a given lemma. For verbs we have 1.83 vs 2.17, maybe because of aspect and rich derivation in Polish verbs. The comparison of hypernymy path lengths did not change much from plWordNet 2.1 (Maziarz et al., 2013a). WordNet's much longer paths are caused by the elaborate topmost part of its hypernymy hierarchy; plWordNet has ≈100 linguistically motivated hypernymy roots. 14 Applications Wordnet-building costs a lot of public money, so as a rule the effect should be free for the public use. This good rule, grounded in Princeton Word-Net's practice, is central for languages other than English, still less resourced. The availability of plWordNet on the WordNet-style open licence has stimulated, over the years, many interesting applications in linguistic research, language resources and tools, scientific applications, commercial applications and education. The plWordNet Web page and Web service have had tens of thousands of visitors, and hundreds of thousands of searches. There are over 100 citations and over 700 users, individual and institutional, who optionally registered when downloading the plWordNet source files. Most of the registered users described the intended use of plWord-Net, and a rich tapestry it is. The limited space only allows us to single out a handful in citations. First of all, plWordNet has been applied in linguistic research: valency frame description and automated verb classification; verb analysis for semantic annotation in a corpus of referential gestures; contrastive/comparative studies, etc. Increasingly often, plWordNet is treated as a large monolingual and bilingual dictionary, e.g., in text verification during editing or as a source of meta-data for publications. Miłkowski (2010) included plWordNet among the dictionaries in a proofreading tool and as a knowledge source for an open Polish-English dictionary, which many translators and translation companies say they use. Open Multilingual Wordnet (Bond, 2013) now includes plWordNet. It is referred to in several other projects on wordnets and semantic lexicons 14 They do not have hypernyms according to the definitions assumed in plWordNet. (Pedersen et al., 2009;Lindén and Carlson, 2010;Borin and Forsberg, 2010;Mititelu, 2012;Zafar et al., 2012;Šojat et al., 2012). Practical machine translation systems use plWordNet. We are aware of applications in measuring translation quality and building the MT component embedded in an application supporting English teaching to children. There are more research and commercial projects, both under way and announced by plWordNet users. They include ontology building and linking, information retrieval, question answering, text mining, semantic analysis, terminology extraction, word sense disambiguation (WSD), text classification, sentiment analysis and opinion mining, automatic text summarisation, speech recognition, or even the practice of aphasia treatment. The lexicographer's work is never done When in 2012 we established the target size of plWordNet 3.0, we were convinced that we would go to limits of the Polish lexical system. We now see that -even if major paths have been exploredwe are discovering numerous smaller paths going deeper into the system. The Polish side of plWordNet could have more relation links per synset. The constitutive relations do not differentiate all hypernymy leaves yet. There are cross-categorial relations, more numerous than in many other wordnets, but still not enough for WSD or semantic analysis. The connection to the valency lexicon could be tighter. The description of verb derivation (as highly productive in Polish as in other Slavic languages) needs much more work, and so do some relations, e.g., meronymy. More information useful for WSD could be introduced, e.g., further glosses or links to external sources like Wikipedia. Finally, for applications in translation (manual and machine-based) we must not only complete the mapping to WordNet, but also go inside synsets, i.e., map lexical units. We are fortunate to have so much more intriguing work to do. Table 1 : 1The count by part of speech (PoS) of Noun/Verb/Adjective synsets, lemmas and lexical units (LUs), and average synset size (avs), in PWN 3.1 (PWN), enWordNet 0.1 (enWN), plWord- Net 3.0 (plWN) and GermaNet 10.0 (www.sfs.uni- tuebingen.de/GermaNet/). * This part of WordNet remains to be extended. Table 2 : 2Synset relation density in Princeton WordNet 3.1 and in plWordNet 2.0 by part of speech. FRC ≥1000 ≥500 ≥200 ≥100 ≥50 PWN 0.383 0.280 0.170 0.107 0.064 plWN 0.732 0.644 0.515 0.416 0.327 Table 3 : 3Percentage of Princeton WordNet noun lemmas in Wikipedia.en and plWordNet (plWN) lemmas in the plWordNet corpus. FRC is lemma frequency in the reference corpus. In retrospect, this decision has been borne out by the scale of differences between plWordNet and WordNet when we got deep enough into the mapping between the two. 2 There are scarcely any such resources even now(Vetulani et al., 2009;Miłkowski, 2007), unless one counts plWordNet . At the height of plWordNet development, several coordinators supervised a small group of editors each. Separate teams work on plWordNet-to-WordNet mapping, and on sentiment annotation. All this allows cross-checking: the teams exchange information about likely errors. Editors were free to add any existing lemma, after checking corpora(Przepiórkowski et al., 2012) and the Internet. Come to think of it, glosses in Princeton WordNet were an afterthought, too. The estimated target size is 10,000 new nouns. 11 That would mean applying the transfer method in an "unorthodox" direction. One normally translates English synsets into whatever language one is building a wordnet for. 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