pwc-archive/papers-with-abstracts huggingface dataset:
Gulf Arabic Linguistic Resource Building for Sentiment Analysis
Authors: Wafia Adouane, Richard Johansson
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Arabic Sentiment Analysis, Sentiment Analysis

This paper deals with building linguistic resources for Gulf Arabic, one of the Arabic variations, for sentiment analysis task using machine learning. To our knowledge, no previous works were done for Gulf Arabic sentiment analysis despite the fact that it is present in different online platforms. Hence, the first challenge is the absence of annotated data and sentiment lexicons. To fill this gap, we created these two main linguistic resources. Then we conducted different experiments: use Naive Bayes classifier without any lexicon; add a sentiment lexicon designed basically for MSA; use only the compiled Gulf Arabic sentiment lexicon and finally use both MSA and Gulf Arabic sentiment lexicons. The Gulf Arabic lexicon gives a good improvement of the classifier accuracy (90.54 {\%}) over a baseline that does not use the lexicon (82.81{\%}), while the MSA lexicon causes the accuracy to drop to (76.83{\%}). Moreover, mixing MSA and Gulf Arabic lexicons causes the accuracy to drop to (84.94{\%}) compared to using only Gulf Arabic lexicon. This indicates that it is useless to use MSA resources to deal with Gulf Arabic due to the considerable differences and conflicting structures between these two languages.
Using Data Mining Techniques for Sentiment Shifter Identification
Authors: Samira Noferesti, Mehrnoush Shamsfard
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: General Classification, Opinion Mining

Sentiment shifters, i.e., words and expressions that can affect text polarity, play an important role in opinion mining. However, the limited ability of current automated opinion mining systems to handle shifters represents a major challenge. The majority of existing approaches rely on a manual list of shifters; few attempts have been made to automatically identify shifters in text. Most of them just focus on negating shifters. This paper presents a novel and efficient semi-automatic method for identifying sentiment shifters in drug reviews, aiming at improving the overall accuracy of opinion mining systems. To this end, we use weighted association rule mining (WARM), a well-known data mining technique, for finding frequent dependency patterns representing sentiment shifters from a domain-specific corpus. These patterns that include different kinds of shifter words such as shifter verbs and quantifiers are able to handle both local and long-distance shifters. We also combine these patterns with a lexicon-based approach for the polarity classification task. Experiments on drug reviews demonstrate that extracted shifters can improve the precision of the lexicon-based approach for polarity classification 9.25 percent.
Discourse Structure and Dialogue Acts in Multiparty Dialogue: the STAC Corpus
Authors: Nicholas Asher, Julie Hunter, Mathieu Morey, Benamara Farah, Stergos Afantenos
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper describes the STAC resource, a corpus of multi-party chats annotated for discourse structure in the style of SDRT (Asher and Lascarides, 2003; Lascarides and Asher, 2009). The main goal of the STAC project is to study the discourse structure of multi-party dialogues in order to understand the linguistic strategies adopted by interlocutors to achieve their conversational goals, especially when these goals are opposed. The STAC corpus is not only a rich source of data on strategic conversation, but also the first corpus that we are aware of that provides full discourse structures for multi-party dialogues. It has other remarkable features that make it an interesting resource for other topics: interleaved threads, creative language, and interactions between linguistic and extra-linguistic contexts.
Purely Corpus-based Automatic Conversation Authoring
Authors: Guillaume Dubuisson Duplessis, Vincent Letard, Anne-Laure Ligozat, Sophie Rosset
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents an automatic corpus-based process to author an open-domain conversational strategy usable both in chatterbot systems and as a fallback strategy for out-of-domain human utterances. Our approach is implemented on a corpus of television drama subtitles. This system is used as a chatterbot system to collect a corpus of 41 open-domain textual dialogues with 27 human participants. The general capabilities of the system are studied through objective measures and subjective self-reports in terms of understandability, repetition and coherence of the system responses selected in reaction to human utterances. Subjective evaluations of the collected dialogues are presented with respect to amusement, engagement and enjoyability. The main factors influencing those dimensions in our chatterbot experiment are discussed.
Dialogue System Characterisation by Back-channelling Patterns Extracted from Dialogue Corpus
Authors: Masashi Inoue, Hiroshi Ueno
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In this study, we describe the use of back-channelling patterns extracted from a dialogue corpus as a mean to characterising text-based dialogue systems. Our goal was to provide system users with the feeling that they are interacting with distinct individuals rather than artificially created characters. An analysis of the corpus revealed that substantial difference exists among speakers regarding the usage patterns of back-channelling. The patterns consist of back-channelling frequency, types, and expressions. They were used for system characterisation. Implemented system characters were tested by asking users of the dialogue system to identify the source speakers in the corpus. Experimental results suggest that possibility of using back-channelling patterns alone to characterize the dialogue system in some cases even among the same age and gender groups.
Towards Automatic Identification of Effective Clues for Team Word-Guessing Games
Authors: Eli Pincus, David Traum
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Team word-guessing games where one player, the clue-giver, gives clues attempting to elicit a target-word from another player, the receiver, are a popular form of entertainment and also used for educational purposes. Creating an engaging computational agent capable of emulating a talented human clue-giver in a timed word-guessing game depends on the ability to provide effective clues (clues able to elicit a correct guess from a human receiver). There are many available web resources and databases that can be mined for the raw material for clues for target-words; however, a large number of those clues are unlikely to be able to elicit a correct guess from a human guesser. In this paper, we propose a method for automatically filtering a clue corpus for effective clues for an arbitrary target-word from a larger set of potential clues, using machine learning on a set of features of the clues, including point-wise mutual information between a clue{'}s constituent words and a clue{'}s target-word. The results of the experiments significantly improve the average clue quality over previous approaches, and bring quality rates in-line with measures of human clue quality derived from a corpus of human-human interactions. The paper also introduces the data used to develop this method; audio recordings of people making guesses after having heard the clues being spoken by a synthesized voice.
Using Contextual Information for Machine Translation Evaluation
Authors: Marina Fomicheva, N{\'u}ria Bel
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation, Sentence, Translation

Automatic evaluation of Machine Translation (MT) is typically approached by measuring similarity between the candidate MT and a human reference translation. An important limitation of existing evaluation systems is that they are unable to distinguish candidate-reference differences that arise due to acceptable linguistic variation from the differences induced by MT errors. In this paper we present a new metric, UPF-Cobalt, that addresses this issue by taking into consideration the syntactic contexts of candidate and reference words. The metric applies a penalty when the words are similar but the contexts in which they occur are not equivalent. In this way, Machine Translations (MTs) that are different from the human translation but still essentially correct are distinguished from those that share high number of words with the reference but alter the meaning of the sentence due to translation errors. The results show that the method proposed is indeed beneficial for automatic MT evaluation. We report experiments based on two different evaluation tasks with various types of manual quality assessment. The metric significantly outperforms state-of-the-art evaluation systems in varying evaluation settings.
Bootstrapping a Hybrid MT System to a New Language Pair
Authors: Jo{\~a}o Ant{\'o}nio Rodrigues, Nuno Rendeiro, Andreia Querido, Sanja {\v{S}}tajner, Ant{\'o}nio Branco
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation, Translation

The usual concern when opting for a rule-based or a hybrid machine translation (MT) system is how much effort is required to adapt the system to a different language pair or a new domain. In this paper, we describe a way of adapting an existing hybrid MT system to a new language pair, and show that such a system can outperform a standard phrase-based statistical machine translation system with an average of 10 persons/month of work. This is specifically important in the case of domain-specific MT for which there is not enough parallel data for training a statistical machine translation system.
Filtering Wiktionary Triangles by Linear Mbetween Distributed Word Models
Authors: M{\'a}rton Makrai
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Translation

Word translations arise in dictionary-like organization as well as via machine learning from corpora. The former is exemplified by Wiktionary, a crowd-sourced dictionary with editions in many languages. {\'A}cs et al. (2013) obtain word translations from Wiktionary with the pivot-based method, also called triangulation, that infers word translations in a pair of languages based on translations to other, typically better resourced ones called pivots. Triangulation may introduce noise if words in the pivot are polysemous. The reliability of each triangulated translation is basically estimated by the number of pivot languages (Tanaka et al 1994). Mikolov et al (2013) introduce a method for generating or scoring word translations. Translation is formalized as a linear mapping between distributed vector space models (VSM) of the two languages. VSMs are trained on monolingual data, while the mapping is learned in a supervised fashion, using a seed dictionary of some thousand word pairs. The mapping can be used to associate existing translations with a real-valued similarity score. This paper exploits human labor in Wiktionary combined with distributional information in VSMs. We train VSMs on gigaword corpora, and the linear translation mapping on direct (non-triangulated) Wiktionary pairs. This mapping is used to filter triangulated translations based on scores. The motivation is that scores by the mapping may be a smoother measure of merit than considering only the number of pivot for the triangle. We evaluate the scores against dictionaries extracted from parallel corpora (Tiedemann 2012). We show that linear translation really provides a more reliable method for triangle scoring than pivot count. The methods we use are language-independent, and the training data is easy to obtain for many languages. We chose the German-Hungarian pair for evaluation, in which the filtered triangles resulting from our experiments are the greatest freely available list of word translations we are aware of.
Translation Errors and Incomprehensibility: a Case Study using Machine-Translated Second Language Proficiency Tests
Authors: Takuya Matsuzaki, Akira Fujita, Naoya Todo, Noriko H. Arai
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation, Translation

This paper reports on an experiment where 795 human participants answered to the questions taken from second language proficiency tests that were translated to their native language. The output of three machine translation systems and two different human translations were used as the test material. We classified the translation errors in the questions according to an error taxonomy and analyzed the participants{'} response on the basis of the type and frequency of the translation errors. Through the analysis, we identified several types of errors that deteriorated most the accuracy of the participants{'} answers, their confidence on the answers, and their overall evaluation of the translation quality.
Word Sense-Aware Machine Translation: Including Senses as Contextual Features for Improved Translation Models
Authors: Steven Neale, Lu{\'\i}s Gomes, Eneko Agirre, Oier Lopez de Lacalle, Ant{\'o}nio Branco
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation, Translation, Word Sense Disambiguation

Although it is commonly assumed that word sense disambiguation (WSD) should help to improve lexical choice and improve the quality of machine translation systems, how to successfully integrate word senses into such systems remains an unanswered question. Some successful approaches have involved reformulating either WSD or the word senses it produces, but work on using traditional word senses to improve machine translation have met with limited success. In this paper, we build upon previous work that experimented on including word senses as contextual features in maxent-based translation models. Training on a large, open-domain corpus (Europarl), we demonstrate that this aproach yields significant improvements in machine translation from English to Portuguese.
SuperCAT: The (New and Improved) Corpus Analysis Toolkit
Authors: K. Bretonnel Cohen, William A. Baumgartner Jr., Irina Temnikova
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper reports SuperCAT, a corpus analysis toolkit. It is a radical extension of SubCAT, the Sublanguage Corpus Analysis Toolkit, from sublanguage analysis to corpus analysis in general. The idea behind SuperCAT is that representative corpora have no tendency towards closure―that is, they tend towards infinity. In contrast, non-representative corpora have a tendency towards closure―roughly, finiteness. SuperCAT focuses on general techniques for the quantitative description of the characteristics of any corpus (or other language sample), particularly concerning the characteristics of lexical distributions. Additionally, SuperCAT features a complete re-engineering of the previous SubCAT architecture.
LanguageCrawl: A Generic Tool for Building Language Models Upon Common-Crawl
Authors: Szymon Roziewski, Wojciech Stokowiec
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Language Modeling, Language Modelling

The web data contains immense amount of data, hundreds of billion words are waiting to be extracted and used for language research. In this work we introduce our tool LanguageCrawl which allows NLP researchers to easily construct web-scale corpus from Common Crawl Archive: a petabyte scale, open repository of web crawl information. Three use-cases are presented: filtering Polish websites, building an N-gram corpora and training continuous skip-gram language model with hierarchical softmax. Each of them has been implemented within the LanguageCrawl toolkit, with the possibility to adjust specified language and N-gram ranks. Special effort has been put on high computing efficiency, by applying highly concurrent multitasking. We make our tool publicly available to enrich NLP resources. We strongly believe that our work will help to facilitate NLP research, especially in under-resourced languages, where the lack of appropriately sized corpora is a serious hindrance to applying data-intensive methods, such as deep neural networks.
Features for Generic Corpus Querying
Authors: Thomas Eckart, Christoph Kuras, Uwe Quasthoff
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: POS, TAG

The availability of large corpora for more and more languages enforces generic querying and standard interfaces. This development is especially relevant in the context of integrated research environments like CLARIN or DARIAH. The paper focuses on several applications and implementation details on the basis of a unified corpus format, a unique POS tag set, and prepared data for word similarities. All described data or applications are already or will be in the near future accessible via well-documented RESTful Web services. The target group are all kinds of interested persons with varying level of experience in programming or corpus query languages.
European Union Language Resources in Sketch Engine
Authors: V{\'\i}t Baisa, Jan Michelfeit, Marek Medve{\v{d}}, Milo{\v{s}} Jakub{\'\i}{\v{c}}ek
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Several parallel corpora built from European Union language resources are presented here. They were processed by state-of-the-art tools and made available for researchers in the corpus manager Sketch Engine. A completely new resource is introduced: EUR-Lex Corpus, being one of the largest parallel corpus available at the moment, containing 840 million English tokens and the largest language pair English-French has more than 25 million aligned segments (paragraphs).
Corpus Query Lingua Franca (CQLF)
Authors: Piotr Ba{\'n}ski, Elena Frick, Andreas Witt
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Management

The present paper describes Corpus Query Lingua Franca (ISO CQLF), a specification designed at ISO Technical Committee 37 Subcommittee 4 {``}Language resource management{''} for the purpose of facilitating the comparison of properties of corpus query languages. We overview the motivation for this endeavour and present its aims and its general architecture. CQLF is intended as a multi-part specification; here, we concentrate on the basic metamodel that provides a frame that the other parts fit in.
A sense-based lexicon of count and mass expressions: The Bochum English Countability Lexicon
Authors: Tibor Kiss, Francis Jeffry Pelletier, Halima Husic, Roman Nino Simunic, Johanna Marie Poppek
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Semantic Similarity, Semantic Textual Similarity

The present paper describes the current release of the Bochum English Countability Lexicon (BECL 2.1), a large empirical database consisting of lemmata from Open ANC (http://www.anc.org) with added senses from WordNet (Fellbaum 1998). BECL 2.1 contains {\mbox{$\approx$}} 11,800 annotated noun-sense pairs, divided in four major countability classes and 18 fine-grained subclasses. In the current version, BECL also provides information on nouns whose senses occur in more than one class allowing a closer look on polysemy and homonymy with regard to countability. Further included are sets of similar senses using the Leacock and Chodorow (LCH) score for semantic similarity (Leacock {\&} Chodorow 1998), information on orthographic variation, on the completeness of all WordNet senses in the database and an annotated representation of different types of proper names. The further development of BECL will investigate the different countability classes of proper names and the general relation between semantic similarity and countability as well as recurring syntactic patterns for noun-sense pairs. The BECL 2.1 database is also publicly available via http://count-and-mass.org.
Detecting Optional Arguments of Verbs
Authors: Andr{\'a}s Kornai, D{\'a}vid M{\'a}rk Nemeskey, G{\'a}bor Recski
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Clustering

We propose a novel method for detecting optional arguments of Hungarian verbs using only positive data. We introduce a custom variant of collexeme analysis that explicitly models the noise in verb frames. Our method is, for the most part, unsupervised: we use the spectral clustering algorithm described in Brew and Schulte in Walde (2002) to build a noise model from a short, manually verified seed list of verbs. We experimented with both raw count- and context-based clusterings and found their performance almost identical. The code for our algorithm and the frame list are freely available at http://hlt.bme.hu/en/resources/tade.
Leveraging Native Data to Correct Preposition Errors in Learners' Dutch
Authors: Lennart Kloppenburg, Malvina Nissim
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We address the task of automatically correcting preposition errors in learners{'} Dutch by modelling preposition usage in native language. Specifically, we build two models exploiting a large corpus of Dutch. The first is a binary model for detecting whether a preposition should be used at all in a given position or not. The second is a multiclass model for selecting the appropriate preposition in case one should be used. The models are tested on native as well as learners data. For the latter we exploit a crowdsourcing strategy to elicit native judgements. On native test data the models perform very well, showing that we can model preposition usage appropriately. However, the evaluation on learners{'} data shows that while detecting that a given preposition is wrong is doable reasonably well, detecting the absence of a preposition is a lot more difficult. Observing such results and the data we deal with, we envisage various ways of improving performance, and report them in the final section of this article.
D(H)ante: A New Set of Tools for XIII Century Italian
Authors: Angelo Basile, Federico Sangati
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: POS, TAG

In this paper we describe 1) the process of converting a corpus of Dante Alighieri from a TEI XML format in to a pseudo-CoNLL format; 2) how a pos-tagger trained on modern Italian performs on Dante{'}s Italian 3) the performances of two different pos-taggers trained on the given corpus. We are making our conversion scripts and models available to the community. The two other models trained on the corpus performs reasonably well. The tool used for the conversion process might turn useful for bridging the gap between traditional digital humanities and modern NLP applications since the TEI original format is not usually suitable for being processed with standard NLP tools. We believe our work will serve both communities: the DH community will be able to tag new documents and the NLP world will have an easier way in converting existing documents to a standardized machine-readable format.
Multilevel Annotation of Agreement and Disagreement in Italian News Blogs
Authors: Fabio Celli, Giuseppe Riccardi, Firoj Alam
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Sentence

In this paper, we present a corpus of news blog conversations in Italian annotated with gold standard agreement/disagreement relations at message and sentence levels. This is the first resource of this kind in Italian. From the analysis of ADRs at the two levels emerged that agreement annotated at message level is consistent and generally reflected at sentence level, moreover, the argumentation structure of disagreement is more complex than agreement. The manual error analysis revealed that this resource is useful not only for the analysis of argumentation, but also for the detection of irony/sarcasm in online debates. The corpus and annotation tool are available for research purposes on request.