pwc-archive/papers-with-abstracts huggingface dataset:
Effort of Genre Variation and Prediction of System Performance
Authors: Dong Wang, Fei Xia
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Domain Adaptation, Language Modelling, Machine Translation, POS, POS Tagging, Prediction

Domain adaptation is an important task in order for NLP systems to work well in real applications. There has been extensive research on this topic. In this paper, we address two issues that are related to domain adaptation. The first question is how much genre variation will affect NLP systems' performance. We investigate the effect of genre variation on the performance of three NLP tools, namely, word segmenter, POS tagger, and parser. We choose the Chinese Penn Treebank (CTB) as our corpus. The second question is how one can estimate NLP systems' performance when gold standard on the test data does not exist. To answer the question, we extend the prediction model in (Ravi et al., 2008) to provide prediction for word segmentation and POS tagging as well. Our experiments show that the predicted scores are close to the real scores when tested on the CTB data.
Dependency parsing for interaction detection in pharmacogenomics
Authors: Gerold Schneider, Fabio Rinaldi, Simon Clematide
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Dependency Parsing

We give an overview of our approach to the extraction of interactions between pharmacogenomic entities like drugs, genes and diseases and suggest classes of interaction types driven by data from PharmGKB and partly following the top level ontology WordNet and biomedical types from BioNLP. Our text mining approach to the extraction of interactions is based on syntactic analysis. We use syntactic analyses to explore domain events and to suggest a set of interaction labels for the pharmacogenomics domain.
Semantic Annotations in Japanese FrameNet: Comparing Frames in Japanese and English
Authors: Kyoko Ohara
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Cultural Vocal Bursts Intensity Prediction

Since 2008, the Japanese FrameNet (JFN, http://jfn.st.hc.keio.ac.jp/) project has been annotating the Balanced Corpus of Contemporary Written Japanese (BCCWJ), the first such corpus, officially released in October 2011. This paper reports annotation results of the book genre of BCCWJ (Ohara 2011, Ohara, Saito, Fujii {\&} Sato 2011). Comparing the semantic frames needed to annotate BCCWJ with those that the FrameNet (FN) project (Fillmore and Baker 2009, Fillmore 2006) already has defined revealed that: 1) differences in the Japanese and English semantic frames often concern different perspectives and different lexical aspects exhibited by the two lexicons; and 2) in most of the cases where JFN defined new semantic frame for a word, the frame did not involve culture-specific scenes. We investigated the extent to which existing semantic frames originally defined for analyzing English words were used, annotating 810 sentences of the so-called core data of the book genre of BCCWJ. In the 810 sentences we were able to assign semantic frames to approximately 4000 words, although we could not assign any to 587 words. That is, of all the LUs in the sentences, we were able to identify semantic frames to about 87 per cent of them. In other words, the semantic frames already defined in FN for English could be used for 87 per cent of the Japanese LUs.
Buildind a Resource of Patterns Using Semantic Types
Authors: Octavian Popescu
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Natural Language Inference, Word Sense Disambiguation

While a word in isolation has a high potential of expressing various senses, in certain phrases this potential is restricted up to the point that one and only one sense is possible. A phrase is called sense stable if the senses of all the words compounding it do not change their sense irrespective of the context which could be added to its left or to its right. By comparing sense stable phrases we can extract corpus patterns. These patterns have slots which are filled by semantic types that capture the relevant information for disambiguation. The relationship between slots is such that a chain like disambiguation process is possible. Annotating a corpus with these kinds of patterns is beneficial for NLP, because problems such as data sparseness, noise, learning complexity are alleviated. We evaluate the inter agreement of annotators on examples coming from BNC.
A data and analysis resource for an experiment in text mining a collection of micro-blogs on a political topic.
Authors: William Black, Rob Procter, Steven Gray, Sophia Ananiadou
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Named Entity Recognition (NER), Sentiment Analysis, text annotation

The analysis of a corpus of micro-blogs on the topic of the 2011 UK referendum about the Alternative Vote has been undertaken as a joint activity by text miners and social scientists. To facilitate the collaboration, the corpus and its analysis is managed in a Web-accessible framework that allows users to upload their own textual data for analysis and to manage their own text annotation resources used for analysis. The framework also allows annotations to be searched, and the analysis to be re-run after amending the analysis resources. The corpus is also doubly human-annotated stating both whether each tweet is overall positive or negative in sentiment and whether it is for or against the proposition of the referendum.
AWATIF: A Multi-Genre Corpus for Modern Standard Arabic Subjectivity and Sentiment Analysis
Authors: Muhammad Abdul-Mageed, Mona Diab
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Opinion Mining, Sentence, Sentiment Analysis

We present AWATIF, a multi-genre corpus of Modern Standard Arabic (MSA) labeled for subjectivity and sentiment analysis (SSA) at the sentence level. The corpus is labeled using both regular as well as crowd sourcing methods under three different conditions with two types of annotation guidelines. We describe the sub-corpora constituting the corpus and provide examples from the various SSA categories. In the process, we present our linguistically-motivated and genre-nuanced annotation guidelines and provide evidence showing their impact on the labeling task.
Causal analysis of task completion errors in spoken music retrieval interactions
Authors: Sunao Hara, Norihide Kitaoka, Kazuya Takeda
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: General Classification, Retrieval, TAG

In this paper, we analyze the causes of task completion errors in spoken dialog systems, using a decision tree with N-gram features of the dialog to detect task-incomplete dialogs. The dialog for a music retrieval task is described by a sequence of tags related to user and system utterances and behaviors. The dialogs are manually classified into two classes: completed and uncompleted music retrieval tasks. Differences in tag classification performance between the two classes are discussed. We then construct decision trees which can detect if a dialog finished with the task completed or not, using information gain criterion. Decision trees using N-grams of manual tags and automatic tags achieved 74.2{\%} and 80.4{\%} classification accuracy, respectively, while the tree using interaction parameters achieved an accuracy rate of 65.7{\%}. We also discuss more details of the causality of task incompletion for spoken dialog systems using such trees.
Generation of Verbal Stems in Derivationally Rich Language
Authors: Kre{\v{s}}imir {\v{S}}ojat, Nives Mikeli{\'c} Preradovi{\'c}, Marko Tadi{\'c}
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Text Generation, valid

The paper presents a procedure for generating prefixed verbs in Croatian comprising combinations of one, two or three prefixes. The result of this generation process is a pool of derivationally valid prefixed verbs, although not necessarily occuring in corpora. The statistics of occurences of generated verbs in Croatian National Corpus has been calculated. Further usage of such language resource with generated potential verbs is also suggested, namely, enrichment of Croatian Morphological Lexicon, Croatian Wordnet and CROVALLEX.
Corpus+WordNet thesaurus generation for ontology enriching
Authors: Fern Castilho, o, Roger Granada, Breno Meneghetti, Leonardo Carvalho, Renata Vieira
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Semantic Textual Similarity

This paper presents a model to enrich an ontology with a thesaurus based on a domain corpus and WordNet. The model is applied to the data privacy domain and the initial domain resources comprise a data privacy ontology, a corpus of privacy laws, regulations and guidelines for projects. Based on these resources, a thesaurus is automatically generated. The thesaurus seeds are composed by the ontology concepts. For these seeds similar terms are extracted from the corpus using known thesaurus generation methods. A filtering process searches for semantic relations between seeds and similar terms within WordNet. As a result, these semantic relations are used to expand the ontology with relations between them and related terms in the corpus. The resulting resource is a hierarchical structure that can help on the ontology investigation and maintenance. The results allow the investigation of the domain knowledge with the support of semantic relations not present on the original ontology.
Reclassifying subcategorization frames for experimental analysis and stimulus generation
Authors: Paula Buttery, Andrew Caines
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Language Acquisition

Researchers in the fields of psycholinguistics and neurolinguistics increasingly test their experimental hypotheses against probabilistic models of language. VALEX (Korhonen et al., 2006) is a large-scale verb lexicon that specifies verb usage as probability distributions over a set of 163 verb SUBCATEGORIZATION FRAMES (SCFs). VALEX has proved to be a popular computational linguistic resource and may also be used by psycho- and neurolinguists for experimental analysis and stimulus generation. However, a probabilistic model based upon a set of 163 SCFs often proves too fine grained for experimenters in these fields. Our goal is to simplify the classification by grouping the frames into generaâ€•explainable clusters that may be used as experimental parameters. We adopted two methods for reclassification. One was a manual linguistic approach derived from verb argumentation and clause features; the other was an automatic, computational approach driven from a graphical representation of SCFs. The premise was not only to compare the results of two quite different methods for our own interest, but also to enable other researchers to choose whichever reclassification better suited their purpose (one being grounded purely in theoretical linguistics and the other in practical language engineering). The various classifications are available as an online resource to researchers.
Creating a Coreference Resolution System for Polish
Authors: Mateusz Kope{\'c}, Maciej Ogrodniczuk
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: coreference-resolution, Coreference Resolution

Although the availability of the natural language processing tools and the development of metrics to evaluate them increases, there is a certain gap to fill in that field for the less-resourced languages, such as Polish. Therefore the projects which are designed to extend the existing tools for diverse languages are the best starting point for making these languages more and more covered. This paper presents the results of the first attempt of the co{\textbackslash}-re{\textbackslash}-fe{\textbackslash}-rence resolution for Polish using statistical methods. It presents the conclusions from the process of adapting the Beautiful Anaphora Resolution Toolkit (BART; a system primarily designed for the English language) for Polish and collates its evaluation results with those of the previously implemented rule-based system. Finally, we describe our plans for the future usage of the tool and highlight the upcoming research to be conducted, such as the experiments of a larger scale and the comparison with other machine learning tools.
LDC Forced Aligner
Authors: Xiaoyi Ma
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Sentence, Speech Recognition, Speech Synthesis, Text-To-Speech Synthesis

This paper describes the LDC forced aligner which was designed to align audio and transcripts. Unlike existing forced aligners, LDC forced aligner can align partially transcribed audio files, and also audio files with large chunks of non-speech segments, such as noise, music, silence etc, by inserting optional wildcard phoneme sequences between sentence or paragraph boundaries. Based on the HTK tool kit, LDC forced aligner can align audio and transcript on sentence or word level. This paper also reports its usage on English and Mandarin Chinese data.
Assessing the Comparability of News Texts
Authors: Emma Barker, Robert Gaizauskas
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Natural Language Inference, Translation

Comparable news texts are frequently proposed as a potential source of alignable subsentential fragments for use in statistical machine translation systems. But can we assess just how potentially useful they will be? In this paper we first discuss a scheme for classifying news text pairs according to the degree of relatedness of the events they report and investigate how robust this classification scheme is via a multi-lingual annotation exercise. We then propose an annotation methodology, similar to that used in summarization evaluation, to allow us to identify and quantify shared content at the subsentential level in news text pairs and report a preliminary exercise to assess this method. We conclude by discussing how this works fits into a broader programme of assessing the potential utility of comparable news texts for extracting paraphrases/translational equivalents for use in language processing applications.
Building Text-to-Speech Systems for Resource Poor Languages
Authors: Nur-Hana Samsudin, Mark Lee
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Clustering, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

This paper describes research on building text-to-speech synthesis systems (TTS) for resource poor languages using available resources from other languages and describes our general approach to building cross-linguistic polyglot TTS. Our approach involves three main steps: language clustering, grapheme to phoneme mapping and prosody modelling. We have tested the mapping of phonemes from German to English and from Indonesian to Spanish. We have also constructed three prosody representations for different language characteristics. For evaluation we have developed an English TTS based on German data, and a Spanish TTS based on Indonesian data and compared their performance against pre-existing monolingual TTSs. Since our motivation is to develop speech synthesis for resource poor languages, we have also developed three TTS for Iban, an Austronesian language with practically no available language resources, using Malay, Indonesian and Spanish resources.
Representing General Relational Knowledge in ConceptNet 5
Authors: Robyn Speer, Catherine Havasi
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Common Sense Reasoning

ConceptNet is a knowledge representation project, providing a large semantic graph that describes general human knowledge and how it is expressed in natural language. This paper presents the latest iteration, ConceptNet 5, including its fundamental design decisions, ways to use it, and evaluations of its coverage and accuracy.
Boosting statistical tagger accuracy with simple rule-based grammars
Authors: Mans Hulden, Jerid Francom
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Part-Of-Speech Tagging

We report on several experiments on combining a rule-based tagger and a trigram tagger for Spanish. The results show that one can boost the accuracy of the best performing n-gram taggers by quickly developing a rough rule-based grammar to complement the statistically induced one and then combining the output of the two. The specific method of combination is crucial for achieving good results. The method provides particularly large gains in accuracy when only a small amount of tagged data is available for training a HMM, as may be the case for lesser-resourced and minority languages.
A New Twitter Verb Lexicon for Natural Language Processing
Authors: Jennifer Williams, Graham Katz
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Game of Chess

We describe in-progress work on the creation of a new lexical resource that contains a list of 486 verbs annotated with quantified temporal durations for the events that they describe. This resource is being compiled from more than 14 million tweets from the Twitter microblogging site. We are creating this lexicon of verbs and typical durations to address a gap in the available information that is represented in existing research. The data that is contained in this lexical resource is unlike any existing resources, which have been traditionally comprised from literature excerpts, news stories, and full-length weblogs. The kind of knowledge about how long an event lasts is crucial for natural language processing and is especially useful when the temporal duration of an event is implied. We are using data from Twitter because Twitter is a rich resource since people are publicly posting about real events and real durations of those events throughout the day.
A finite-state morphological transducer for Kyrgyz
Authors: Jonathan Washington, Mirlan Ipasov, Francis Tyers
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Morphological Analysis, Translation

This paper describes the development of a free/open-source finite-state morphological transducer for Kyrgyz. The transducer has been developed for morphological generation for use within a prototype Turkish{\^a}Kyrgyz machine translation system, but has also been extensively tested for analysis. The finite-state toolkit used for the work was the Helsinki Finite-State Toolkit (HFST). The paper describes some issues in Kyrgyz morphology, the development of the tool, some linguistic issues encountered and how they were dealt with, and which issues are left to resolve. An evaluation is presented which shows that the transducer has medium-level coverage, between 82{\%} and 87{\%} on two freely available corpora of Kyrgyz, and high precision and recall over a manually verified test set.
A Corpus for Research on Deliberation and Debate
Authors: Marilyn Walker, Jean Fox Tree, Pranav Anand, Rob Abbott, Joseph King
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

Deliberative, argumentative discourse is an important component of opinion formation, belief revision, and knowledge discovery; it is a cornerstone of modern civil society. Argumentation is productively studied in branches ranging from theoretical artificial intelligence to political rhetoric, but empirical analysis has suffered from a lack of freely available, unscripted argumentative dialogs. This paper presents the Internet Argument Corpus (IAC), a set of 390,704 posts in 11,800 discussions extracted from the online debate site 4forums.com. A 2866 thread/130,206 post extract of the corpus has been manually sided for topic of discussion, and subsets of this topic-labeled extract have been annotated for several dialogic and argumentative markers: degrees of agreement with a previous post, cordiality, audience-direction, combativeness, assertiveness, emotionality of argumentation, and sarcasm. As an application of this resource, the paper closes with a discussion of the relationship between discourse marker pragmatics, agreement, emotionality, and sarcasm in the IAC corpus.
Evaluating the Similarity Estimator component of the TWIN Personality-based Recommender System
Authors: Alex Roshchina, ra, John Cardiff, Paolo Rosso
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Collaborative Filtering, Decision Making, Recommendation Systems

With the constant increase in the amount of information available in online communities, the task of building an appropriate Recommender System to support the user in her decision making process is becoming more and more challenging. In addition to the classical collaborative filtering and content based approaches, taking into account ratings, preferences and demographic characteristics of the users, a new type of Recommender System, based on personality parameters, has been emerging recently. In this paper we describe the TWIN (Tell Me What I Need) Personality Based Recommender System, and report on our experiments and experiences of utilizing techniques which allow the extraction of the personality type from text (following the Big Five model popular in the psychological research). We estimate the possibility of constructing the personality-based Recommender System that does not require users to fill in personality questionnaires. We are applying the proposed system in the online travelling domain to perform TripAdvisor hotels recommendation by analysing the text of user generated reviews, which are freely accessible from the community website.
Learning Sentiment Lexicons in Spanish
Authors: Ver{\'o}nica P{\'e}rez-Rosas, Carmen Banea, Rada Mihalcea
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Opinion Mining, Question Answering, Sentiment Analysis, Speech Synthesis, Subjectivity Analysis, Text-To-Speech Synthesis

In this paper we present a framework to derive sentiment lexicons in a target language by using manually or automatically annotated data available in an electronic resource rich language, such as English. We show that bridging the language gap using the multilingual sense-level aligned WordNet structure allows us to generate a high accuracy (90{\%}) polarity lexicon comprising 1,347 entries, and a disjoint lower accuracy (74{\%}) one encompassing 2,496 words. By using an LSA-based vectorial expansion for the generated lexicons, we are able to obtain an average F-measure of 66{\%} in the target language. This implies that the lexicons could be used to bootstrap higher-coverage lexicons using in-language resources.
Unsupervised Word Sense Disambiguation with Multilingual Representations
Authors: Fern, Erwin ez-Ordo{\~n}ez, Rada Mihalcea, Samer Hassan
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Translation, Word Sense Disambiguation

In this paper we investigate the role of multilingual features in improving word sense disambiguation. In particular, we explore the use of semantic clues derived from context translation to enrich the intended sense and therefore reduce ambiguity. Our experiments demonstrate up to 26{\%} increase in disambiguation accuracy by utilizing multilingual features as compared to the monolingual baseline.