pwc-archive/papers-with-abstracts huggingface dataset:
Media monitoring and information extraction for the highly inflected agglutinative language Hungarian
Authors: J{\'u}lia Pajzs, Ralf Steinberger, Maud Ehrmann, Mohamed Ebrahim, Leonida della Rocca, Stefano Bucci, Eszter Simon, Tam{\'a}s V{\'a}radi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Information Retrieval, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

The Europe Media Monitor (EMM) is a fully-automatic system that analyses written online news by gathering articles in over 70 languages and by applying text analysis software for currently 21 languages, without using linguistic tools such as parsers, part-of-speech taggers or morphological analysers. In this paper, we describe the effort of adding to EMM Hungarian text mining tools for news gathering; document categorisation; named entity recognition and classification for persons, organisations and locations; name lemmatisation; quotation recognition; and cross-lingual linking of related news clusters. The major challenge of dealing with the Hungarian language is its high degree of inflection and agglutination. We present several experiments where we apply linguistically light-weight methods to deal with inflection and we propose a method to overcome the challenges. We also present detailed frequency lists of Hungarian person and location name suffixes, as found in real-life news texts. This empirical data can be used to draw further conclusions and to improve existing Named Entity Recognition software. Within EMM, the solutions described here will also be applied to other morphologically complex languages such as those of the Slavic language family. The media monitoring and analysis system EMM is freely accessible online via the web page http://emm.newsbrief.eu/overview.html.
French Resources for Extraction and Normalization of Temporal Expressions with HeidelTime
Authors: V{\'e}ronique Moriceau, Xavier Tannier
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Information Retrieval

In this paper, we describe the development of French resources for the extraction and normalization of temporal expressions with HeidelTime, a open-source multilingual, cross-domain temporal tagger. HeidelTime extracts temporal expressions from documents and normalizes them according to the TIMEX3 annotation standard. Several types of temporal expressions are extracted: dates, times, durations and temporal sets. French resources have been evaluated in two different ways: on the French TimeBank corpus, a corpus of newspaper articles in French annotated according to the ISO-TimeML standard, and on a user application for automatic building of event timelines. Results on the French TimeBank are quite satisfaying as they are comparable to those obtained by HeidelTime in English and Spanish on newswire articles. Concerning the user application, we used two temporal taggers for the preprocessing of the corpus in order to compare their performance and results show that the performances of our application on French documents are better with HeidelTime. The French resources and evaluation scripts are publicly available with HeidelTime.
Legal aspects of text mining
Authors: Maarten Truyens, Patrick Van Eecke
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Unlike data mining, text mining has received only limited attention in legal circles. Nevertheless, interesting legal stumbling blocks exist, both with respect to the data collection and data sharing phases, due to the strict rules of copyright and database law. Conflicts are particularly likely when content is extracted from commercial databases, and when texts that have a minimal level of creativity are stored in a permanent way. In all circumstances, even with non-commercial research, license agreements and website terms of use can impose further restrictions. Accordingly, only for some delineated areas (very old texts for which copyright expired, legal statutes, texts in the public domain) strong legal certainty can be obtained without case-by-case assessments. As a result, while prior permission is certainly not required in all cases, many researchers tend to err on the side of caution, and seek permission from publishers, institutions and individual authors before including texts in their corpora, although this process can be difficult and very time-consuming. In the United States, the legal assessment is very different, due to the open-ended nature and flexibility offered by the {``}fair use{''} doctrine.
Treelet Probabilities for HPSG Parsing and Error Correction
Authors: Angelina Ivanova, Gertjan van Noord
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Grammatical Error Correction, Sentence

Most state-of-the-art parsers take an approach to produce an analysis for any input despite errors. However, small grammatical mistakes in a sentence often cause parser to fail to build a correct syntactic tree. Applications that can identify and correct mistakes during parsing are particularly interesting for processing user-generated noisy content. Such systems potentially could take advantage of linguistic depth of broad-coverage precision grammars. In order to choose the best correction for an utterance, probabilities of parse trees of different sentences should be comparable which is not supported by discriminative methods underlying parsing software for processing deep grammars. In the present work we assess the treelet model for determining generative probabilities for HPSG parsing with error correction. In the first experiment the treelet model is applied to the parse selection task and shows superior exact match accuracy than the baseline and PCFG. In the second experiment it is tested for the ability to score the parse tree of the correct sentence higher than the constituency tree of the original version of the sentence containing grammatical error.
A Corpus and Phonetic Dictionary for Tunisian Arabic Speech Recognition
Authors: Abir Masmoudi, Mariem Ellouze Khmekhem, Yannick Est{\`e}ve, lamia hadrich belguith, Nizar Habash
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Arabic Speech Recognition, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In this paper we describe an effort to create a corpus and phonetic dictionary for Tunisian Arabic Automatic Speech Recognition (ASR). The corpus, named TARIC (Tunisian Arabic Railway Interaction Corpus) has a collection of audio recordings and transcriptions from dialogues in the Tunisian Railway Transport Network. The phonetic (or pronunciation) dictionary is an important ASR component that serves as an intermediary between acoustic models and language models in ASR systems. The method proposed in this paper, to automatically generate a phonetic dictionary, is rule based. For that reason, we define a set of pronunciation rules and a lexicon of exceptions. To determine the performance of our phonetic rules, we chose to evaluate our pronunciation dictionary on two types of corpora. The word error rate of word grapheme-to-phoneme mapping is around 9{\%}.
Discovering frames in specialized domains
Authors: Marie-Claude L{'}Homme, Beno{\^\i}t Robichaud, Carlos Subirats R{\"u}ggeberg
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper proposes a method for discovering semantic frames (Fillmore, 1982, 1985; Fillmore et al., 2003) in specialized domains. It is assumed that frames are especially relevant for capturing the lexical structure in specialized domains and that they complement structures such as ontologies that appear better suited to represent specific relationships between entities. The method we devised is based on existing lexical entries recorded in a specialized database related to the field of the environment (erode, impact, melt, recycling, warming). The frames and the data encoded in FrameNet are used as a reference. Selected information was extracted automatically from the database on the environment (and, when possible, compared to FrameNet), and presented to a linguist who analyzed this information to discover potential frames. Several different frames were discovered with this method. About half of them correspond to frames already described in FrameNet; some new frames were also defined and part of these might be specific to the field of the environment.
Resources for the Detection of Conventionalized Metaphors in Four Languages
Authors: Lori Levin, Teruko Mitamura, Brian MacWhinney, Davida Fromm, Jaime Carbonell, Weston Feely, Robert Frederking, Anatole Gershman, Carlos Ramirez
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper describes a suite of tools for extracting conventionalized metaphors in English, Spanish, Farsi, and Russian. The method depends on three significant resources for each language: a corpus of conventionalized metaphors, a table of conventionalized conceptual metaphors (CCM table), and a set of extraction rules. Conventionalized metaphors are things like {``}escape from poverty{''} and {``}burden of taxation{''}. For each metaphor, the CCM table contains the metaphorical source domain word (such as {``}escape{''}) the target domain word (such as {``}poverty{''}) and the grammatical construction in which they can be found. The extraction rules operate on the output of a dependency parser and identify the grammatical configurations (such as a verb with a prepositional phrase complement) that are likely to contain conventional metaphors. We present results on detection rates for conventional metaphors and analysis of the similarity and differences of source domains for conventional metaphors in the four languages.
CLARIN-NL: Major results
Authors: Jan Odijk
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

In this paper I provide a high level overview of the major results of CLARIN-NL so far. I will show that CLARIN-NL is starting to provide the data, facilities and services in the CLARIN infrastructure to carry out humanities research supported by large amounts of data and tools. These services have easy interfaces and easy search options (no technical background needed). Still some training is required, to understand both the possibilities and the limitations of the data and the tools. Actual use of the facilities leads to suggestions for improvements and to suggestions for new functionality. All researchers are therefore invited to start using the elements in the CLARIN infrastructure offered by CLARIN-NL. Though I will show that a lot has been achieved in the CLARIN-NL project, I will also provide a long list of functionality and interoperability cases that have not been dealt with in CLARIN-NL and must remain for future work.
Exploiting Portuguese Lexical Knowledge Bases for Answering Open Domain Cloze Questions Automatically
Authors: Hugo Gon{\c{c}}alo Oliveira, In{\^e}s Coelho, Paulo Gomes
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Question Answering

We present the task of answering cloze questions automatically and how it can be tackled by exploiting lexical knowledge bases (LKBs). This task was performed in what can be seen as an indirect evaluation of Portuguese LKB. We introduce the LKBs used and the algorithms applied, and then report on the obtained results and draw some conclusions: LKBs are definitely useful resources for this challenging task, and exploiting them, especially with PageRanking-based algorithms, clearly improves the baselines. Moreover, larger LKB, created automatically and not sense-aware led to the best results, as opposed to handcrafted LKB structured on synsets.
Annotation of Computer Science Papers for Semantic Relation Extrac-tion
Authors: Yuka Tateisi, Yo Shidahara, Yusuke Miyao, Akiko Aizawa
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Relation, Relation Extraction, Retrieval

We designed a new annotation scheme for formalising relation structures in research papers, through the investigation of computer science papers. The annotation scheme is based on the hypothesis that identifying the role of entities and events that are described in a paper is useful for intelligent information retrieval in academic literature, and the role can be determined by the relationship between the author and the described entities or events, and relationships among them. Using the scheme, we have annotated research abstracts from the IPSJ Journal published in Japanese by the Information Processing Society of Japan. On the basis of the annotated corpus, we have developed a prototype information extraction system which has the facility to classify sentences according to the relationship between entities mentioned, to help find the role of the entity in which the searcher is interested.
Identifying Idioms in Chinese Translations
Authors: Wan Yu Ho, Christine Kng, Shan Wang, Francis Bond
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

Optimally, a translated text should preserve information while maintaining the writing style of the original. When this is not possible, as is often the case with figurative speech, a common practice is to simplify and make explicit the implications. However, in our investigations of translations from English to another language, English-to-Chinese texts were often found to include idiomatic expressions (usually in the form of Chengyu {\ae}{\`e} ̄) where there were originally no idiomatic, metaphorical, or even figurative expressions. We have created an initial small lexicon of Chengyu, with which we can use to find all occurrences of Chengyu in a given corpus, and will continue to expand the database. By examining the rates and patterns of occurrence across four genres in the NTU Multilingual Corpus, a resource may be created to aid machine translation or, going further, predict Chinese translational trends in any given genre.
Machine Translation for Subtitling: A Large-Scale Evaluation
Authors: Thierry Etchegoyhen, Lindsay Bywood, Mark Fishel, Panayota Georgakopoulou, Jie Jiang, Gerard van Loenhout, Arantza del Pozo, Mirjam Sepesy Mau{\v{c}}ec, Anja Turner, Martin Volk
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modelling, Machine Translation, Translation

This article describes a large-scale evaluation of the use of Statistical Machine Translation for professional subtitling. The work was carried out within the FP7 EU-funded project SUMAT and involved two rounds of evaluation: a quality evaluation and a measure of productivity gain/loss. We present the SMT systems built for the project and the corpora they were trained on, which combine professionally created and crowd-sourced data. Evaluation goals, methodology and results are presented for the eleven translation pairs that were evaluated by professional subtitlers. Overall, a majority of the machine translated subtitles received good quality ratings. The results were also positive in terms of productivity, with a global gain approaching 40{\%}. We also evaluated the impact of applying quality estimation and filtering of poor MT output, which resulted in higher productivity gains for filtered files as opposed to fully machine-translated files. Finally, we present and discuss feedback from the subtitlers who participated in the evaluation, a key aspect for any eventual adoption of machine translation technology in professional subtitling.
T-PAS; A resource of Typed Predicate Argument Structures for linguistic analysis and semantic processing
Authors: Elisabetta Jezek, Bernardo Magnini, Anna Feltracco, Alessia Bianchini, Octavian Popescu
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Clustering

The goal of this paper is to introduce T-PAS, a resource of typed predicate argument structures for Italian, acquired from corpora by manual clustering of distributional information about Italian verbs, to be used for linguistic analysis and semantic processing tasks. T-PAS is the first resource for Italian in which semantic selection properties and sense-in-context distinctions of verbs are characterized fully on empirical ground. In the paper, we first describe the process of pattern acquisition and corpus annotation (section 2) and its ongoing evaluation (section 3). We then demonstrate the benefits of pattern tagging for NLP purposes (section 4), and discuss current effort to improve the annotation of the corpus (section 5). We conclude by reporting on ongoing experiments using semiautomatic techniques for extending coverage (section 6).
Narrowing the Gap Between Termbases and Corpora in Commercial Environments
Authors: Kara Warburton
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Translation

Terminological resources offer potential to support applications beyond translation, such as controlled authoring and indexing, which are increasingly of interest to commercial enterprises. The ad-hoc semasiological approach adopted by commercial terminographers diverges considerably from methodologies prescribed by conventional theory. The notion of termhood in such production-oriented environments is driven by pragmatic criteria such as frequency and repurposability of the terminological unit. A high degree of correspondence between the commercial corpus and the termbase is desired. Research carried out at the City University of Hong Kong using four IT companies as case studies revealed a large gap between corpora and termbases. Problems in selecting terms and in encoding them properly in termbases account for a significant portion of this gap. A rigorous corpus-based approach to term selection would significantly reduce this gap and improve the effectiveness of commercial termbases. In particular, single-word terms (keywords) identified by comparison to a reference corpus offer great potential for identifying important multi-word terms in this context. We conclude that terminography for production purposes should be more corpus-based than is currently the norm.
Author-Specific Sentiment Aggregation for Polarity Prediction of Reviews
Authors: Subhabrata Mukherjee, Sachindra Joshi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dependency Parsing, General Classification, Prediction, Sentiment Analysis, Topic Models

In this work, we propose an author-specific sentiment aggregation model for polarity prediction of reviews using an ontology. We propose an approach to construct a Phrase Annotated Author Specific Sentiment Ontology Tree (PASOT), where the facet nodes are annotated with opinion phrases of the author, used to describe the facets, as well as the author{'}s preference for the facets. We show that an author-specific aggregation of sentiment over an ontology fares better than a flat classification model, which does not take the domain-specific facet importance or author-specific facet preference into account. We compare our approach to supervised classification using Support Vector Machines, as well as other baselines from previous works, where we achieve an accuracy improvement of 7.55{\%} over the SVM baseline. Furthermore, we also show the effectiveness of our approach in capturing thwarting in reviews, achieving an accuracy improvement of 11.53{\%} over the SVM baseline.
Clustering of Multi-Word Named Entity variants: Multilingual Evaluation
Authors: Guillaume Jacquet, Maud Ehrmann, Ralf Steinberger
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Clustering, Document Classification, Form, Question Answering

Multi-word entities, such as organisation names, are frequently written in many different ways. We have previously automatically identified over one million acronym pairs in 22 languages, consisting of their short form (e.g. EC) and their corresponding long forms (e.g. European Commission, European Union Commission). In order to automatically group such long form variants as belonging to the same entity, we cluster them, using bottom-up hierarchical clustering and pair-wise string similarity metrics. In this paper, we address the issue of how to evaluate the named entity variant clusters automatically, with minimal human annotation effort. We present experiments that make use of Wikipedia redirection tables and we show that this method produces good results.
A Database for Measuring Linguistic Information Content
Authors: Richard Sproat, Bruno Cartoni, HyunJeong Choe, David Huynh, Linne Ha, Ravindran Rajakumar, Evelyn Wenzel-Grondie
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Which languages convey the most information in a given amount of space? This is a question often asked of linguists, especially by engineers who often have some information theoretic measure of information in mind, but rarely define exactly how they would measure that information. The question is, in fact remarkably hard to answer, and many linguists consider it unanswerable. But it is a question that seems as if it ought to have an answer. If one had a database of close translations between a set of typologically diverse languages, with detailed marking of morphosyntactic and morphosemantic features, one could hope to quantify the differences between how these different languages convey information. Since no appropriate database exists we decided to construct one. The purpose of this paper is to present our work on the database, along with some preliminary results. We plan to release the dataset once complete.
Designing and Evaluating a Reliable Corpus of Web Genres via Crowd-Sourcing
Authors: Noushin Rezapour Asheghi, Serge Sharoff, Katja Markert
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Machine Translation

Research in Natural Language Processing often relies on a large collection of manually annotated documents. However, currently there is no reliable genre-annotated corpus of web pages to be employed in Automatic Genre Identification (AGI). In AGI, documents are classified based on their genres rather than their topics or subjects. The major shortcoming of available web genre collections is their relatively low inter-coder agreement. Reliability of annotated data is an essential factor for reliability of the research result. In this paper, we present the first web genre corpus which is reliably annotated. We developed precise and consistent annotation guidelines which consist of well-defined and well-recognized categories. For annotating the corpus, we used crowd-sourcing which is a novel approach in genre annotation. We computed the overall as well as the individual categories{'} chance-corrected inter-annotator agreement. The results show that the corpus has been annotated reliably.
Crowdsourcing as a preprocessing for complex semantic annotation tasks
Authors: H{\'e}ctor Mart{\'\i}nez Alonso, Lauren Romeo
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This article outlines a methodology that uses crowdsourcing to reduce the workload of experts for complex semantic tasks. We split turker-annotated datasets into a high-agreement block, which is not modified, and a low-agreement block, which is re-annotated by experts. The resulting annotations have higher observed agreement. We identify different biases in the annotation for both turkers and experts.