pwc-archive/papers-with-abstracts huggingface dataset:
Capturing syntactico-semantic regularities among terms: An application of the FrameNet methodology to terminology
Authors: Marie-Claude L{'}Homme, Janine Pimentel
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

Terminological databases do not always provide detailed information on the linguistic behaviour of terms, although this is important for potential users such as translators or students. In this paper we describe a project that aims to fill this gap by proposing a method for annotating terms in sentences based on that developed within the FrameNet project (Ruppenhofer et al. 2010) and by implementing it in an online resource called DiCoInfo. We focus on the methodology we devised, and show with some preliminary results how similar actantial (i.e. argumental) structures can provide evidence for defining lexical relations in specific languages and capturing cross-linguistic equivalents. The paper argues that the syntactico-semantic annotation of the contexts in which the terms occur allows lexicographers to validate their intuitions concerning the linguistic behaviour of terms as well as interlinguistic relations between them. The syntactico-semantic annotation of contexts could, therefore, be considered a good starting point in terminology work that aims to describe the linguistic functioning of terms and offer a sounder basis to define interlinguistic relationships between terms that belong to different languages.
The FAUST Corpus of Adequacy Assessments for Real-World Machine Translation Output
Authors: Daniele Pighin, Llu{\'\i}s M{\`a}rquez, Llu{\'\i}s Formiga
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Translation

We present a corpus consisting of 11,292 real-world English to Spanish automatic translations annotated with relative (ranking) and absolute (adequate/non-adequate) quality assessments. The translation requests, collected through the popular translation portal http://reverso.net, provide a most variated sample of real-world machine translation (MT) usage, from complete sentences to units of one or two words, from well-formed to hardly intelligible texts, from technical documents to colloquial and slang snippets. In this paper, we present 1) a preliminary annotation experiment that we carried out to select the most appropriate quality criterion to be used for these data, 2) a graph-based methodology inspired by Interactive Genetic Algorithms to reduce the annotation effort, and 3) the outcomes of the full-scale annotation experiment, which result in a valuable and original resource for the analysis and characterization of MT-output quality.
Annotating Story Timelines as Temporal Dependency Structures
Authors: Steven Bethard, Oleks Kolomiyets, R, Marie-Francine Moens
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Reading Comprehension, Relation

We present an approach to annotating timelines in stories where events are linked together by temporal relations into a temporal dependency tree. This approach avoids the disconnected timeline problems of prior work, and results in timelines that are more suitable for temporal reasoning. We show that annotating timelines as temporal dependency trees is possible with high levels of inter-annotator agreement - Krippendorff's Alpha of 0.822 on selecting event pairs, and of 0.700 on selecting temporal relation labels - even with the moderately sized relation set of BEFORE, AFTER, INCLUDES, IS-INCLUDED, IDENTITY and OVERLAP. We also compare several annotation schemes for identifying story events, and show that higher inter-annotator agreement can be reached by focusing on only the events that are essential to forming the timeline, skipping words in negated contexts, modal contexts and quoted speech.
An ontological approach to model and query multimodal concurrent linguistic annotations
Authors: Julien Seinturier, Elisabeth Murisasco, Emmanuel Bruno, Philippe Blache
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

This paper focuses on the representation and querying of knowledge-based multimodal data. This work stands in the OTIM project which aims at processing multimodal annotation of a large conversational French speech corpus. Within OTIM, we aim at providing linguists with a unique framework to encode and manipulate numerous linguistic domains (from prosody to gesture). Linguists commonly use Typed Feature Structures (TFS) to provide an uniform view of multimodal annotations but such a representation cannot be used within an applicative framework. Moreover TFS expressibility is limited to hierarchical and constituency relations and does not suit to any linguistic domain that needs for example to represent temporal relations. To overcome these limits, we propose an ontological approach based on Description logics (DL) for the description of linguistic knowledge and we provide an applicative framework based on OWL DL (Ontology Web Language) and the query language SPARQL.
A PropBank for Portuguese: the CINTIL-PropBank
Authors: Ant{\'o}nio Branco, Catarina Carvalheiro, S{\'\i}lvia Pereira, Sara Silveira, Jo{\~a}o Silva, S{\'e}rgio Castro, Jo{\~a}o Gra{\c{c}}a
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

With the CINTIL-International Corpus of Portuguese, an ongoing corpus annotated with fully flegded grammatical representation, sentences get not only a high level of lexical, morphological and syntactic annotation but also a semantic analysis that prepares the data to a manual specification step and thus opens the way for a number of tools and resources for which there is a great research focus at the present. This paper reports on the construction of a propbank that builds on CINTIL-DeepGramBank, with nearly 10 thousand sentences, on the basis of a deep linguistic grammar and on the process and the linguistic criteria guiding that construction, which makes possible to obtain a complete PropBank with both syntactic and semantic levels of linguistic annotation. Taking into account this and the promising scores presented in this study for inter-annotator agreement, CINTIL-PropBank presents itself as a great resource to train a semantic role labeller, one of our goals with this project.
Highlighting relevant concepts from Topic Signatures
Authors: Montse Cuadros, Llu{\'\i}s Padr{\'o}, German Rigau
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Word Sense Disambiguation

This paper presents deepKnowNet, a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. Basically, the method applies a knowledge-based Word Sense Disambiguation algorithm to assign the most appropriate WordNet sense to large sets of topically related words acquired from the web, named TSWEB. This Word Sense Disambiguation algorithm is the personalized PageRank algorithm implemented in UKB. This new method improves by automatic means the current content of WordNet by creating large volumes of new and accurate semantic relations between synsets. KnowNet was our first attempt towards the acquisition of large volumes of semantic relations. However, KnowNet had some limitations that have been overcomed with deepKnowNet. deepKnowNet disambiguates the first hundred words of all Topic Signatures from the web (TSWEB). In this case, the method highlights the most relevant word senses of each Topic Signature and filter out the ones that are not so related to the topic. In fact, the knowledge it contains outperforms any other resource when is empirically evaluated in a common framework based on a similarity task annotated with human judgements.
A tool for enhanced search of multilingual digital libraries of e-journals
Authors: Ranka Stankovi{\'c}, Cvetana Krstev, Ivan Obradovi{\'c}, Aleks Trtovac, ra, Milo{\v{s}} Utvi{\'c}
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Articles, Information Retrieval, Machine Translation

This paper outlines the main features of Bibli{\v{s}}a, a tool that offers various possibilities of enhancing queries submitted to large collections of TMX documents generated from aligned parallel articles residing in multilingual digital libraries of e-journals. The queries initiated by a simple or multiword keyword, in Serbian or English, can be expanded by Bibli{\v{s}}a, both semantically and morphologically, using different supporting monolingual and multilingual resources, such as wordnets and electronic dictionaries. The tool operates within a complex system composed of several modules including a web application, which makes it readily accessible on the web. Its functionality has been tested on a collection of 44 TMX documents generated from articles published bilingually by the journal INFOtecha, yielding encouraging results. Further enhancements of the tool are underway, with the aim of transforming it from a powerful full-text and metadata search tool, to a useful translator's aid, which could be of assistance both in reviewing terminology used in context and in refining the multilingual resources used within the system.
Extending a wordnet framework for simplicity and scalability
Authors: Pedro Fialho, S{\'e}rgio Curto, Ana Cristina Mendes, Lu{\'\i}sa Coheur
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Retrieval

The WordNet knowledge model is currently implemented in multiple software frameworks providing procedural access to language instances of it. Frameworks tend to be focused on structural/design aspects of the model thus describing low level interfaces for linguistic knowledge retrieval. Typically the only high level feature directly accessible is word lookup while traversal of semantic relations leads to verbose/complex combinations of data structures, pointers and indexes which are irrelevant in an NLP context. Here is described an extension to the JWNL framework that hides technical requirements of access to WordNet features with an essentially word/sense based API applying terminology from the official online interface. This high level API is applied to the original English version of WordNet and to an SQL based Portuguese lexicon, translated into a WordNet based representation usable by JWNL.
DeCour: a corpus of DEceptive statements in Italian COURts
Authors: Tommaso Fornaciari, Massimo Poesio
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Deception Detection

In criminal proceedings, sometimes it is not easy to evaluate the sincerity of oral testimonies. DECOUR - DEception in COURt corpus - has been built with the aim of training models suitable to discriminate, from a stylometric point of view, between sincere and deceptive statements. DECOUR is a collection of hearings held in four Italian Courts, in which the speakers lie in front of the judge. These hearings become the object of a specific criminal proceeding for calumny or false testimony, in which the deceptiveness of the statements of the defendant is ascertained. Thanks to the final Court judgment, that points out which lies are told, each utterance of the corpus has been annotated as true, uncertain or false, according to its degree of truthfulness. Since the judgment of deceptiveness follows a judicial inquiry, the annotation has been realized with a greater degree of confidence than ever before. Moreover, in Italy this is the first corpus of deceptive texts not relying on Â‘mock' lies created in laboratory conditions, but which has been collected in a natural environment.
Irish Treebanking and Parsing: A Preliminary Evaluation
Authors: Teresa Lynn, {\"O}zlem {\c{C}}etino{\u{g}}lu, Jennifer Foster, Elaine U{\'\i} Dhonnchadha, Mark Dras, Josef van Genabith
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, POS

Language resources are essential for linguistic research and the development of NLP applications. Low-density languages, such as Irish, therefore lack significant research in this area. This paper describes the early stages in the development of new language resources for Irish â€• namely the first Irish dependency treebank and the first Irish statistical dependency parser. We present the methodology behind building our new treebank and the steps we take to leverage upon the few existing resources. We discuss language-specific choices made when defining our dependency labelling scheme, and describe interesting Irish language characteristics such as prepositional attachment, copula, and clefting. We manually develop a small treebank of 300 sentences based on an existing POS-tagged corpus and report an inter-annotator agreement of 0.7902. We train MaltParser to achieve preliminary parsing results for Irish and describe a bootstrapping approach for further stages of development.
Expertise Mining for Enterprise Content Management
Authors: Georgeta Bordea, Sabrina Kirrane, Paul Buitelaar, Bianca Pereira
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval, Management, Term Extraction, Topic Models

Enterprise content analysis and platform configuration for enterprise content management is often carried out by external consultants that are not necessarily domain experts. In this paper, we propose a set of methods for automatic content analysis that allow users to gain a high level view of the enterprise content. Here, a main concern is the automatic identification of key stakeholders that should ideally be involved in analysis interviews. The proposed approach employs recent advances in term extraction, semantic term grounding, expert profiling and expert finding in an enterprise content management setting. Extracted terms are evaluated using human judges, while term grounding is evaluated using a manually created gold standard for the DBpedia datasource.
Word Alignment for English-Turkish Language Pair
Authors: Mehmet Talha {\c{C}}akmak, S{\"u}leyman Acar, G{\"u}l{\c{s}}en Eryi{\u{g}}it
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Translation, Word Alignment

Word alignment is an important step for machine translation systems. Although the alignment performance between grammatically similar languages is reported to be very high in many studies, the case is not the same for language pairs from different language families. In this study, we are focusing on English-Turkish language pairs. Turkish is a highly agglutinative language with a very productive and rich morphology whereas English has a very poor morphology when compared to this language. As a result of this, one Turkish word is usually aligned with several English words. The traditional models which use word-level alignment approaches generally fail in such circumstances. In this study, we evaluate a Giza++ system by splitting the words into their morphological units (stem and suffixes) and compare the model with the traditional one. For the first time, we evaluate the performance of our aligner on gold standard parallel sentences rather than in a real machine translation system. Our approach reduced the alignment error rate by 40{\%} relative. Finally, a new test corpus of 300 manually aligned sentences is released together with this study.
Comparing performance of different set-covering strategies for linguistic content optimization in speech corpora
Authors: Nelly Barbot, Olivier Boeffard, Arnaud Delhay
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Descriptive, Speech Recognition, Speech Synthesis

Set covering algorithms are efficient tools for solving an optimal linguistic corpus reduction. The optimality of such a process is directly related to the descriptive features of the sentences of a reference corpus. This article suggests to verify experimentally the behaviour of three algorithms, a greedy approach and a lagrangian relaxation based one giving importance to rare events and a third one considering the Kullback-Liebler divergence between a reference and the ongoing distribution of events. The analysis of the content of the reduced corpora shows that the both first approaches stay the most effective to compress a corpus while guaranteeing a minimal content. The variant which minimises the Kullback-Liebler divergence guarantees a distribution of events close to a reference distribution as expected; however, the price for this solution is a much more important corpus. In the proposed experiments, we have also evaluated a mixed-approach considering a random complement to the smallest coverings.
PEXACC: A Parallel Sentence Mining Algorithm from Comparable Corpora
Authors: Radu Ion
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval, Machine Translation, Sentence, Translation

Extracting parallel data from comparable corpora in order to enrich existing statistical translation models is an avenue that attracted a lot of research in recent years. There are experiments that convincingly show how parallel data extracted from comparable corpora is able to improve statistical machine translation. Yet, the existing body of research on parallel sentence mining from comparable corpora does not take into account the degree of comparability of the corpus being processed or the computation time it takes to extract parallel sentences from a corpus of a given size. We will show that the performance of a parallel sentence extractor crucially depends on the degree of comparability such that it is more difficult to process a weakly comparable corpus than a strongly comparable corpus. In this paper we describe PEXACC, a distributed (running on multiple CPUs), trainable parallel sentence/phrase extractor from comparable corpora. PEXACC is freely available for download with the ACCURAT Toolkit, a collection of MT-related tools developed in the ACCURAT project.
A Framework for Spelling Correction in Persian Language Using Noisy Channel Model
Authors: Mohammad Hoseyn Sheykholeslam, Behrouz Minaei-Bidgoli, Hossein Juzi
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Spelling Correction

There are several methods offered for spelling correction in Farsi (Persian) Language. Unfortunately no powerful framework has been implemented because of lack of a large training set in Farsi as an accurate model. A training set consisting of erroneous and related correction string pairs have been obtained from a large number of instances of the books each of which were typed two times in Computer Research Center of Islamic Sciences. We trained our error model using this huge set. In testing part after finding erroneous words in sample text, our program proposes some candidates for related correction. The paper focuses on describing the method of ranking related corrections. This method is customized version of Noisy Channel Spelling Correction for Farsi. This ranking method attempts to find intended correction c from a typo t, that maximizes P(c) P(t | c). In this paper different methods are described and analyzed to obtain a wide overview of the field. Our evaluation results show that Noisy Channel Model using our corpus and training set in this framework works more accurately and improves efficiently in comparison with other methods.
Dbnary: Wiktionary as a LMF based Multilingual RDF network
Authors: Gilles S{\'e}rasset
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval, Retrieval

Contributive resources, such as wikipedia, have proved to be valuable in Natural Language Processing or Multilingual Information Retrieval applications.This article focusses on Wiktionary, the dictionary part of the collaborative resources sponsored by the Wikimedia foundation. In this article we present a word net that has been extracted from French, English and German wiktionaries. We present the structure of this word net and discuss the specific extraction problems induced by this kind of contributive resources and the method used to overcome them. Then we show how we represent the extracted data as a Lexical Markup Framework (LMF) compatible lexical network represented in Resource Description Framework (RDF) format.
Dysarthric Speech Database for Development of QoLT Software Technology
Authors: Dae-Lim Choi, Bong-Wan Kim, Yeon-Whoa Kim, Yong-Ju Lee, Yongnam Um, Minhwa Chung
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Speech Recognition

This paper describes the creation of a dysarthric speech database which has been done as part of a national program to help the disabled lead a better life â€• a challenging endeavour that is targeting development of speech technologies for people with articulation disabilities. The additional aims of this database are to study the phonetic characteristics of the different types of the disabled persons, develop the automatic method to assess degrees of disability, and investigate the phonetic features of dysarthric speech. For these purposes, a large database of about 600 mildly or moderately severe dysarthric persons is planned for a total of 4 years (2010.06. 01 {\textasciitilde} 2014.05.31). At present a dysarthric speech database of 120 speakers has been collected and we are continuing to record new speakers with cerebral paralysis of mild and moderate severity. This paper also introduces the prompting items, the assessment of the speech disability severity of the speakers, and other considerations for the creation of a dysarthric speech.
CLTC: A Chinese-English Cross-lingual Topic Corpus
Authors: Yunqing Xia, Guoyu Tang, Peng Jin, Xia Yang
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Articles, Clustering, Text Clustering

Cross-lingual topic detection within text is a feasible solution to resolving the language barrier in accessing the information. This paper presents a Chinese-English cross-lingual topic corpus (CLTC), in which 90,000 Chinese articles and 90,000 English articles are organized within 150 topics. Compared with TDT corpora, CLTC has three advantages. First, CLTC is bigger in size. This makes it possible to evaluate the large-scale cross-lingual text clustering methods. Second, articles are evenly distributed within the topics. Thus it can be used to produce test datasets for different purposes. Third, CLTC can be used as a cross-lingual comparable corpus to develop methods for cross-lingual information access. A preliminary evaluation with CLTC corpus indicates that the corpus is effective in evaluating cross-lingual topic detection methods.
Customizable SCF Acquisition in Italian
Authors: Tommaso Caselli, Francesco Rubino, Francesca Frontini, Irene Russo, Valeria Quochi
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Dependency Parsing, Information Retrieval, Machine Translation

Lexica of predicate-argument structures constitute a useful tool for several tasks in NLP. This paper describes a web-service system for automatic acquisition of verb subcategorization frames (SCFs) from parsed data in Italian. The system acquires SCFs in an unsupervised manner. We created two gold standards for the evaluation of the system, the first by mixing together information from two lexica (one manually created and the second automatically acquired) and manual exploration of corpus data and the other annotating data extracted from a specialized corpus (environmental domain). Data filtering is accomplished by means of the maximum likelihood estimate (MLE). The evaluation phase has allowed us to identify the best empirical MLE threshold for the creation of a lexicon (P=0.653, R=0.557, F1=0.601). In addition to this, we assigned to the extracted entries of the lexicon a confidence score based on the relative frequency and evaluated the extractor on domain specific data. The confidence score will allow the final user to easily select the entries of the lexicon in terms of their reliability: one of the most interesting feature of this work is the possibility the final users have to customize the results of the SCF extractor, obtaining different SCF lexica in terms of size and accuracy.