pwc-archive/papers-with-abstracts huggingface dataset:
Discovering the Italian literature: interactive access to audio indexed text resources
Authors: Vincenzo Galat{\`a}, Alberto Benin, Piero Cosi, Giuseppe Riccardo Leone, Giulio Paci, Giacomo Sommavilla, Fabio Tesser
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Cultural Vocal Bursts Intensity Prediction, Sentence, text-to-speech, Text to Speech

In this paper we present a web interface to study Italian through the access to read Italian literature. The system allows to browse the content, search for specific words and listen to the correct pronunciation produced by native speakers in a given context. This work aims at providing people who are interested in learning Italian with a new way of exploring the Italian culture and literature through a web interface with a search module. By submitting a query, users may browse and listen to the results through several modalities including: a) the voice of a native speaker: if an indexed audio track is available, the user can listen either to the query terms or to the whole context in which they appear (sentence, paragraph, verse); b) a synthetic voice: the user can listen to the results read by a text-to-speech system; c) an avatar: the user can listen to and look at a talking head reading the paragraph and visually reproducing real speech articulatory movements. In its up to date version, different speech technologies currently being developed at ISTC-CNR are implemented into a single framework. The system will be described in detail and hints for future work are discussed.
Enabling Language Resources to Expose Translations as Linked Data on the Web
Authors: Jorge Gracia, Elena Montiel-Ponsoda, Daniel Vila-Suero, Guadalupe Aguado-de-Cea
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

Language resources, such as multilingual lexica and multilingual electronic dictionaries, contain collections of lexical entries in several languages. Having access to the corresponding explicit or implicit translation relations between such entries might be of great interest for many NLP-based applications. By using Semantic Web-based techniques, translations can be available on the Web to be consumed by other (semantic enabled) resources in a direct manner, not relying on application-specific formats. To that end, in this paper we propose a model for representing translations as linked data, as an extension of the lemon model. Our translation module represents some core information associated to term translations and does not commit to specific views or translation theories. As a proof of concept, we have extracted the translations of the terms contained in Terminesp, a multilingual terminological database, and represented them as linked data. We have made them accessible on the Web both for humans (via a Web interface) and software agents (with a SPARQL endpoint).
Pivot-based multilingual dictionary building using Wiktionary
Authors: Judit {\'A}cs
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Translation

We describe a method for expanding existing dictionaries in several languages by discovering previously non-existent links between translations. We call this method triangulation and we present and compare several variations of it. We assess precision manually, and recall by comparing the extracted dictionaries with independently obtained basic vocabulary sets. We featurize the translation candidates and train a maximum entropy classifier to identify correct translations in the noisy data.
Exploring the utility of coreference chains for improved identification of personal names
Authors: Andrea Glaser, Jonas Kuhn
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Machine Translation

Identifying the real world entity that a proper name refers to is an important task in many NLP applications. Context plays an important role in disambiguating entities with the same names. In this paper, we discuss a dataset and experimental set-up that allows us to systematically explore the effects of different sizes and types of context in this disambiguation task. We create context by first identifying coreferent expressions in the document and then combining sentences these expressions occur in to one informative context. We apply different filters to obtain different levels of coreference-based context. Since hand-labeling a dataset of a decent size is expensive, we investigate the usefulness of an automatically created pseudo-ambiguity dataset. The results on this pseudo-ambiguity dataset show that using coreference-based context performs better than using a fixed window of context around the entity. The insights taken from the pseudo data experiments can be used to predict how the method works with real data. In our experiments on real data we obtain comparable results.
Multilingual eXtended WordNet Knowledge Base: Semantic Parsing and Translation of Glosses
Authors: Tatiana Erekhinskaya, Meghana Satpute, Dan Moldovan
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Semantic Parsing, Translation

This paper presents a method to create WordNet-like lexical resources for different languages. Instead of directly translating glosses from one language to another, we perform first semantic parsing of WordNet glosses and then translate the resulting semantic representation. The proposed approach simplifies the machine translation of the glosses. The approach provides ready to use semantic representation of glosses in target languages instead of just plain text.
Relation Inference in Lexical Networks ... with Refinements
Authors: Manel Zarrouk, Mathieu Lafourcade
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Relation

Improving lexical networkÂ’s quality is an important issue in the creation process of these language resources. This can be done by automatically inferring new relations from already existing ones with the purpose of (1) densifying the relations to cover the eventual lack of information and (2) detecting errors. In this paper, we devise such an approach applied to the JeuxDeMots lexical network, which is a freely available lexical and semantic resource for French. We first present the principles behind the lexical network construction with crowdsourcing and games with a purpose and illustrated them with JeuxDeMots (JDM). Then, we present the outline of an elicitation engine based on an inference engine using schemes like deduction, induction and abduction which will be referenced and briefly presented and we will especially highlight the new scheme (Relation Inference Scheme with Refinements) added to our system. An experiment showing the relevance of this scheme is then presented.
A Multimodal Dataset for Deception Detection
Authors: Ver{\'o}nica P{\'e}rez-Rosas, Rada Mihalcea, Alexis Narvaez, Mihai Burzo
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Deception Detection

This paper presents the construction of a multimodal dataset for deception detection, including physiological, thermal, and visual responses of human subjects under three deceptive scenarios. We present the experimental protocol, as well as the data acquisition process. To evaluate the usefulness of the dataset for the task of deception detection, we present a statistical analysis of the physiological and thermal modalities associated with the deceptive and truthful conditions. Initial results show that physiological and thermal responses can differentiate between deceptive and truthful states.
C-PhonoGenre: a 7-hours corpus of 7 speaking styles in French: relations between situational features and prosodic properties
Authors: Jean-Philippe Goldman, Tea Pr{\v{s}}ir, Antoine Auchlin
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Phonogenres, or speaking styles, are typified acoustic images associated to types of language activities, causing prosodic and phonostylistic variations. This communication presents a large speech corpus (7 hours) in French, extending a previous work by Goldman et al. (2011a), Simon et al. (2010), with a greater number and complementary repertoire of considered phonogenres. The corpus is available with segmentation at phonetic, syllabic and word levels, as well as manual annotation. Segmentations and annotations were achieved semi-automatically, through a set of Praat implemented tools, and manual steps. The phonogenres are also described with a reduced set of situational dimensions as in Lucci (1983) and Koch {\&} Oesterreichers (2001). A preliminary acoustic study, joining rhythmical comparative measurements (Dellwo 2010) to Goldman et al.s (2007a) ProsoReport, reports acoustic differences between phonogenres.
The AMARA Corpus: Building Parallel Language Resources for the Educational Domain
Authors: Ahmed Abdelali, Francisco Guzman, Hassan Sajjad, Stephan Vogel
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e. 20 monolingual corpora and 190 parallel corpora. This corpus includes both resource-rich languages such as English and Arabic, and resource-poor languages such as Hindi and Thai. In this paper, we describe the gathering, validation, and preprocessing of a large collection of parallel, community-generated subtitles. Furthermore, we describe the methodology used to prepare the data for Machine Translation tasks. Additionally, we provide a document-level, jointly aligned development and test sets for 14 language pairs, designed for tuning and testing Machine Translation systems. We provide baseline results for these tasks, and highlight some of the challenges we face when building machine translation systems for educational content.
Dependency parsing representation effects on the accuracy of semantic applications --- an example of an inflective language
Authors: Lauma Pretkalni{\c{n}}a, Art{\=u}rs Znoti{\c{n}}{\v{s}}, Laura Rituma, Didzis Go{\v{s}}ko
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: coreference-resolution, Coreference Resolution, Dependency Parsing, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Semantic Role Labeling

In this paper we investigate how different dependency representations of a treebank influence the accuracy of the dependency parser trained on this treebank and the impact on several parser applications: named entity recognition, coreference resolution and limited semantic role labeling. For these experiments we use Latvian Treebank, whose native annotation format is dependency based hybrid augmented with phrase-like elements. We explore different representations of coordinations, complex predicates and punctuation mark attachment. Our experiments shows that parsers trained on the variously transformed treebanks vary significantly in their accuracy, but the best-performing parser as measured by attachment score not always leads to best accuracy for an end application.
Co-clustering of bilingual datasets as a mean for assisting the construction of thematic bilingual comparable corpora
Authors: Guiyao Ke, Pierre-Francois Marteau
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Clustering, Information Retrieval

We address in this paper the assisted construction of bilingual thematic comparable corpora by means of co-clustering bilingual documents collected from raw sources such as the Web. The proposed approach is based on a quantitative comparability measure and a co-clustering approach which allow to mix similarity measures existing in each of the two linguistic spaces with a {``}thematic{''} comparability measure that defines a mapping between these two spaces. With the improvement of the co-clustering ({\$}k{\$}-medoids) performance we get, we use a comparability threshold and a manual verification to ensure the good and robust alignment of co-clusters (co-medoids). Finally, from any available raw corpus, we enrich the aligned clusters in order to provide {``}thematic{''} comparable corpora of good quality and controlled size. On a case study that exploit raw web data, we show that this approach scales reasonably well and is quite suited for the construction of thematic comparable corpora of good quality.
Ruled-based, Interlingual Motivated Mapping of plWordNet onto SUMO Ontology
Authors: Pawe{\l} K{\k{e}}dzia, Maciej Piasecki
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

In this paper we study a rule-based approach to mapping plWordNet onto SUMO Upper Ontology on the basis of the already existing mappings: plWordNet -- the Princeton WordNet -- SUMO. Information acquired from the inter-lingual relations between plWordNet and Princeton WordNet and the relations between Princeton WordNet and SUMO ontology are used in the proposed rules. Several mapping rules together with the matching examples are presented. The automated mapping results were evaluated in two steps, (i) we automatically checked formal correctness of the mappings for the pairs of plWordNet synset and SUMO concept, (ii) a subset of 160 mapping examples was manually checked by two+one linguists. We analyzed types of the mapping errors and their causes. The proposed rules expressed very high precision, especially when the errors in the resources are taken into account. Because both wordnets were constructed independently and as a result the obtained rules are not trivial and they reveal the differences between both wordnets and both languages.
Extending the coverage of a MWE database for Persian CPs exploiting valency alternations
Authors: Pollet Samvelian, Pegah Faghiri, Sarra El Ayari
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

PersPred is a manually elaborated multilingual syntactic and semantic Lexicon for Persian Complex Predicates (CPs), referred to also as Light Verb Constructions (LVCs) or Compound Verbs. CPs constitutes the regular and the most common way of expressing verbal concepts in Persian, which has only around 200 simplex verbs. CPs can be defined as multi-word sequences formed by a verb and a non-verbal element and functioning in many respects as a simplex verb. Bonami {\&} Samvelain (2010) and Samvelian {\&} Faghiri (to appear) extendedly argue that Persian CPs are MWEs and consequently must be listed. The first delivery of PersPred, contains more than 600 combinations of the verb zadan hit with a noun, presented in a spreadsheet. In this paper we present a semi-automatic method used to extend the coverage of PersPred 1.0, which relies on the syntactic information on valency alternations already encoded in the database. Given the importance of CPs in the verbal lexicon of Persian and the fact that lexical resources cruelly lack for Persian, this method can be further used to achieve our goal of making PersPred an appropriate resource for NLP applications.
Finding a Tradeoff between Accuracy and Rater's Workload in Grading Clustered Short Answers
Authors: Andrea Horbach, Alexis Palmer, Magdalena Wolska
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: All, Clustering

n this paper we investigate the potential of answer clustering for semi-automatic scoring of short answer questions for German as a foreign language. We use surface features like word and character n-grams to cluster answers to listening comprehension exercises per question and simulate having human graders only label one answer per cluster and then propagating this label to all other members of the cluster. We investigate various ways to select this single item to be labeled and find that choosing the item closest to the centroid of a cluster leads to improved (simulated) grading accuracy over random item selection. Averaged over all questions, we can reduce a teacherÂ’s workload to labeling only 40{\%} of all different answers for a question, while still maintaining a grading accuracy of more than 85{\%}.
Macrosyntactic Segmenters of a French Spoken Corpus
Authors: Ilaine Wang, Sylvain Kahane, Isabelle Tellier
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The aim of this paper is to describe an automated process to segment spoken French transcribed data into macrosyntactic units. While sentences are delimited by punctuation marks for written data, there is no obvious hint nor limit to major units for speech. As a reference, we used the manual annotation of macrosyntactic units based on illocutionary as well as syntactic criteria and developed for the Rhapsodie corpus, a 33.000 words prosodic and syntactic treebank. Our segmenters were built using machine learning methods as supervised classifiers{\textasciitilde}: segmentation is about identifying the boundaries of units, which amounts to classifying each interword space. We trained six different models on Rhapsodie using different sets of features, including prosodic and morphosyntactic cues, on the assumption that their combination would be relevant for the task. Both types of cues could be resulting either from manual annotation/correction or from fully automated processes, which comparison might help determine the cost of manual effort, especially for the 3M words of spoken French of the Orfeo project those experiments are contributing to.
Vulnerability in Acquisition, Language Impairments in Dutch: Creating a VALID Data Archive
Authors: Jetske Klatter, Roel van Hout, Henk van den Heuvel, Paula Fikkert, Anne Baker, Jan de Jong, Frank Wijnen, S, Eric ers, Paul Trilsbeek
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: All, Language Acquisition, valid

The VALID Data Archive is an open multimedia data archive (under construction) with data from speakers suffering from language impairments. We report on a pilot project in the CLARIN-NL framework in which five data resources were curated. For all data sets concerned, written informed consent from the participants or their caretakers has been obtained. All materials were anonymized. The audio files were converted into wav (linear PCM) files and the transcriptions into CHAT or ELAN format. Research data that consisted of test, SPSS and Excel files were documented and converted into CSV files. All data sets obtained appropriate CMDI metadata files. A new CMDI metadata profile for this type of data resources was established and care was taken that ISOcat metadata categories were used to optimize interoperability. After curation all data are deposited at the Max Planck Institute for Psycholinguistics Nijmegen where persistent identifiers are linked to all resources. The content of the transcriptions in CHAT and plain text format can be searched with the TROVA search engine.
The Extended DIRNDL Corpus as a Resource for Coreference and Bridging Resolution
Authors: Anders Bj{\"o}rkelund, Kerstin Eckart, Arndt Riester, Nadja Schauffler, Katrin Schweitzer
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: coreference-resolution, Coreference Resolution

DIRNDL is a spoken and written corpus based on German radio news, which features coreference and information-status annotation (including bridging anaphora and their antecedents), as well as prosodic information. We have recently extended DIRNDL with a fine-grained two-dimensional information status labeling scheme. We have also applied a state-of-the-art part-of-speech and morphology tagger to the corpus, as well as highly accurate constituency and dependency parsers. In the light of this development we believe that DIRNDL is an interesting resource for NLP researchers working on automatic coreference and bridging resolution. In order to enable and promote usage of the data, we make it available for download in an accessible tabular format, compatible with the formats used in the CoNLL and SemEval shared tasks on automatic coreference resolution.
A flexible language learning platform based on language resources and web services
Authors: Elena Volodina, Ildik{\'o} Pil{\'a}n, Lars Borin, Therese Lindstr{\"o}m Tiedemann
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Acquisition, Text Simplification

We present L{\"a}rka, the language learning platform of Spr{\"a}kbanken (the Swedish Language Bank). It consists of an exercise generator which reuses resources available through Spr{\"a}kbanken: mainly Korp, the corpus infrastructure, and Karp, the lexical infrastructure. Through L{\"a}rka we reach new user groups ― students and teachers of Linguistics as well as second language learners and their teachers ― and this way bring Spr{\"a}kbanken{'}s resources in a relevant format to them. L{\"a}rka can therefore be viewed as an case of real-life language resource evaluation with end users. In this article we describe L{\"a}rka{'}s architecture, its user interface, and the five exercise types that have been released for users so far. The first user evaluation following in-class usage with students of linguistics, speech therapy and teacher candidates are presented. The outline of future work concludes the paper.
Towards interoperable discourse annotation. Discourse features in the Ontologies of Linguistic Annotation
Authors: Christian Chiarcos
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper describes the extension of the Ontologies of Linguistic Annotation (OLiA) with respect to discourse features. The OLiA ontologies provide a a terminology repository that can be employed to facilitate the conceptual (semantic) interoperability of annotations of discourse phenomena as found in the most important corpora available to the community, including OntoNotes, the RST Discourse Treebank and the Penn Discourse Treebank. Along with selected schemes for information structure and coreference, discourse relations are discussed with special emphasis on the Penn Discourse Treebank and the RST Discourse Treebank. For an example contained in the intersection of both corpora, I show how ontologies can be employed to generalize over divergent annotation schemes.
Morphological parsing of Swahili using crowdsourced lexical resources
Authors: Patrick Littell, Kaitlyn Price, Lori Levin
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation

We describe a morphological analyzer for the Swahili language, written in an extension of XFST/LEXC intended for the easy declaration of morphophonological patterns and importation of lexical resources. Our analyzer was supplemented extensively with data from the Kamusi Project (kamusi.org), a user-contributed multilingual dictionary. Making use of this resource allowed us to achieve wide lexical coverage quickly, but the heterogeneous nature of user-contributed content also poses some challenges when adapting it for use in an expert system.
Improving Entity Linking using Surface Form Refinement
Authors: Eric Charton, Marie-Jean Meurs, Ludovic Jean-Louis, Michel Gagnon
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Entity Linking, Entity Resolution, Form, Named Entity Recognition (NER), Word Sense Disambiguation

In this paper, we present an algorithm for improving named entity resolution and entity linking by using surface form generation and rewriting. Surface forms consist of a word or a group of words that matches lexical units like Paris or New York City. Used as matching sequences to select candidate entries in a knowledge base, they contribute to the disambiguation of those candidates through similarity measures. In this context, misspelled textual sequences (entities) can be impossible to identify due to the lack of available matching surface forms. To address this problem, we propose an algorithm for surface form refinement based on Wikipedia resources. The approach extends the surface form coverage of our entity linking system, and rewrites or reformulates misspelled mentions (entities) prior to starting the annotation process. The algorithm is evaluated on the corpus associated with the monolingual English entity linking task of NIST KBP 2013. We show that the algorithm improves the entity linking system performance.