From GLÀFF to PsychoGLÀFF: a large psycholinguistics-oriented French lexical resource

International audienceIn this paper, we present two French lexical resources, GLÀFF and PsychoGLÀFF. The former, automatically extracted from the collaborative online dictionary Wiktionary, is a large-scale versatile lexicon exploitable in Natural Language Processing applications and linguistic studies. The latter, based on GLÀFF, is a lexicon specifically designed for psycholinguistic research. GLÀFF, counting more than 1.4 million entries, features an unprecedented size. It reports lemmas, main syntactic categories, inflectional features and phonemic transcriptions. PsychoGLÀFF contains additional information related to formal aspects of the lexicon and its distribution. It contains about 340,000 entries (120,000 lemmas) that are corpora-attested. We explain how the resources have been created and compare them to other known resources in terms of coverage and quality. Regarding PsychoGLÀFF, the comparison shows that it has an exceptionally large repertoire while having a comparable quality

Publishing and Linking WordNet using lemon and RDF

McCrae J, Fellbaum C, Cimiano P. Publishing and Linking WordNet using lemon and RDF. In: Proceedings of the 3rd Workshop on Linked Data in Linguistics. 2014

Building the Emirati Arabic FrameNet

The Emirati Arabic FrameNet (EAFN) project aims to initiate a FrameNet for Emirati Arabic, utilizing the Emirati Arabic Corpus. The goal is to create a resource comparable to the initial stages of the Berkeley FrameNet. The project is divided into manual and automatic tracks, based on the predominant techniques being used to collect frames in each track. Work on the EAFN is progressing, and we here report on initial results for annotations and evaluation. The EAFN project aims to provide a general semantic resource for the Arabic language, sure to be of interest to researchers from general linguistics to natural language processing. As we report here, the EAFN is well on target for the first release of data in the coming year

GLÀFF, un Gros Lexique À tout Faire du Français

International audienceThis paper introduces GLÀFF, a large-scale versatile French lexicon extracted from Wiktionary, the collaborative online dictionary. GLÀFF contains, for each entry, a morphosyntactic description and a phonetic transcription. It distinguishes itself from the other available lexicons mainly by its size, its potential for constant updating and its copylefted license that makes it available for use, modification and redistribution. We explain how we have built GLÀFF and compare it to other known resources. We show that its size and quality are strong assets that could allow GLÀFF to become a reference lexicon for NLP, linguistics and psycholinguistics.Cet article présente GLÀFF, un lexique du français à large couverture extrait du Wiktionnaire, le dictionnaire collaboratif en ligne. GLÀFF contient pour chaque entrée une description morphosyntaxique et une transcription phonémique. Il se distingue des autres lexiques existants principalement par sa taille, sa licence libre et la possibilité de le faire évoluer de façon constante. Nous décrivons ici comment nous l'avons construit, puis caractérisé en le comparant à différentes ressources connues. Cette comparaison montre que sa taille et sa qualité font de GLÀFF un candidat sérieux comme nouvelle ressource standard pour le TAL, la linguistique et la psycholinguistique

Attaching Translations to Proper Lexical Senses in DBnary

International audienceThe DBnary project aims at providing high quality Lexical Linked Data extracted from different Wiktionary language editions. Data from 10 different languages is currently extracted for a total of over 3.16M translation links that connect lexical entries from the 10 extracted languages, to entries in more than one thousand languages. In Wiktionary, glosses are often associated with translations to help users understand to what sense they refer to, whether through a textual definition or a target sense number. In this article we aim at the extraction of as much of this information as possible and then the disambiguation of the corresponding translations for all languages available. We use an adaptation of various textual and semantic similarity techniques based on partial or fuzzy gloss overlaps to disambiguate the translation relations (To account for the lack of normalization, e.g. lemmatization and PoS tagging) and then extract some of the sense number information present to build a gold standard so as to evaluate our disambiguation as well as tune and optimize the parameters of the similarity measures. We obtain F-measures of the order of 80\% (on par with similar work on English only), across the three languages where we could generate a gold standard (French, Portuguese, Finnish) and show that most of the disambiguation errors are due to inconsistencies in Wiktionary itself that cannot be detected at the generation of DBnary (shifted sense numbers, inconsistent glosses, etc.)

Cross-Dictionary Linking at Sense Level with a Double-Layer Classifier

We present a system for linking dictionaries at the sense level, which is part of a wider programme aiming to extend current lexical resources and to create new ones by automatic means. One of the main challenges of the sense linking task is the existence of non one-to-one mappings among senses. Our system handles this issue by addressing the task as a binary classification problem using standard Machine Learning methods, where each sense pair is classified independently from the others. In addition, it implements a second, statistically-based classification layer to also model the dependence existing among sense pairs, namely, the fact that a sense in one dictionary that is already linked to a sense in the other dictionary has a lower probability of being linked to a further sense. The resulting double-layer classifier achieves global Precision and Recall scores of 0.91 and 0.80, respectively