Vers la création d'un Verbnet du français

International audienceVerbNet est une ressource lexicale pour les verbes anglais qui est bien utile pour le TAL grâce à sa large couverture et sa classification cohérente. Une telle ressource n'existe pas pour le français malgré quelques tentatives. Nous montrons comment adapter semi-automatiquement VerbNet en utilisant deux ressources lexicales existantes, le LVF (Les Verbes Français) et le LG (Lexique-Grammaire). Abstract. VerbNet is an English lexical resource that has proven useful for NLP due to its high coverage and coherent classification. Such a resource doesn't exist for French, despite some (mostly automatic and unsupervised) at-tempts. We show how to semi-automatically adapt VerbNet using existing lexical resources, namely LVF (Les Verbes Français) and LG (Lexique-Grammaire). Mots-clés : VerbNet, cadres de sous-catégorisations, rôles sémantiques

Single Classifier Approach for Verb Sense Disambiguation based on Generalized Features

Abstract We present a supervised method for verb sense disambiguation based on VerbNet. Most previous supervised approaches to verb sense disambiguation create a classifier for each verb that reaches a frequency threshold. These methods, however, have a significant practical problem that they cannot be applied to rare or unseen verbs. In order to overcome this problem, we create a single classifier to be applied to rare or unseen verbs in a new text. This single classifier also exploits generalized semantic features of a verb and its modifiers in order to better deal with rare or unseen verbs. Our experimental results show that the proposed method achieves equivalent performance to per-verb classifiers, which cannot be applied to unseen verbs. Our classifier could be utilized to improve the classifications in lexical resources of verbs, such as VerbNet, in a semi-automatic manner and to possibly extend the coverage of these resources to new verbs

Vers la création d'un Verbnet du français

International audienceVerbNet est une ressource lexicale pour les verbes anglais qui est bien utile pour le TAL grâce à sa large couverture et sa classification cohérente. Une telle ressource n'existe pas pour le français malgré quelques tentatives. Nous montrons comment adapter semi-automatiquement VerbNet en utilisant deux ressources lexicales existantes, le LVF (Les Verbes Français) et le LG (Lexique-Grammaire). Abstract. VerbNet is an English lexical resource that has proven useful for NLP due to its high coverage and coherent classification. Such a resource doesn't exist for French, despite some (mostly automatic and unsupervised) at-tempts. We show how to semi-automatically adapt VerbNet using existing lexical resources, namely LVF (Les Verbes Français) and LG (Lexique-Grammaire). Mots-clés : VerbNet, cadres de sous-catégorisations, rôles sémantiques

Probabilistic models of language processing and acquisition

Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with probabilistic theories of categorization and ambiguity resolution in perception

An Empirical Comparison of VerbNet Syntactic Frames and the Semlink Corpus

This paper describes a method of automatically comparing syntactic frames from the verb lexicon VerbNet with syntactic frames from the Semlink corpus. A method of extracting syntactic frames and semantic argument structures is explained, followed by a method of comparing syntactic frames, both directly and by argument structure. The results of the comparison are described in terms of matching success for frame tokens and frame types, divided into categories based on frame type frequency within Semlink. Overall, 54.14% of the frame tokens within Semlink can be directly matched to VerbNet, with an additional 14.32% matching by argument structure. However, only 29.30% of the frame types within Semlink can be matched to VerbNet, suggesting that the comparison method cannot match a majority of the large variation of frames types in Semlink. A set of distinguishing frame types for VerbNet classes is also proposed and included in this work

Exploiting a Verb Lexicon in Automatic Semantic Role Labelling

We develop an unsupervised semantic role labelling system that relies on the direct application of information in a predicate lexicon combined with a simple probability model. We demonstrate the usefulness of predicate lexicons for role labelling, as well as the feasibility of modifying an existing role-labelled corpus for evaluating a different set of semantic roles. We achieve a substantial improvement over an informed baseline.

Proceedings of HLT/EMNLP 2005 Exploiting a Verb Lexicon in Automatic Semantic Role Labelling

We develop an unsupervised semantic role labelling system that relies on the direct application of information in a predicate lexicon combined with a simple probability model. We demonstrate the usefulness of predicate lexicons for role labelling, as well as the feasibility of modifying an existing role-labelled corpus for evaluating a different set of semantic roles. We achieve a substantial improvement over an informed baseline.