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Reconnaissance d'implications textuelles à forte composante linguistique

Marilisa Amoia 1
1 TALARIS - Natural Language Processing: representation, inference and semantics
Inria Nancy - Grand Est, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this thesis, I investigate how lexical resources based on the organisation of lexical knowledge in classes which share common (syntactic, semantic, etc.) features support natural language processing and in particular symbolic recognition of textual entailment. First, I present a robust and wide coverage approach to lexico-structural verb paraphrase recognition based on Levin's (1993) classification of English verbs. Then, I show that by extending Levin's framework to general inference patterns, a classification of English adjectives can be obtained that compared with previous approaches, provides a more fine grained semantic characterisation of their inferential properties. Further, I develop a compositional semantic framework to assign a semantic representation to adjectives based on an ontologically promiscuous approach \citep{Hobbs85} and thereby supporting first order inference for all types of adjectives including extensional ones. Finally, I present a test suite for adjectival inference I developed as a resource for the evaluation of computational systems handling natural language inference.
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Submitted on : Thursday, November 13, 2008 - 4:53:07 PM
Last modification on : Thursday, March 5, 2020 - 4:49:21 PM
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  • HAL Id : tel-00338608, version 1



Marilisa Amoia. Reconnaissance d'implications textuelles à forte composante linguistique. Informatique [cs]. Universität des Saarlandes Saarbrücken, 2008. Français. ⟨tel-00338608⟩



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