Extraction d'information de sous-catégorisation à partir des tables du LADL
Identifieur interne : 005C13 ( Hal/Corpus ); précédent : 005C12; suivant : 005C14Extraction d'information de sous-catégorisation à partir des tables du LADL
Auteurs : Claire Gardent ; Bruno Guillaume ; Guy Perrier ; Ingrid FalkSource :
Descripteurs français
- mix :
Abstract
Maurice Gross' grammar lexicon contains rich and exhaustive information about the morphosyntactic and syntactic properties of French syntactic functors (verbs, adjectives, nouns). Yet its use within natural language processing systems is hampered both by its non standard encoding and by a structure that is partly implicit and partly underspecified. In this paper, we present a method for translating this information into a format more amenable for use by NLP systems, we discuss the results obtained so far, we compare our approach with related work and we identify the possible further uses that can be made of the reformatted information.
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Hal:inria-00103163Le document en format XML
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<front><div type="abstract" xml:lang="en">Maurice Gross' grammar lexicon contains rich and exhaustive information about the morphosyntactic and syntactic properties of French syntactic functors (verbs, adjectives, nouns). Yet its use within natural language processing systems is hampered both by its non standard encoding and by a structure that is partly implicit and partly underspecified. In this paper, we present a method for translating this information into a format more amenable for use by NLP systems, we discuss the results obtained so far, we compare our approach with related work and we identify the possible further uses that can be made of the reformatted information.</div>
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<hal api="V3"><titleStmt><title xml:lang="fr">Extraction d'information de sous-catégorisation à partir des tables du LADL</title>
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<abstract xml:lang="en">Maurice Gross' grammar lexicon contains rich and exhaustive information about the morphosyntactic and syntactic properties of French syntactic functors (verbs, adjectives, nouns). Yet its use within natural language processing systems is hampered both by its non standard encoding and by a structure that is partly implicit and partly underspecified. In this paper, we present a method for translating this information into a format more amenable for use by NLP systems, we discuss the results obtained so far, we compare our approach with related work and we identify the possible further uses that can be made of the reformatted information.</abstract>
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