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Jugement exact de grammaticalité d'arbre syntaxique probable

Jean-Philippe Prost 1
1 TEXTE - Exploration et exploitation de données textuelles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : The robustness of probabilistic parsing generally comes at the expense of grammaticality judgment – the grammaticality of the most probable output parse remaining unknown. Parsers, such as the Stanford or the Reranking ones, can not discriminate between grammatical and ungrammatical probable parses, whether their surface realisations are themselves grammatical or not. In this paper we show that a Model-Theoretic representation of Syntax alleviates the grammaticality judgment on a parse tree. In order to demonstrate the practicality and usefulness of an alliance between stochastic parsing and knowledge-based representation, we introduce an exact method for putting a binary grammatical judgment on a probable phrase structure. We experiment with parse trees generated by a probabilistic parser. We show experimental evidence on parse trees generated by a probabilistic parser to confirm our hypothesis.
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Contributor : Jean-Philippe Prost <>
Submitted on : Monday, February 20, 2017 - 12:17:09 PM
Last modification on : Thursday, December 6, 2018 - 9:06:36 PM
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  • HAL Id : lirmm-01471804, version 1



Jean-Philippe Prost. Jugement exact de grammaticalité d'arbre syntaxique probable. TALN: Traitement Automatique des Langues Naturelles, Jul 2014, Marseille, France. ⟨lirmm-01471804⟩



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