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Classifying texts through natural language parsing and semantic filtering

Jacques Chauché 1 Violaine Prince 1 
1 TEXTE - Exploration et exploitation de données textuelles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : This paper presents a study in text classification through semantic and syntactic natural language processing. The authors have used a parser for French, SYGFRAN, and applied it to a real project of press articles classification. The results of this research on a corpus of 4, 843 texts containing more than 76, 000 sentences are described. Classification into 37 categories has been obtained through meaning discrimination by semantic filtering techniques, explained in the document.
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Contributor : Violaine Prince-Barbier Connect in order to contact the contributor
Submitted on : Thursday, October 11, 2007 - 3:28:30 PM
Last modification on : Friday, August 5, 2022 - 3:03:22 PM
Long-term archiving on: : Monday, September 24, 2012 - 1:17:09 PM


  • HAL Id : lirmm-00178563, version 1



Jacques Chauché, Violaine Prince. Classifying texts through natural language parsing and semantic filtering. 3rd International Language and Technology Conference, Oct 2007, Poznan, Pologne, pp.012-020. ⟨lirmm-00178563⟩



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