How to Combine Text-Mining Methods to Validate Induced Verb-Object Relations?

Nicolas Béchet 1 Jacques Chauché 2 Violaine Prince 2 Mathieu Roche 3, 4, *
* Corresponding author
1 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
2 TEXTE - Exploration et exploitation de données textuelles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : This paper describes methods using Natural Language Processing approaches to extract and validate induced syntactic relations (here restricted to the Verb-Object relation). These methods use a syntactic parser and a semantic closeness measure to extract such relations. Then, their validation is based on two different techniques: A Web Validation system on one part, then a Semantic-Vectorbased approach, and finally different combinations of both techniques in order to rank induced Verb-Object relations. The Semantic Vector approach is a Roget-based method which computes a syntactic relation as a vector. Web Validation uses a search engine to determine the relevance of a syntactic relation according to its popularity. An experimental protocol is set up to judge automatically the relevance of the sorted induced relations. We finally apply our approach on a French corpus of news by using ROC Curves to evaluate the results.
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Nicolas Béchet, Jacques Chauché, Violaine Prince, Mathieu Roche. How to Combine Text-Mining Methods to Validate Induced Verb-Object Relations?. Computer Science and Information Systems, ComSIS Consortium, 2014, 11 (1), pp.133-155. ⟨http://www.comsis.org/archive.php?show=ppr471-1305⟩. ⟨10.2298/CSIS130528021B⟩. ⟨lirmm-01054918⟩

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