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Réordonnancer des thésaurus distributionnels en combinant différents critères

Abstract : In this article, we propose a method for improving distributional thesauri based on a bootstrapping mechanism: a set of positive and negative examples of semantically similar words are selected in an unsupervised way and used for training a supervised classifier. This classifier is then applied for reranking the semantic neighbors of the thesaurus used for example selection. We show how the relations between the mono-terms of similar nominal compounds can be used for performing this selection and how to associate this criterion, either by early fusion or late fusion, with an already tested criterion based on the symmetry of semantic relations. We evaluate the interest of the proposed procedure for a large set of English nouns with various frequencies. This article is an extended version of (Ferret, 2013 ; Ferret, 2015a).
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Submitted on : Monday, July 23, 2018 - 2:34:25 PM
Last modification on : Tuesday, September 21, 2021 - 9:48:11 AM


  • HAL Id : cea-01847291, version 1



Olivier Ferret. Réordonnancer des thésaurus distributionnels en combinant différents critères. Revue TAL, ATALA (Association pour le Traitement Automatique des Langues), 2015, 56 (2), pp.21-49. ⟨cea-01847291⟩



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