Regroupement sémantique de relations pour l'extraction d'information non supervisée

Abstract : Most studies in unsupervised information extraction concentrate on the relation extraction and few work has been proposed on the organization of the extracted relations. We present in this paper a two-step clustering procedure to group semantically equivalent relations : a first step clusters relations with similar expressions while a second step groups these first clusters into larger semantic clusters, using different semantic similarities. Our experiments show the stability of distributional similarities over WordNet-based similarities for semantic clustering. We also demonstrate that the use of a multi-level clustering not only reduces the calculations from all relation pairs to basic clusters pairs, but it also improves the clustering results.
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Wei Wang, Romaric Besançon, Olivier Ferret, Brigitte Grau. Regroupement sémantique de relations pour l'extraction d'information non supervisée. 20ème Conférence sur le Traitement Automatique des Langues Naturelles (TALN 2013), Jun 2013, Les Sables d Olonne, France. pp.353-366. ⟨cea-01858474⟩

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