Word sense disambiguation based on word sense clustering

Authors:
Henry Anaya-Sánchez;Aurora Pons-Porrata;Rafael Berlanga-Llavori
Affiliations:
Center of Pattern Recognition and Data Mining, Universidad de Oriente, Cuba;Center of Pattern Recognition and Data Mining, Universidad de Oriente, Cuba;Universitat Jaume I, Castellón, Spain
Venue:
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Year:
2006

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Abstract

In this paper we address the problem of Word Sense Disambiguation by introducing a knowledge-driven framework for the disambiguation of nouns. The proposal is based on the clustering of noun sense representations and it serves as a general model that includes some existing disambiguation methods. A first prototype algorithm for the framework, relying on both topic signatures built from WordNet and the Extended Star clustering algorithm, is also presented. This algorithm yields encouraging experimental results for the SemCor corpus, showing improvements in recall over other knowledge-driven methods.