Ensemble Methods for Unsupervised WSD

Samuel Brody, Roberto Navigli, Mirella Lapata

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Combination methods are an effective way of improving system performance. This paper examines the benefits of system combination for unsupervised WSD. We investigate several voting-and arbiter-based combination strategies over a diverse pool of unsupervised WSD systems. Our combination methods rely on predominant senses which are derived automatically from raw text. Experiments using the SemCor and Senseval-3 data sets demonstrate that our ensembles yield significantly better results when compared with state-of-the-art.
Original languageEnglish
Title of host publicationACL 2006, 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, Sydney, Australia, 17-21 July 2006
PublisherAssociation for Computational Linguistics
Number of pages8
Publication statusPublished - 2006


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