Ensemble methods for unsupervised WSD

  • Authors:
  • Samuel Brody;Roberto Navigli;Mirella Lapata

  • Affiliations:
  • University of Edinburgh;Università di Roma "La Sapienza";University of Edinburgh

  • Venue:
  • ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
  • Year:
  • 2006

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Abstract

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.