Exploring automatic word sense disambiguation with decision lists and the web

  • Authors:
  • Eneko Agirre;David Martínez

  • Affiliations:
  • IxA NLP group, Donostia, Basque Country;IxA NLP group, Donostia, Basque Country

  • Venue:
  • Proceedings of the COLING-2000 Workshop on Semantic Annotation and Intelligent Content
  • Year:
  • 2000

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Abstract

The most effective paradigm for word sense disambiguation, supervised learning, seems to be stuck because of the knowledge acquisition bottleneck. In this paper we take an in-depth study of the performance of decision lists on two publicly available corpora and an additional corpus automatically acquired from the Web, using the fine-grained highly polysemous senses in WordNet. Decision lists are shown a versatile state-of-the-art technique. The experiments reveal, among other facts, that SemCor can be an acceptable (0.7 precision for polysemous words) starting point for an all-words system. The results on the DSO corpus show that for some highly polysemous words 0.7 precision seems to be the current state-of-the-art limit. On the other hand, independently constructed hand-tagged corpora are not mutually useful, and a corpus automatically acquired from the Web is shown to fail.