Extracting glosses to disambiguate word senses

  • Authors:
  • Weisi Duan;Alexander Yates

  • Affiliations:
  • Carnegie Mellon University, Pittsburgh, PA;Temple University, Philadelphia, PA

  • Venue:
  • HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
  • Year:
  • 2010

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Abstract

Like most natural language disambiguation tasks, word sense disambiguation (WSD) requires world knowledge for accurate predictions. Several proxies for this knowledge have been investigated, including labeled corpora, user-contributed knowledge, and machine readable dictionaries, but each of these proxies requires significant manual effort to create, and they do not cover all of the ambiguous terms in a language. We investigate the task of automatically extracting world knowledge, in the form of glosses, from an unlabeled corpus. We demonstrate how to use these glosses to automatically label a training corpus to build a statistical WSD system that uses no manually-labeled data, with experimental results approaching that of a supervised SVM-based classifier.