Text and knowledge mining for coreference resolution

  • Authors:
  • Sanda M. Harabagiu;Rǎzvan C. Bunescu;Steven J. Maiorano

  • Affiliations:
  • Southern Methodist University, Dallas, TX;Southern Methodist University, Dallas, TX;Southern Methodist University, Dallas, TX

  • Venue:
  • NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
  • Year:
  • 2001

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Abstract

Traditionally coreference is resolved by satisfying a combination of salience, syntactic, semantic and discourse constraints. The acquisition of such knowledge is time-consuming, difficult and error-prone. Therefore, we present a knowledge minimalist methodology of mining coreference rules from annotated text corpora. Semantic consistency evidence, which is a form of knowledge required by coreference, is easily retrieved from WordNet. Additional consistency knowledge is discovered by a meta-bootstrapping algorithm applied to unlabeled texts.