A machine learning approach to pronoun resolution in spoken dialogue

  • Authors:
  • Michael Strube;Christoph Müller

  • Affiliations:
  • European Media Laboratory GmbH, Heidelberg, Germany;European Media Laboratory GmbH, Heidelberg, Germany

  • Venue:
  • ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
  • Year:
  • 2003

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Abstract

We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NP-and non-NP-antecedents. We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron's (2002) manually tuned system.