Online large-margin training of dependency parsers

  • Authors:
  • Ryan McDonald;Koby Crammer;Fernando Pereira

  • Affiliations:
  • University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA

  • Venue:
  • ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2005

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Abstract

We present an effective training algorithm for linearly-scored dependency parsers that implements online large-margin multi-class training (Crammer and Singer, 2003; Crammer et al., 2003) on top of efficient parsing techniques for dependency trees (Eisner, 1996). The trained parsers achieve a competitive dependency accuracy for both English and Czech with no language specific enhancements.