Using a probabilistic class-based lexicon for lexical ambiguity resolution

  • Authors:
  • Detlef Prescher;Stefan Riezler;Mats Rooth

  • Affiliations:
  • Universität Stuttgart, Germany;Universität Stuttgart, Germany;Universität Stuttgart, Germany

  • Venue:
  • COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
  • Year:
  • 2000

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Abstract

This paper presents the use of probabilistic class-based lexica for disambiguation in target-word selection. Our method employs minimal but precise contextual information for disambiguation. That is, only information provided by the target-verb, enriched by the condensed information of a probabilistic class-based lexicon, is used. Induction of classes and fine-tuning to verbal arguments is done in an unsupervised manner by EM-based clustering techniques. The method shows promising results in an evaluation on real-world translations.