Word sense disambiguation using label propagation based semi-supervised learning

  • Authors:
  • Zheng-Yu Niu;Dong-Hong Ji;Chew Lim Tan

  • Affiliations:
  • Institute for Infocomm Research, Singapore;Institute for Infocomm Research, Singapore;National University of Singapore, Singapore

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

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Abstract

Shortage of manually sense-tagged data is an obstacle to supervised word sense disambiguation methods. In this paper we investigate a label propagation based semi-supervised learning algorithm for WSD, which combines labeled and unlabeled data in learning process to fully realize a global consistency assumption: similar examples should have similar labels. Our experimental results on benchmark corpora indicate that it consistently outperforms SVM when only very few labeled examples are available, and its performance is also better than monolingual bootstrapping, and comparable to bilingual bootstrapping.