Metric learning for synonym acquisition

  • Authors:
  • Nobuyuki Shimizu;Masato Hagiwara;Yasuhiro Ogawa;Katsuhiko Toyama;Hiroshi Nakagawa

  • Affiliations:
  • University of Tokyo;Nagoya University;Nagoya University;Nagoya University;University of Tokyo

  • Venue:
  • COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
  • Year:
  • 2008

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Abstract

The distance or similarity metric plays an important role in many natural language processing (NLP) tasks. Previous studies have demonstrated the effectiveness of a number of metrics such as the Jaccard coefficient, especially in synonym acquisition. While the existing metrics perform quite well, to further improve performance, we propose the use of a supervised machine learning algorithm that fine-tunes them. Given the known instances of similar or dissimilar words, we estimated the parameters of the Mahalanobis distance. We compared a number of metrics in our experiments, and the results show that the proposed metric has a higher mean average precision than other metrics.