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Word Clustering for Collocation-Based Word Sense Disambiguation

  • Peng Jin
  • Xu Sun
  • Yunfang Wu
  • Shiwen Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4394)

Abstract

The main disadvantage of collocation-based word sense disambiguation is that the recall is low, with relatively high precision. How to improve the recall without decrease the precision? In this paper, we investigate a word-class approach to extend the collocation list which is constructed from the manually sense-tagged corpus. But the word classes are obtained from a larger scale corpus which is not sense tagged. The experiment results have shown that the F-measure is improved to 71% compared to 54% of the baseline system where the word-class is not considered, although the precision decreases slightly. Further study discovers the relationship between the F-measure and the number of word-class trained from the various sizes of corpus.

Keywords

Target Word Machine Translation Word Sense Word Sense Disambiguation Baseline System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Peng Jin
    • 1
  • Xu Sun
    • 1
  • Yunfang Wu
    • 1
  • Shiwen Yu
    • 1
  1. 1.Department of Computer Science and Technology, Institute of Computational Linguistics, Peking University, 100871, BeijingChina

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