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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brown, P.F., Pietra, V.J., de Souza, P.V., Lai, J.C., Mercer, R.L.: Class-based N-gram Models of Natural Language. Computational Linguistics 4, 467–479 (1992)Google Scholar
  2. 2.
    Chao, G., Dyer, G.M.: Maximum Entropy Models for Word Sense Disambiguation. In: Proceedings of the 19th International Conference on Computational Linguistics, Taipei, Taiwan, pp. 155–161 (2002)Google Scholar
  3. 3.
    Dagan, D., Itai, A.: Word Sense Disambiguation Using a Second Language Monolingual Corpus. Computational Linguistics. 4, 563–596 (1994)Google Scholar
  4. 4.
    Dang, H.T., Chia, C., Palmer, M., Chiou, F.D., Rosenzweig, J.: Simple Features for Chinese Word Sense Disambiguation. In: Proceedings of the 19th International Conference on Computational Linguistics, Taipei, Taiwan, pp. 204–211 (2002)Google Scholar
  5. 5.
    Gelbukh, A., Sidorov, G., Han, S.-Y., Hernández-Rubio, E.: Automatic Enrichment of a Very Large Dictionary of Word Combinations on the Basis of Dependency Formalism. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, pp. 430–437. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Kim, S.B., Seo, H.C., Rim, H.C.: Information Retrieval Using Word Senses: Root Sense Tagging Approach. In: SIGIR’04, Sheffield, South Yorkshire, UK, pp. 258–265 (2004)Google Scholar
  7. 7.
    Lee, H.A., Kim, G.C.: Translation Selection through Source Word Sense Disambiguation and Target Word Selection. In: Proceedings of the 19th International, Taipei, Taiwan (2002)Google Scholar
  8. 8.
    Lee, Y.K., Ng, H.T., Chia, T.K.: Supervised Word Sense Disambiguation with Support Vector Machines and Multiple Knowledge Sources. In: Proceedings of SENSEVAL-3: Third International Workshop on the Evaluating Systems for the Semantic Analysis of Text, Barcelona, Spain (2004)Google Scholar
  9. 9.
    Li, H.: Word Clustering and Disambiguation Based on Co-occurrence Data. Natural Language Engineering 8, 25–42 (2002)CrossRefGoogle Scholar
  10. 10.
    Li, W.Y., Lu, Q., Li, W.J.: Integrating Collocation Features in Chinese Word Sense Disambiguation. In: Proceeding of the Fourth SIGHAN Workshop on Chinese Language Processing, pp. 87–94 (2005)Google Scholar
  11. 11.
    Martin, S., Liermann, J., Ney, K.: Algorithms for Bigram and Trigram Word Clustering. Speech Communication 1, 19–37 (1998)CrossRefGoogle Scholar
  12. 12.
    Och, F.J.: An Efficient Method for Determining Bilingual Word Classes. In: Proceeding of the Ninth Conference of the European Chapter of the Association for Computational Linguistics, pp. 71–76 (1999)Google Scholar
  13. 13.
    Pedersen, T.A: Simple Approach to Building Ensembles of Naive Bayesian Classifiers for Word Sense Disambiguation. In: Proceeding of the first Annul Meeting of the North American Chapter for Computational Linguistics, pp. 63–69 (2000)Google Scholar
  14. 14.
    Stokoe, C., Oakes, M.P., Tait, J.: Word Sense Disambiguation in Information Retrieval Revisited. In: Proceeding of the 26th annual International ACM SIGIR conference On research and development in Information retrieval, ACM Press, New York (2003)Google Scholar
  15. 15.
    Yarowsky, D.: One Sense Per Collocation. In: Proceeding of ARPA Human Language Technology workshop, Princeton, New Jersey (1993)Google Scholar
  16. 16.
    Yarowsky, D.: Hierarchical Decision Lists for Word Sense Disambiguation, Computers and the Humanities. Computers and the Humanities 1, 179–186 (2000)CrossRefGoogle Scholar

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

Personalised recommendations