Enhancing Chinese Word Segmentation with Character Clustering

  • Yijia Liu
  • Wanxiang Che
  • Ting Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8202)

Abstract

In semi-supervised learning framework, clustering has been proved a helpful feature to improve system performance in NER and other NLP tasks. However, there hasn’t been any work that employs clustering in word segmentation. In this paper, we proposed a new approach to compute clusters of characters and use these results to assist a character based Chinese word segmentation system. Contextual information is considered when we perform character clustering algorithm to address character ambiguity. Experiments show our character clusters result in performance improvement. Also, we compare our clusters features with widely used mutual information (MI). When two features integrated, further improvement is achieved.

Keywords

Brown clustering Chinese word segmentation semi-supervised learning 

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References

  1. 1.
    Wang, Y., Kazama, J., Tsuruoka, Y., Chen, W., Zhang, Y., Torisawa, K.: Improving Chinese word segmentation and pos tagging with semi-supervised methods using large auto-analyzed data. In: Proceedings of the Fifth International Joint Conference on Natural Language Processing, IJCNLP 2011 (2011)Google Scholar
  2. 2.
    Sun, W., Xu, J.: Enhancing chinese word segmentation using unlabeled data. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 970–979. Association for Computational Linguistics (2011)Google Scholar
  3. 3.
    Miller, S., Guinness, J., Zamanian, A.: Name tagging with word clusters and discriminative training. In: Proceedings of HLT-NAACL, vol. 4. Citeseer (2004)Google Scholar
  4. 4.
    Liang, P.: Semi-supervised learning for natural language. PhD thesis, Massachusetts Institute of Technology (2005)Google Scholar
  5. 5.
    Brown, P.F., Desouza, P.V., Mercer, R.L., Pietra, V.J.D., Lai, J.C.: Class-based n-gram models of natural language. Computational Linguistics 18(4), 467–479 (1992)Google Scholar
  6. 6.
    Chen, W., Kazama, J., Uchimoto, K., Torisawa, K.: Improving dependency parsing with subtrees from auto-parsed data. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 2, pp. 570–579. Association for Computational Linguistics (2009)Google Scholar
  7. 7.
    Okazaki, N.: Crfsuite: a fast implementation of conditional random fields (crfs) (2007)Google Scholar
  8. 8.
    Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 384–394. Association for Computational Linguistics (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yijia Liu
    • 1
  • Wanxiang Che
    • 1
  • Ting Liu
    • 1
  1. 1.Research Center for Social Computing and Information Retrieval School of Computer Science and TechnologyHarbin Institute of TechnologyChina

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