Chinese Chunking with Tri-training Learning

  • Wenliang Chen
  • Yujie Zhang
  • Hitoshi Isahara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4285)


This paper presents a practical tri-training method for Chinese chunking using a small amount of labeled training data and a much larger pool of unlabeled data. We propose a novel selection method for tri-training learning in which newly labeled sentences are selected by comparing the agreements of three classifiers. In detail, in each iteration, a new sample is selected for a classifier if the other two classifiers agree on the labels while itself disagrees. We compare the proposed tri-training learning approach with co-training learning approach on Upenn Chinese Treebank V4.0(CTB4). The experimental results show that the proposed approach can improve the performance significantly.


Unlabeled Data Word Sense Disambiguation Label Training Data Agree Method Shallow Parsing 
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.
    Abney, S.P.: Parsing by chunks. In: Berwick, R.C., Abney, S.P., Tenny, C. (eds.) Principle-Based Parsing: Computation and Psycholinguistics, pp. 257–278. Kluwer, Dordrecht (1991)Google Scholar
  2. 2.
    Ramshaw, L., Marcus, M.: Text chunking using transformation-based learning. In: Yarovsky, D., Church, K. (eds.) Proceedings of the Third Workshop on Very Large Corpora, Somerset, New Jersey, Association for Computational Linguistics, pp. 82–94 (1995)Google Scholar
  3. 3.
    Sang, E.F.T.K., Buchholz, S.: Introduction to the conll-2000 shared task: Chunking. In: Proceedings of CoNLL 2000 and LLL 2000, Lisbin, Portugal, pp. 127–132 (2000)Google Scholar
  4. 4.
    Li, H., Webster, J.J., Kit, C., Yao, T.: Transductive hmm based chinese text chunking. In: Proceedings of IEEE NLP-KE2003, Beijing, China, pp. 257–262 (2003)Google Scholar
  5. 5.
    Tan, Y., Yao, T., Chen, Q., Zhu, J.: Applying conditional random fields to chinese shallow parsing. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 167–176. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Wu, S.H., Shih, C.W., Wu, C.W., Tsai, T.H., Hsu, W.L.: Applying maximum entropy to robust chinese shallow parsing. In: Proceedings of ROCLING 2005 (2005)Google Scholar
  7. 7.
    Zhao, T., Yang, M., Liu, F., Yao, J., Yu, H.: Statistics based hybrid approach to chinese base phrase identification. In: Proceedings of Second Chinese Language Processing Workshop (2000)Google Scholar
  8. 8.
    Zhou, Z.H., Li, M.: Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering 17, 1529–1541 (2005)CrossRefGoogle Scholar
  9. 9.
    Chen, W., Zhang, Y., Isahara, H.: An empirical study of chinese chunking. In: Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, Sydney, Australia, Association for Computational Linguistics, pp. 97–104 (2006)Google Scholar
  10. 10.
    Steedman, M., Hwa, R., Clark, S., Osborne, M., Sarkar, A., Hockenmaier, J., Ruhlen, P., Baker, S., Crim, J.: Example selection for bootstrapping statistical parsers. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 157–164 (2003)Google Scholar
  11. 11.
    Pham, T., Ng, H., Lee, W.: Word sense disambiguation with semi-supervised learning. In: AAAI 2005, The Twentieth National Conference on Artificial Intelligence (2005)Google Scholar
  12. 12.
    Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (ACL) (1995)Google Scholar
  13. 13.
    Collins, M., Singer, Y.: Unsupervised models for named entity classification. In: Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 100–110 (1999)Google Scholar
  14. 14.
    Ando, R., Zhang, T.: A high-performance semi-supervised learning method for text chunking. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL) (2005)Google Scholar
  15. 15.
    Steedman, M., Osborne, M., Sarkar, A., Clark, S., Hwa, R., Hockenmaier, J., Ruhlen, P., Baker, S., Crim, J.: Bootstrapping statistical parsers from small datasets. In: The Proceedings of the Annual Meeting of the European Chapter of the ACL, pp. 331–338 (2003)Google Scholar
  16. 16.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on Computational learning theory, pp. 92–100 (1998)Google Scholar
  17. 17.
    Sang, E.F.T.K.: Memory-based shallow parsing. JMLR 2, 559–594 (2002)zbMATHCrossRefGoogle Scholar
  18. 18.
    Sha, F., Pereira, F.: Shallow parsing with conditional random fields. In: Proceedings of HLT-NAACL 2003 (2003)Google Scholar
  19. 19.
    Kudo, T., Matsumoto, Y.: Chunking with support vector machines. In: Proceedings of NAACL 2001 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wenliang Chen
    • 1
    • 2
  • Yujie Zhang
    • 1
  • Hitoshi Isahara
    • 1
  1. 1.Computational Linguistics GroupNational Institute of Information and Communications TechnologyKyotoJapan
  2. 2.Natural Language Processing LabNortheastern UniversityShenyangChina

Personalised recommendations