Chinese Named Entity Recognition Based on Multilevel Linguistic Features

  • Honglei Guo
  • Jianmin Jiang
  • Gang Hu
  • Tong Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3248)


This paper presents a Chinese named entity recognition system that employs the Robust Risk Minimization (RRM) classification method and incorporates the advantages of character-based and word-based models. From experiments on a large-scale corpus, we show that significant performance enhancements can be obtained by integrating various linguistic information (such as Chinese word segmentation, semantic types, part of speech, and named entity triggers) into a basic Chinese character based model. A novel feature weighting mechanism is also employed to obtain more useful cues from most important linguistic features. Moreover, to overcome the limitation of computational resources in building a high-quality named entity recognition system from a large-scale corpus, informative samples are selected by an active learning approach.


Chinese Character Linguistic Feature Chinese Word Name Entity Recognition Entity Recognition 
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.
    Sang, E.F.T.K., Meulder, F.D.: Introduction to the CoNLL-2003 shared task: Language independent named entity recognition. In: Daelemans, W., Osborne, M. (eds.) Proceedings of CoNLL 2003, pp. 142–147 (2003)Google Scholar
  2. 2.
    Borthwick, A.: A Maximum Entropy Approach to Named Entity Recognition. New York University (1999)Google Scholar
  3. 3.
    Klein, D., Smarr, J., Nguyen, H., Manning, C.D.: Named entity recognition with character-level models. In: Proceedings of CoNLL 2003, pp. 180–183 (2003)Google Scholar
  4. 4.
    Bikel, D.M., Schwartz, R.L., Weischedel, R.M.: An algorithm that learns what’s in a name. Machine Learning 34, 211–231 (1999)zbMATHCrossRefGoogle Scholar
  5. 5.
    Carreras, X., Màrquez, L., Padró, L.: A simple named entity extractor using adaboost. In: Proceedings of CoNLL 2003, pp. 152–155 (2003)Google Scholar
  6. 6.
    Meulder, F.D., Daelemans, W.: Memory-based named entity recognition using unannotated data. In: Proceedings of CoNLL 2003, pp. 208–211 (2003)Google Scholar
  7. 7.
    Isozaki, H., Kazawa, H.: Efficient support vector classifiers for named entity recognition. In: Proceedings of Coling 2002 (2002)Google Scholar
  8. 8.
    Zhang, T., Johnson, D.E.: A Robust Risk Minimization based Named Entity Recognition System. In: Proceedings CoNLL 2003, pp. 204–207 (2003)Google Scholar
  9. 9.
    Yu, S., Bai, S., Wu, P.: Description of the kent ridge digital labs system used for muc-7. In: Proceedings of the Seventh Message Understanding Conference, MUC-7 (1998)Google Scholar
  10. 10.
    Jing, H., Florian, R., Luo, X., Zhang, T., Ittycheriah, A.: Howtogetachinesename (entity): Segmentation and combination issues. In: EMNLP 2003 (2003)Google Scholar
  11. 11.
    Sun, J., Gao, J., Zhang, L., Zhou, M., Huang, C.: Chinese named entity identification using class-based language model. In: Proceedings of Coling 2002 (2002)Google Scholar
  12. 12.
    Jiang, J., Guo, H., Hu, G., Zhang, T.: Chinese named entity recognition by regularized winnow algorithm. In: Proceedings of 20th International Conference on Computer Processing of Oriental Languages, pp. 50–56 (2003)Google Scholar
  13. 13.
    Zhang, T., Damerau, F., Johnson, D.E.: Text chunking based on a generalization of Winnow. Journal of Machine Learning Research 2, 615–637 (2002)zbMATHCrossRefGoogle Scholar
  14. 14.
    Lewis, D., Catlett, J.: Heterogeneous uncertainty sampling for supervised learning. In: Proceedings of the Eleventh International Conference on Machine Learning, pp. 148–156 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Honglei Guo
    • 1
  • Jianmin Jiang
    • 1
  • Gang Hu
    • 1
  • Tong Zhang
    • 2
  1. 1.IBM China Research LaboratoryBeijingP.R. China
  2. 2.IBM T.J. Watson Research CenterYorktown HeightsUSA

Personalised recommendations