PKUNEI – A Knowledge–Based Approach for Chinese Product Named Entity Semantic Identification

  • Wenyuan Yu
  • Cheng Wang
  • Wenxin Li
  • Zhuoqun Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5459)

Abstract

We present the idea of Named Entity Semantic Identification that is identifying the named entity in a knowledge base and give a definition of this idea. Then we introduced PKUNEI - an approach for Chinese product named entity semantic identification. This approach divided the whole process into 2 separate phases: a role-model based NER phase and a query-driven semantic identification phase. We describe the model of NER phase, the automatically building of knowledge base and the implementation of semantic identification phase. The experimental results demonstrate that our approach is effective for the semantic identification task.

Keywords

named entity semantic identification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pierre, J.M.: Mining Knowledge from Text Collections Using Automatically Generated Metadata. In: Karagiannis, D., Reimer, U. (eds.) PAKM 2002. LNCS, vol. 2569, pp. 537–548. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    Bick, E.: A Named Entity Recognizer for Danish. In: Lino, et al. (eds.) Proc. of 4th International Conf. on Language Resources and Evaluation (LREC 2004), Lisbon, pp. 305–308 (2004)Google Scholar
  3. 3.
    Niu, C., Li, W., Ding, J.h., Srihari, R.K.: A Bootstrapping Approach to Named Entity Classification Using Successive Learners. In: Proceedings of the 41st ACL, Sapporo, Japan, pp. 335–342 (2003)Google Scholar
  4. 4.
    Liu, F., Liu, J., Lv, B., Xu, B., Yu, H.: Product Named Entity Recognition Based on Hierarchical Hidden Markov Model. In: Proceedings of Forurth SIGHAN Workshop on Chinese Language Processing, pp. 40–47. Juje, Korea (2005)Google Scholar
  5. 5.
    Bikel, D.M., Miller, S., Schwartz, R., Weischedel, R.: Nymble.: a High-Performance Learning Name-finder. In: Fifth Conference on Applied Natural Language Processing, pp. 194–201 (1997)Google Scholar
  6. 6.
    Isozaki, H., Kazawa, H.: Efficient supportvector classifiers for named entity recognition. In: Proceedings of the COLING 2002, pp. 390–396 (2002)Google Scholar
  7. 7.
    Paliouras, G., Karkaletsis, V., Petasis, G., Spyropoulos, C.D.: Learning decision trees for named-entity recognition and classification. In: Proceedings of the 14th European Conference on Artificial Intelligence, Berlin, Germany (2000)Google Scholar
  8. 8.
    Sun, J., Zhou, M., Gao, J.: A Class-based Language Model Approach to Chinese Named Entity Identification. Computational Linguistics and Chinese Language Processing 8(2), 1–28 (2003)Google Scholar
  9. 9.
    Zhang, H., Liu, Q., Yu, H., Cheng, X., Bai, S.: Chinese Named Entity Recognition Using Role Model. Special issue Word Formation and Chinese Language processing of the International Journal of Computational Linguistics and Chinese Language Processing 8(2), 29–60 (2003)Google Scholar
  10. 10.
    Zhao, J., Liu, F.: Product named entity recognition in Chinese text. Lang. Resources & Evaluation 42, 197–217 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Wenyuan Yu
    • 1
  • Cheng Wang
    • 1
  • Wenxin Li
    • 1
  • Zhuoqun Xu
    • 1
  1. 1.Key Laboratory of Machine PerceptionPeking UniversityBeijingChina

Personalised recommendations