Advertisement

A Novel Composite Kernel Approach to Chinese Entity Relation Extraction

  • Ji Zhang
  • You Ouyang
  • Wenjie Li
  • Yuexian Hou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5459)

Abstract

Relation extraction is the task of finding semantic relations between two entities from the text. In this paper, we propose a novel composite kernel for Chinese relation extraction. The composite kernel is defined as the combination of two independent kernels. One is the entity kernel built upon the non-content-related features. The other is the string semantic similarity kernel concerning the content information. Three combinations, namely linear combination, semi-polynomial combination and polynomial combination are investigated. When evaluated on the ACE 2005 Chinese data set, the results show that the proposed approach is effective.

Keywords

Kernel-based Chinese Relation Extraction Composite Kernel Entity Kernel String Semantic Similarity Kernel 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Miller, S., Fox, H., Ramshaw, L., Weischedel, R.: A novel use of statistical parsing to extract information from text. In: NAACL 2000 (2000)Google Scholar
  2. 2.
    Kambhatla, N.: Combining lexical, syntactic and semantic features with Maximun Entropy models for extracting relations. In: ACL 2004 (2004)Google Scholar
  3. 3.
    Zhao, S.B., Grishman, R.: Extracting relations with integrated information using kernel kernel methods. In: ACL 2005, Univ of Michigan-Ann Arbor USA, 25-30 June 2005, pp. 419–426 (2005)Google Scholar
  4. 4.
    Zhou, G.D., Su, J., Zhang, J., Zhang, M.: Exploring Various Knowledge in Relation Extraction. In: ACL 2005 (2005)Google Scholar
  5. 5.
    Jiang, J., Zhai, C.: A Systematic Exploration of the Feature Space for Relation Extraction. In: Proceedings of NAACL/HLT, pp. 113–120 (2007)Google Scholar
  6. 6.
    Zelenko, D., Aone, C., Richardella, A.: Kernel Methods for Relation Extraction. Journal of Machine Learning Research 2003(2), 1083–1106 (2003)Google Scholar
  7. 7.
    Culotta, A., Sorensen, J.: Dependency Tree Kernel for Relation Extraction. In: ACL 2004 (2004)Google Scholar
  8. 8.
    Bunescu, R., Mooney, R.: A shortest Path Dependency Tree kernel for Relation Extraction. In: Proceedings of HLT/EMNLP, pp. 724–731 (2005)Google Scholar
  9. 9.
    Zhang, M., Zhang, J., Su, J., Zhou, G.D.: A Composite Kernel to Extract Relations between Entities with both Flat and Structured Features. In: COLING-ACL 2006, Sydney, Australia, pp. 825–832 (2006)Google Scholar
  10. 10.
    Zhou, G., Zhang, M., Ji, D., Zhu, Q.: Tree Kernel-based Relation Extraction with Context-Sensitive Structured Parse Tree Information. In: Proceedings of EMNLP, pp. 728–736 (2007)Google Scholar
  11. 11.
    Zhang, P., Li, W.J., Wei, F.R., Lu, Q., Hou, Y.X.: Exploiting the Role of Position Feature in Chinese Relation Extraction. In: Proceedings of the 6th International Conference on Language Resources and Evaluation (LREC) (to appear, 2008)Google Scholar
  12. 12.
    Li, W.J., Zhang, P., Wei, F.R., Hou, Y.X., Lu, Q.: A Novel Feature-based Approach to Chinese Entity Relation Extraction. In: Proceeding of ACL 2008: HLT, Short Papers (Companion Volume), Columbus, Ohio, USA, pp. 89–92 (June 2008)Google Scholar
  13. 13.
    Che, W.X.: Improved-Edit-Distance Kernel for Chinese Relation Extraction. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS(LNAI), vol. 2651. Springer, Heidelberg (2005)Google Scholar
  14. 14.
    Huang, R.H., Sun, L., Feng, Y.Y.: Study of kernel-based methods for chinese relation extraction. In: Li, H., Liu, T., Ma, W.-Y., Sakai, T., Wong, K.-F., Zhou, G. (eds.) AIRS 2008. LNCS, vol. 4993, pp. 598–604. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Islam, A., Inkpen, D.: Semantic Text Similarity Using Corpus-Based Word Similarity and String Similarity. ACM Transactions on Knowledge Discovery from Data 2(2), Article 10 (July 2008)Google Scholar
  16. 16.
    Turney, P.: Mining the web for synonyms: PMI-IR versus LSA on TOEFL. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS, vol. 2167, p. 491. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  17. 17.
    Joachims, T.: Text categorization with Support Vector Machines: Learning with many relevant features. In: Proceedings of European Conference on Machine Learning (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ji Zhang
    • 1
    • 2
  • You Ouyang
    • 1
  • Wenjie Li
    • 1
  • Yuexian Hou
    • 2
  1. 1.Department of ComputingThe Hong Kong Polytechnic UniversityHong Kong
  2. 2.School of Computer Science and TechnologyTianjin UniversityChina

Personalised recommendations