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Using Convex Combination Kernel Function to Extract Entity Relation in Specific Field

  • Qi Shang
  • Jianyi GuoEmail author
  • Yantuan Xian
  • Zhengtao Yu
  • Yonghua Wen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9998)

Abstract

Kernel method has been proven to be effective in measuring the similarity of two complex relation patterns. Aim at the optimization problem of compound kernel functions, this paper presents a method of finding the optimal convex combination kernel function, which is comprised of multiple kernel functions and needs to be optimized. After preprocessing the corpus and selecting features including lexical information, phrases syntax information and dependency information, the feature matrix was constructed by using these features. The optimal kernel function can be found in the process of mapping the feature matrix to different high-dimensional matrix, and the different classification models can be obtained. The experiments are conducted on the domain dataset from Web and the experimental results show that our approach outperforms state-of-the-art learning models such as ME or Convolution tree kernel.

Keywords

Entity relation extraction Compound kernel functions Optimization Convex combination of kernel functions 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 61262041, 61472168 and 61562052) and the key project of National Natural Science Foundation of Yunnan province (Grant No. 2013FA030).

References

  1. 1.
    Zhao, J., Liu, K., Zhou, G.Y.: Open information extraction. J. Chin. Inf. Process. 25(6), 98–110 (2011)MathSciNetGoogle Scholar
  2. 2.
    Aone, C., Ramos-Santacruz, M.: Rees: A large-scale relation and event extraction system. In: Proceedings of the 6th Applied Natural Language Processing Conference, pp. 76–83. ACM Press, New York (2000)Google Scholar
  3. 3.
    Califf, M.E., Mooney, J.: Bottom-up relational learning of pattern matching rules for information extraction. J. Mach. Learn. Res. 4, 177–210 (2003)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Zhou, G., Su, J., Zhang, J.: Exploring various knowledge in relation extraction. In: ACL, June 2005, pp. 427–434 (2005)Google Scholar
  5. 5.
    Dong, J., Sun, L., Feng, Y.Y.: Chinese automatic entity relation extraction. J, Chin. Inf. Process. 21(4), 80–85 (2007)Google Scholar
  6. 6.
    Ye, F., Shi, H., Wu, S.: Research on pattern representation method in semi-supervised semantic relation extraction based on bootstrapping. In: 2014 Seventh International Symposium on Computational Intelligence and Design (ISCID). IEEE(2014)Google Scholar
  7. 7.
    Komachi, M., Kudo, T., Shimbo, M., Matsumoto, Y.: Graph-based analysis of semantic drift in espresso-like bootstrapping algorithms. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 1011–1020 (2008)Google Scholar
  8. 8.
    Zeng, D., Liu, K., Lai, S.: Relation classification via convolutional deep neural network. In: Proceedings of COLING (2014)Google Scholar
  9. 9.
    Liu, K.B., Li, F., Liu, L., Han, Y.: Implementation of a kernel-based Chinese relation extraction system. J. Comput. Res. Dev. 44(8), 1406–1411 (2007)CrossRefGoogle Scholar
  10. 10.
    Zhuang, C.L., Qian, L.H., Zhou, G.D.: Research on tree kernel-based entity semantic relation extraction. J. Chin. Inf. Process. 23(1), 3–9 (2009). ISSN: 1003-0077Google Scholar
  11. 11.
    Yang, Z., Tang, N., Zhang, X., et al.: Multiple kernel learning in protein–protein interaction extraction from biomedical literature. J. Artif. Intell. Med. 51(3), 163–173 (2011)CrossRefGoogle Scholar
  12. 12.
    Peng, C., Gu, J., Qian, L.: Research on tree kernel-based personal relation extraction. In: Zhou, M., Zhou, G., Zhao, D., Liu, Q., Zou, L. (eds.) NLPCC 2012. CCIS, vol. 333, pp. 225–236. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-34456-5_21 CrossRefGoogle Scholar
  13. 13.
    Arenas-García, J., Martínez-Ramón, M., Gómez-Verdejo, V., Figueiras-Vidal, A.R.: Multiple plant identifier via adaptive LMS convex combination. In: Proceedings of the IEEE International Symposium on Intelligent Signal Processing, Budapest, Hungary, pp. 137–142 (2003)Google Scholar
  14. 14.
    Arenas-García, J., Figueiras-Vidal, A.R., Sayed, A.H.: Mean-square performance of a convex combination of two adaptive filters. IEEE Trans. Signal Process. 54(3), 1078–1090 (2006)CrossRefGoogle Scholar
  15. 15.
    Knowloge- base, CYC. http://www.cyc.com/2008
  16. 16.
    Miller, G.: Introduction to wordnet: an on-line lexical database. Int. J. Lexicograhy 3(4), 235–3244 (1990)CrossRefGoogle Scholar
  17. 17.
    Dong, Z.D., Dong, Q.: National Knowledge Infrastructure (2005)Google Scholar
  18. 18.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: 16th International World Wide Web Conference (WWW2007). ACM Press, New York (2007)Google Scholar
  19. 19.
    ICTCLAS tool from Chinese Academy of Sciences. http://ictclas.nlpir.org/downloads
  20. 20.
    LIBSVM developed by Lin, Z.R from Taiwan University. http://www.csie.ntu.edu.tw/~cjlin/libsvm
  21. 21.

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Qi Shang
    • 1
  • Jianyi Guo
    • 1
    • 2
    Email author
  • Yantuan Xian
    • 1
    • 2
  • Zhengtao Yu
    • 1
  • Yonghua Wen
    • 1
  1. 1.The School of Information Engineering and AutomationKunming University of Science and TechnologyKunmingChina
  2. 2.Computer Technology Application Key Laboratory of Yunnan ProvinceThe Institute of Intelligent Information ProcessingKunmingChina

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