Leveraging Chinese Encyclopedia for Weakly Supervised Relation Extraction

  • Xiyue Guo
  • Tingting HeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9544)


In the research of named-entity relation extraction based on supervision, selecting relation features for traditional methods are usually finished by people, and it’s hard to implement these methods for large-scale corpus. On the other hand, fixing relation types is the premise, so the practicabilities of these methods are not so ideal. This paper presents a weakly supervised method for Chinese named-entity relation extraction without man-made annotations, and the relation types in this method are not chosen artificially. The method collects entity relation types from the structured knowledge in encyclopedia pages, and then automatically annotates the relation instances existing in the texts based on these relation types. Simultaneously, the syntactic and semantic features of entity relations will be considered in this method, then the machine learning data will be completed, finally we use Support Vector Machine (SVM) model to train relation classifiers from training data, and these classifiers could try to extract entity relations from testing data. We carry out the experiment with the data from Chinese Baidu Encyclopedia pages, and the results show the effectiveness of this method, the overall F1 value reaches to 83.12 %. In order to probe the universality of this method, we also use the acquired relation classifiers to extract entity relations from news texts, and the results manifest that this method owns certain universality.


Relation extraction Weakly supervised SVM Baidu Encyclopedia 



We are very indebted to the reviewers who reviewed the papers very carefully. This work was supported by the major project of national social science fund (No. 12 & 2D223), the international cooperation project of Hubei Province (No. 2014BHE0017) and the self-determined research funds of CCNU from the colleges’ basic research and operation of MOE (CCNU15ZD003). The authors wish to thank Guangyou Zhou, Xinhui Tu for improving the research idea, Fanghong Jian, Jie Yuan and Peng Mo for providing help in experiments and text-proofing.


  1. 1.
    Zelenko, D., Aone, C., et al.: Kernel methods for relation extraction. J. Mach. Learn. Res. 3, 1083–1106 (2003)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the AC, pp. 1003–1011 (2009)Google Scholar
  3. 3.
    Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 1535–1545 (2011)Google Scholar
  4. 4.
    Thomas, P., Neves, M., et al.: Relation extraction for drug-drug interactions using ensemble learning. In: Drug-Drug Interaction Extraction, Huelva, Spain, pp. 11–18 (2011)Google Scholar
  5. 5.
    Surdeanu, M., Tibshirani, J., et al.: Multi-instance multi-label learning for relation extraction. In: Conference on Empirical Methods in Natural Language Processing and Natural Language Learning, Jeju Island, Korea, pp. 455–465 (2012)Google Scholar
  6. 6.
    Li, H., Wu, X., et al.: A relation extraction method of Chinese named entities based on location and semantic features. Appl. Intell. 38(1), 1–15 (2013)CrossRefGoogle Scholar
  7. 7.
    He, T., Xu, C., et al.: Named-entity relation extraction method based on seed self-expansion. Comput. Eng. 32(21), 183–184, 193 (2006)Google Scholar
  8. 8.
    Xu, F., Wang, T., Chen, H., et al.: SVM-based Chinese entity relation extraction. In: 9th Chinese National Conference on Computational Linguistics, Dalian, China, pp. 497–502 (2007)Google Scholar
  9. 9.
    Chen, P., Guo, J., et al.: Chinese field entity relation extraction based on convex combination kernel function. J. Chin. Inf. Process. 27(5), 144–148 (2013)Google Scholar
  10. 10.
    Zeng, D., Zhao, J., et al.: Open entity attribute-value extraction from unstructured text. J. Jiangxi Norm. Univ. (Nat. Sci. Ed.) 37(3), 279–283 (2013)Google Scholar
  11. 11.
    Guo, X., He, T., et al.: Chinese named-entity relation extraction based on the syntactic and semantic features. J. Chin. Inf. Process. 28(6), 183–189 (2014)Google Scholar
  12. 12.
    Zhang, H., Huang, M., Zhu, X.: A unified active learning framework for biomedical relation extraction. J. Comput. Sci. Technol. 27(6), 1302–1313 (2012)CrossRefGoogle Scholar
  13. 13.
    Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods. MIT Press, pp. 185–208 (1999)Google Scholar
  14. 14.
    Xianyi, C., Qian, Z.: A study of relation extraction of undefined relation type based on semi-supervised learning framework. J. Nanjing Univ. Nat. Sci. 48(4), 466–474 (2012)Google Scholar
  15. 15.
    Wang, H., Qi, Z., Hao, H., Xu, B.: A hybrid method for chinese entity relation extraction. In: Zong, C., Nie, J.-Y., Zhao, D., Feng, Y. (eds.) NLPCC 2014. CCIS, vol. 496, pp. 357–367. Springer, Heidelberg (2014)Google Scholar
  16. 16.
    Zhen, J., Dake, H.E., et al.: Relation extraction from Chinese online encyclopedia based on weakly supervised learning. CAAI Trans. Intell. Syst. 10(1), 113–119 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.National Engineering Research Center for E-learningCentral China Normal UniversityWuhanChina
  2. 2.School of ComputerCentral China Normal UniversityWuhanChina
  3. 3.School of Information TechnologyXingyi Normal University for NationalitiesXingyiChina

Personalised recommendations