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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)

Abstract

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.

Keywords

Relation extraction Weakly supervised SVM Baidu Encyclopedia 

Notes

Acknowledgments

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.

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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

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