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2020 , Vol. 64 >Issue 17: 131 - 144

DOI: https://doi.org/10.13266/j.issn.0252-3116.2020.17.014

Research on the Model of Adversarial Entity Relation Extraction in Cross-Lingual Context

  • Yu Chuanming ,
  • Wang Manyi ,
  • An Lu
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  • 1 School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073;
    2 School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073;
    3 School of Information Management, Wuhan University, Wuhan 430072

Received date: 2020-02-08

  Revised date: 2020-04-22

  Online published: 2020-09-05

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Abstract

[Purpose/significance] From the perspective of entity relation extraction, the knowledge acquisition task in a single language context is extended to a cross-language context, and the relation extraction effect of low-resource languages is improved.[Method/process] This paper proposed a Cross-Lingual Adversarial Relation Extraction (CLARE) framework, which decomposed cross-lingual relation extraction into parallel corpus acquisition and adversarial adaptation relation extraction. Through dictionary expansion or self-learning methods, the source language relation extraction data set was converted into the target language data set. On this basis, the feature representation of the source language was transferred to the target language using adversarial feature adaptation, and then the target language relation extraction network obtained by training was used to classify the target language.[Result/conclusion] The method in this paper is applied to the English-Chinese and Chinese-English cross-lingual relation extraction task based on the ACE2005 multilingual dataset. The Macro-F1 values of the optimal models on the two tasks are 0.880 1 and 0.842 2 respectively, indicating that the proposed CLARE framework for cross-language adversarial relation extraction can significantly improve the effect of low-resource language entity relation extraction. The research results are of great significance for improving the relation extraction model in the cross-lingual context and promoting the application of entity relation extraction research in the field of information science.

Key words： cross-lingual information extraction; entity relation extraction; deep learning; generative adversarial network

Cite this article

Yu Chuanming , Wang Manyi , An Lu . Research on the Model of Adversarial Entity Relation Extraction in Cross-Lingual Context[J]. Library and Information Service, 2020 , 64(17) : 131 -144 . DOI: 10.13266/j.issn.0252-3116.2020.17.014

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