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Towards an entity relation extraction framework in the cross-lingual context

Chuanming Yu (School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China)
Haodong Xue (School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China)
Manyi Wang (School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China)
Lu An (School of Information Management, Wuhan University, Wuhan, China)

The Electronic Library

ISSN: 0264-0473

Article publication date: 3 August 2021

Issue publication date: 4 November 2021




Owing to the uneven distribution of annotated corpus among different languages, it is necessary to bridge the gap between low resource languages and high resource languages. From the perspective of entity relation extraction, this paper aims to extend the knowledge acquisition task from a single language context to a cross-lingual context, and to improve the relation extraction performance for low resource languages.


This paper proposes a cross-lingual adversarial relation extraction (CLARE) framework, which decomposes cross-lingual relation extraction into parallel corpus acquisition and adversarial adaptation relation extraction. Based on the proposed framework, this paper conducts extensive experiments in two tasks, i.e. the English-to-Chinese and the English-to-Arabic cross-lingual entity relation extraction.


The Macro-F1 values of the optimal models in the two tasks are 0.880 1 and 0.789 9, respectively, indicating that the proposed CLARE framework for CLARE can significantly improve the effect of low resource language entity relation extraction. The experimental results suggest that the proposed framework can effectively transfer the corpus as well as the annotated tags from English to Chinese and Arabic. This study reveals that the proposed approach is less human labour intensive and more effective in the cross-lingual entity relation extraction than the manual method. It shows that this approach has high generalizability among different languages.


The research results are of great significance for improving the performance of the cross-lingual knowledge acquisition. The cross-lingual transfer may greatly reduce the time and cost of the manual construction of the multi-lingual corpus. It sheds light on the knowledge acquisition and organization from the unstructured text in the era of big data.



This research was supported by the National Natural Science Foundation of China (Grant No. 71974202, 71921002 and 71790612), the project of the Ministry of Education of China (Grant No. 19YJC870029) and the Fundamental Research Funds for the Central Universities, Zhongnan University of Economics and Law (Grant No. 2722021AJ011).


Yu, C., Xue, H., Wang, M. and An, L. (2021), "Towards an entity relation extraction framework in the cross-lingual context", The Electronic Library, Vol. 39 No. 3, pp. 411-434.



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