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| Joint Extraction of Entities and Relations Based on CWHC-AM |
| LI Hongyu1, DUAN Liguo1,2, HOU Chenlei1, YAO Longfei1 |
1.College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi 030024, China;
2.Transfer Prepatory Office, Shanxi Normal University College of Modern Art and Sciences, Linfen, Shanxi 041000, China |
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Abstract Entity and relationship extraction is to extract triples from unstructured natural language text. The existing pipeline method extracts entities first and then relations, without capturing the internal relations and dependencies of the two subtasks. This article proposes a joint extraction model of entities and relations based on CWHC-AM, regarding the multiple relationship issue in extraction as multiple binary relationship tasks. The multi-layer pointer network labeling scheme is adopted to transform the joint extraction task of entities and relations into a sequence labeling problem. And the adversarial training is introduced to improve the robustness of the model. Experiments on the Baidu DuIE2.0 Chinese dataset show that the method in this article can effectively extract multiple relations and binary relations at the same time with better results than the baseline model.
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Received: 16 July 2021
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2022, Vol. 36 
