Recurrent Update Representation Based on Multi-head Attention Mechanism for Joint Entity and Relation Extraction

Shengjie Jia, Jikun Dong, Kaifang Long, Jiran Zhu, Hongyun Du, Guijuan Zhang, Hui Yu, Weizhi Xu

Abstract: Joint extraction of entities and relations from unstructured text is an important task in information extraction and knowledge graph construction. However, most of the existing work only considers the information of the context in the sentence and the information of the entities, with little attention to the information of the possible relations between the entities, which may lead to the failure to extract valid triplets. In this paper, we propose a recurrent update representational method based on multi-head attention mechanism for relation extraction. We use a multi-head attention mechanism to interact the information between the relational representation and the sentence context representation, and make the feature information of both fully integrated by cyclically updating the representation. The model performs relation extraction after the representation is updated. Using this approach we are able to leverage the relationship information between entities for relational triple extraction. Our experimental results on four public datasets show that our approach is effective and the model outperforms all baseline models.