PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction

• URL: http://arxiv.org/abs/2106.09895v1
• Date: Fri, 18 Jun 2021 03:38:07 GMT
• Title: PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
• Authors: Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Ming Xu, Yefeng Zheng
• Abstract summary: We propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC) PRGC achieves state-of-the-art performance on public benchmarks with higher efficiency and delivers consistent performance gain on complex scenarios of overlapping triples.
• Score: 23.998135821388203
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer from some inherent limitations, such as redundancy of relation prediction, poor generalization of span-based extraction and inefficiency. In this paper, we decompose this task into three subtasks, Relation Judgement, Entity Extraction and Subject-object Alignment from a novel perspective and then propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC). Specifically, we design a component to predict potential relations, which constrains the following entity extraction to the predicted relation subset rather than all relations; then a relation-specific sequence tagging component is applied to handle the overlapping problem between subjects and objects; finally, a global correspondence component is designed to align the subject and object into a triple with low-complexity. Extensive experiments show that PRGC achieves state-of-the-art performance on public benchmarks with higher efficiency and delivers consistent performance gain on complex scenarios of overlapping triples.

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