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.
@article{arxiv.2106.09895,
title = {PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction},
author = {Hengyi Zheng and Rui Wen and Xi Chen and Yifan Yang and Yunyan Zhang and Ziheng Zhang and Ningyu Zhang and Bin Qin and Ming Xu and Yefeng Zheng},
journal= {arXiv preprint arXiv:2106.09895},
year = {2021}
}