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

Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Xu Ming, Yefeng Zheng


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. The source code has been submitted as the supplementary material and will be made publicly available after the blind review.
Anthology ID:
2021.acl-long.486
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6225–6235
Language:
URL:
https://aclanthology.org/2021.acl-long.486
DOI:
10.18653/v1/2021.acl-long.486
Bibkey:
Cite (ACL):
Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Xu Ming, and Yefeng Zheng. 2021. PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 6225–6235, Online. Association for Computational Linguistics.
Cite (Informal):
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction (Zheng et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.486.pdf
Optional supplementary material:
 2021.acl-long.486.OptionalSupplementaryMaterial.zip
Video:
 https://aclanthology.org/2021.acl-long.486.mp4
Code
 hy-struggle/PRGC
Data
WebNLG