Revisiting the Negative Data of Distantly Supervised Relation Extraction

Chenhao Xie, Jiaqing Liang, Jingping Liu, Chengsong Huang, Wenhao Huang, Yanghua Xiao


Abstract
Distantly supervision automatically generates plenty of training samples for relation extraction. However, it also incurs two major problems: noisy labels and imbalanced training data. Previous works focus more on reducing wrongly labeled relations (false positives) while few explore the missing relations that are caused by incompleteness of knowledge base (false negatives). Furthermore, the quantity of negative labels overwhelmingly surpasses the positive ones in previous problem formulations. In this paper, we first provide a thorough analysis of the above challenges caused by negative data. Next, we formulate the problem of relation extraction into as a positive unlabeled learning task to alleviate false negative problem. Thirdly, we propose a pipeline approach, dubbed ReRe, that first performs sentence classification with relational labels and then extracts the subjects/objects. Experimental results show that the proposed method consistently outperforms existing approaches and remains excellent performance even learned with a large quantity of false positive samples. Source code is available online at https://github.com/redreamality/RERE-relation-extraction.
Anthology ID:
2021.acl-long.277
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:
3572–3581
Language:
URL:
https://aclanthology.org/2021.acl-long.277
DOI:
10.18653/v1/2021.acl-long.277
Bibkey:
Cite (ACL):
Chenhao Xie, Jiaqing Liang, Jingping Liu, Chengsong Huang, Wenhao Huang, and Yanghua Xiao. 2021. Revisiting the Negative Data of Distantly Supervised Relation 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 3572–3581, Online. Association for Computational Linguistics.
Cite (Informal):
Revisiting the Negative Data of Distantly Supervised Relation Extraction (Xie et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.277.pdf
Video:
 https://aclanthology.org/2021.acl-long.277.mp4
Code
 redreamality/RERE-relation-extraction
Data
NYT11-HRLWebNLG