Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

Suncong Zheng, Feng Wang, Hongyun Bao, Yuexing Hao, Peng Zhou, Bo Xu


Abstract
Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem.. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What’s more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.
Anthology ID:
P17-1113
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1227–1236
Language:
URL:
https://aclanthology.org/P17-1113
DOI:
10.18653/v1/P17-1113
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/P17-1113.pdf
Video:
 https://vimeo.com/234945423
Code
 additional community code
Data
WebNLG