Extracting Entities and Relations with Joint Minimum Risk Training

Changzhi Sun, Yuanbin Wu, Man Lan, Shiliang Sun, Wenting Wang, Kuang-Chih Lee, Kewen Wu


Abstract
We investigate the task of joint entity relation extraction. Unlike prior efforts, we propose a new lightweight joint learning paradigm based on minimum risk training (MRT). Specifically, our algorithm optimizes a global loss function which is flexible and effective to explore interactions between the entity model and the relation model. We implement a strong and simple neural network where the MRT is executed. Experiment results on the benchmark ACE05 and NYT datasets show that our model is able to achieve state-of-the-art joint extraction performances.
Anthology ID:
D18-1249
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2256–2265
Language:
URL:
https://aclanthology.org/D18-1249
DOI:
10.18653/v1/D18-1249
Bibkey:
Cite (ACL):
Changzhi Sun, Yuanbin Wu, Man Lan, Shiliang Sun, Wenting Wang, Kuang-Chih Lee, and Kewen Wu. 2018. Extracting Entities and Relations with Joint Minimum Risk Training. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2256–2265, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Extracting Entities and Relations with Joint Minimum Risk Training (Sun et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1249.pdf
Attachment:
 D18-1249.Attachment.zip
Data
ACE 2005