Jointly Extracting Relations with Class Ties via Effective Deep Ranking

Hai Ye, Wenhan Chao, Zhunchen Luo, Zhoujun Li


Abstract
Connections between relations in relation extraction, which we call class ties, are common. In distantly supervised scenario, one entity tuple may have multiple relation facts. Exploiting class ties between relations of one entity tuple will be promising for distantly supervised relation extraction. However, previous models are not effective or ignore to model this property. In this work, to effectively leverage class ties, we propose to make joint relation extraction with a unified model that integrates convolutional neural network (CNN) with a general pairwise ranking framework, in which three novel ranking loss functions are introduced. Additionally, an effective method is presented to relieve the severe class imbalance problem from NR (not relation) for model training. Experiments on a widely used dataset show that leveraging class ties will enhance extraction and demonstrate the effectiveness of our model to learn class ties. Our model outperforms the baselines significantly, achieving state-of-the-art performance.
Anthology ID:
P17-1166
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1810–1820
Language:
URL:
https://aclanthology.org/P17-1166
DOI:
10.18653/v1/P17-1166
Bibkey:
Cite (ACL):
Hai Ye, Wenhan Chao, Zhunchen Luo, and Zhoujun Li. 2017. Jointly Extracting Relations with Class Ties via Effective Deep Ranking. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1810–1820, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Jointly Extracting Relations with Class Ties via Effective Deep Ranking (Ye et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1166.pdf
Code
 oceanypt/DR_RE