Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction

Ningyu Zhang, Shumin Deng, Zhanling Sun, Xi Chen, Wei Zhang, Huajun Chen


Abstract
A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms. We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations. Particularly, we show that capsule networks improve multiple entity pairs relation extraction.
Anthology ID:
D18-1120
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:
986–992
Language:
URL:
https://aclanthology.org/D18-1120
DOI:
10.18653/v1/D18-1120
Bibkey:
Cite (ACL):
Ningyu Zhang, Shumin Deng, Zhanling Sun, Xi Chen, Wei Zhang, and Huajun Chen. 2018. Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 986–992, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction (Zhang et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1120.pdf
Attachment:
 D18-1120.Attachment.zip
Code
 zjunlp/deepke