Joint Type Inference on Entities and Relations via Graph Convolutional Networks

Changzhi Sun, Yeyun Gong, Yuanbin Wu, Ming Gong, Daxin Jiang, Man Lan, Shiliang Sun, Nan Duan


Abstract
We develop a new paradigm for the task of joint entity relation extraction. It first identifies entity spans, then performs a joint inference on entity types and relation types. To tackle the joint type inference task, we propose a novel graph convolutional network (GCN) running on an entity-relation bipartite graph. By introducing a binary relation classification task, we are able to utilize the structure of entity-relation bipartite graph in a more efficient and interpretable way. Experiments on ACE05 show that our model outperforms existing joint models in entity performance and is competitive with the state-of-the-art in relation performance.
Anthology ID:
P19-1131
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1361–1370
Language:
URL:
https://aclanthology.org/P19-1131
DOI:
10.18653/v1/P19-1131
Bibkey:
Cite (ACL):
Changzhi Sun, Yeyun Gong, Yuanbin Wu, Ming Gong, Daxin Jiang, Man Lan, Shiliang Sun, and Nan Duan. 2019. Joint Type Inference on Entities and Relations via Graph Convolutional Networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1361–1370, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Joint Type Inference on Entities and Relations via Graph Convolutional Networks (Sun et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1131.pdf
Data
ACE 2005