@inproceedings{sun-etal-2019-joint,
title = "Joint Type Inference on Entities and Relations via Graph Convolutional Networks",
author = "Sun, Changzhi and
Gong, Yeyun and
Wu, Yuanbin and
Gong, Ming and
Jiang, Daxin and
Lan, Man and
Sun, Shiliang and
Duan, Nan",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1131",
doi = "10.18653/v1/P19-1131",
pages = "1361--1370",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Joint Type Inference on Entities and Relations via Graph Convolutional Networks
%A Sun, Changzhi
%A Gong, Yeyun
%A Wu, Yuanbin
%A Gong, Ming
%A Jiang, Daxin
%A Lan, Man
%A Sun, Shiliang
%A Duan, Nan
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F sun-etal-2019-joint
%X 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.
%R 10.18653/v1/P19-1131
%U https://aclanthology.org/P19-1131
%U https://doi.org/10.18653/v1/P19-1131
%P 1361-1370
Markdown (Informal)
[Joint Type Inference on Entities and Relations via Graph Convolutional Networks](https://aclanthology.org/P19-1131) (Sun et al., ACL 2019)
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.