Joint Type Inference on Entities and Relations via Graph Convolutional Networks

@inproceedings{Sun2019JointTI,
  title={Joint Type Inference on Entities and Relations via Graph Convolutional Networks},
  author={Changzhi Sun and Yeyun Gong and Yuanbin Wu and Ming Gong and Daxin Jiang and Man Lan and Shiliang Sun and Nan Duan},
  booktitle={Annual Meeting of the Association for Computational Linguistics},
  year={2019}
}
We develop a new paradigm for the task of joint entity relation extraction. [] Key Method 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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