%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