Joint Type Inference on Entities and Relations via Graph Convolutional Networks
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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Datasets
ACE 2005
Results from the Paper
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric)
