Abstract
We develop a new paradigm for the task of
joint entity relation extraction. It first identi-
fies 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 effi-
cient 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.