Improving Coreference Resolution by Learning Entity Level Distributed Representations
A long standing challenge in coreference resolution has been the incorporation of entity level information features defined over clusters of mentions instead of mention pairs. We present a neural network based coreference system that produces high dimensional vector representations for pairs of coreference clusters. Using these representations, our system learns when combining clusters is desirable. We train the system with a learning to search algorithm that teaches it which local decisions (cluster merges) will lead to a high scoring final coreference partition. The system substantially outperforms the current state of the art on the English and Chinese portions of the CoNLL 2012 Shared Task dataset despite using few hand engineered features.
ACL