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arXiv · 1606.01323

Improving Coreference Resolution by Learning Entity-Level Distributed Representations

Kevin Clark·Christopher D. Manning
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Abstract

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.

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Kevin Clark, Christopher D. Manning. 2016-06-04. Improving Coreference Resolution by Learning Entity-Level Distributed Representations. https://arxiv.org/abs/1606.01323

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