Papers › Learning Global Features for Coreference Resolution

Learning Global Features for Coreference Resolution

11 Apr 2016NAACL 2016 6arXiv:1604.03035archive 2025-07-28

Sam Wiseman, Alexander M. Rush, Stuart M. Shieber

There is compelling evidence that coreference prediction would benefit from modeling global information about entity-clusters. Yet, state-of-the-art performance can be achieved with systems treating each mention prediction independently, which we attribute to the inherent difficulty of crafting informative cluster-level features. We instead propose to use recurrent neural networks (RNNs) to learn latent, global representations of entity clusters directly from their mentions. We show that such representations are especially useful for the prediction of pronominal mentions, and can be incorporated into an end-to-end coreference system that outperforms the state of the art without requiring any additional search.

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