A Constrained Latent Variable Model for Coreference Resolution

Coreference resolution is a well known clustering task in Natural Language Processing. In this paper, we describe the Latent Left Linking model (LM), a novel, principled, and linguistically motivated latent structured prediction approach to coreference resolution. We show that LM admits efficient inference and can be augmented with knowledge-based constraints; we also present a fast stochastic gradient based learning. Experiments on ACE and Ontonotes data show that LM and its constrained version, CLM, are more accurate than several state-of-the-art approaches as well as some structured prediction models proposed in the literature.