A Discriminative Latent Variable Model for Clustering of Streaming Data with Application to Coreference Resolution
Rajhans Samdani, Kai-Wei Chang, Dan Roth · 2013
We present a latent variable structured predic-tion model, called the Latent Left-linking Model (L3M), for discriminative supervised clustering of items that follow a streaming order. L3M ad-mits efficient inference and we present a learning framework for L3M that smoothly interpolates between latent structural SVMs and hidden vari-able CRFs. We present a fast stochastic gradient-based learning technique for L3M. We apply L3M to coreference resolution, which is a well known clustering task in Natural Language Pro-cessing, and experimentally show that L3M out-performs several existing structured prediction-based techniques for coreference as well as sev-eral state-of-the-art, albeit ad hoc, approaches. 1.