Robust Entity Clustering via Phylogenetic Inference
Nicholas Andrews, Jason M. Eisner, Mark H. Dredze · 2014
Entity clustering must determine when two named-entity mentions refer to the same entity. Typical approaches use a pipeline ar-chitecture that clusters the mentions using fixed or learned measures of name and con-text similarity. In this paper, we propose a model for cross-document coreference res-olution that achieves robustness by learn-ing similarity from unlabeled data. The generative process assumes that each entity mention arises from copying and option-ally mutating an earlier name from a sim-ilar context. Clustering the mentions into entities depends on recovering this copying tree jointly with estimating models of the mutation process and parent selection pro-cess. We present a block Gibbs sampler for posterior inference and an empirical evalu-ation on several datasets. 1