Publications from 2014
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Robust Entity Clustering via Phylogenetic Inference
Entity clustering must determine when two named-entity mentions refer to the same entity. Typical approaches use a pipeline architecture that clusters the mentions using fixed or learned measures of name and context similarity. In this paper, we propose a model for cross-document coreference resolution that achieves robustness by learning similarity from unlabeled data. The generative process assumes that each entity mention arises from copying and optionally mutating an earlier name from a similar context. Clustering the mentions into entities depends on recovering this copying tree jointly with estimating models of the mutation process and parent selection process. We present a block Gibbs sampler for posterior inference and an empirical evaluation on several datasets.
Nicholas Andrews, Jason Eisner, Mark Dredze
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2014
