A Discriminative Hierarchical Model for Fast Coreference at Large Scale

Methods that measure compatibility between mention pairs are currently the dominant ap- proach to coreference. However, they suffer from a number of drawbacks including diffi- culties scaling to large numbers of mentions and limited representational power. As these drawbacks become increasingly restrictive, the need to replace the pairwise approaches with a more expressive, highly scalable al- ternative is becoming urgent. In this paper we propose a novel discriminative hierarchical model that recursively partitions entities into trees of latent sub-entities. These trees suc- cinctly summarize the mentions providing a highly compact, information-rich structure for reasoning about entities and coreference un- certainty at massive scales. We demonstrate that the hierarchical model is several orders of magnitude faster than pairwise, allowing us to perform coreference on six million author mentions in under four hours on a single CPU.