Conditional Models of Identity Uncertainty with Application to Noun Coreference

Coreference analysis, also known as record linkage or identity uncertainty, is a difficult and important problem in natural language processing, databases, citation matching and many other tasks. This paper introduces several discriminative, conditional-probability models for coreference analysis, all examples of undirected graphical models. Unlike many historical approaches to coreference, the models presented here are relational--they do not assume that pairwise coreference decisions should be made independently from each other. Unlike other relational models of coreference that are generative, the conditional model here can incorporate a great variety of features of the input without having to be concerned about their dependencies--paralleling the advantages of conditional random fields over hidden Markov models. We present positive results on noun phrase coreference in two standard text data sets.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Andrew McCallum, Ben Wellner
• Keywords: Coreference Analysis, Hidden Markov Models, NIPS 2004, NIPS 2007, Undirected Graphical Models