First-Order Probabilistic Models for Coreference Resolution

Traditional noun phrase coreference resolution systems represent features only of pairs of noun phrases. In this paper, we propose a machine learning method that enables features over sets of noun phrases, resulting in a first-order probabilistic model for coreference. We outline a set of approximations that make this approach practical, and apply our method to the ACE coreference dataset, achieving a 45% error reduction over a comparable method that only considers features of pairs of noun phrases. This result demonstrates an example of how a firstorder logic representation can be incorporated into a probabilistic model and scaled efficiently.

• Type: Conference
• Year: 2007
• Where: NAACL
• Authors: Aron Culotta, Michael L. Wick, Andrew McCallum
• Keywords: Computational Linguistics, NAACL 2007, Noun Phrase Coreference, Noun Phrases, Probabilistic Model