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Unpublished Paper
First-Order Probabilistic Models for Coreference Resolution
(2006)
Abstract
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 an 11% error reduction over a comparable method that only considers features of pairs of noun phrases. This result demonstrates an example of how a powerful representation language can be incorporated into a probabilistic model and be scaled efficiently.
Disciplines
Publication Date
2006
Comments
This is the pre-published version harvested from CIIR.
Citation Information
Aron Culotta, Michael Wick, Robert Hall and Andrew McCallum. "First-Order Probabilistic Models for Coreference Resolution" (2006) Available at: http://works.bepress.com/andrew_mccallum/116/