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NAACL
2007

First-Order Probabilistic Models for Coreference Resolution

13 years 5 months ago
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.
Aron Culotta, Michael L. Wick, Andrew McCallum
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where NAACL
Authors Aron Culotta, Michael L. Wick, Andrew McCallum
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