2007 FirstOrderProbModForCorefRes

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Subject Headings: Coreference Resolution Algorithm, Coreference Resolution System.

Notes

Cited By

2008

Quotes

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 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 first-order logic representation can be incorporated into a probabilistic model and scaled efficiently.


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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2007 FirstOrderProbModForCorefResAron Culotta
Michael Wick
Robert Hall
Andrew McCallum
First-Order Probabilistic Models for Coreference ResolutionProceedings of the Human Language Technology Conference of the North American Chapter of the Association of Computational Linguisticshttp://www.cs.umass.edu/~culotta/pubs/culotta07first.pdf2007