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dc.contributor.authorStoyanov, Veselin
dc.contributor.authorCardie, Claire
dc.contributor.authorGilbert, Nathan
dc.contributor.authorRiloff, Ellen
dc.contributor.authorButtler, David
dc.contributor.authorHysom, David
dc.date.accessioned2010-04-13T19:22:50Z
dc.date.available2010-04-13T19:22:50Z
dc.date.issued2010-04-13T19:22:50Z
dc.identifier.urihttps://hdl.handle.net/1813/14919
dc.description.abstractWe have created a software infrastructure called Reconcile that is a platform for the development of learning-based noun phrase (NP) coreference resolution systems. Reconcile’s architecture was designed to facilitate the rapid creation of coreference resolutions systems, easy implementation of new feature sets and approaches to coreference resolution, and empirical evaluation of coreference resolvers across a variety of benchmark data sets and standard scoring metrics. Reconcile is written in Java and can be easily customized with different subcomponents, feature sets, and parameter settings. In this report, we describe Reconcile’s architecture, processing pipeline, and the subcomponents and algorithms that are currently implemented and available in Reconcile. We also present experimental results showing that Reconcile can be used to create a coreference resolver which achieves performance levels comparable to state-of-the-art systems on six benchmark data sets.en_US
dc.description.sponsorshipThis research was supported in part by Lawrence Livermore National Laboratory subcontract B573245 and the Department of Homeland Security under ONR Grant N0014-07-1-0152.en_US
dc.language.isoen_USen_US
dc.subjectnatural language processingen_US
dc.subjectcoreference resolutionen_US
dc.titleReconcile: A Coreference Resolution Research Platformen_US
dc.typetechnical reporten_US


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