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dc.contributor.authorNg, Vincenten_US
dc.date.accessioned2007-04-04T19:28:35Z
dc.date.available2007-04-04T19:28:35Z
dc.date.issued2003-12-23en_US
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cis/TR2003-1918en_US
dc.identifier.urihttps://hdl.handle.net/1813/5630
dc.description.abstractState-of-the-art coreference resolution systems are mostly knowledge-based systems that operate by relying on a set of hand-crafted coreference resolution heuristics. Recently, however, machine learning approaches have been shown to be a promising way to build coreference resolution systems that are more robust than their knowledge-based counterparts. Nevertheless, there are several key issues in existing machine learning approaches to the problem that are either not explored or being overlooked, potentially leading to a deterioration of system performance. This document examines each of these issues in detail and suggests potential solutions.en_US
dc.format.extent614245 bytes
dc.format.mimetypeapplication/postscript
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleMachine Learning for Coreference Resolution: Recent Successes and Future Challengesen_US
dc.typetechnical reporten_US


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