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Please use this identifier to cite or link to this item: http://hdl.handle.net/1813/5630
Title: Machine Learning for Coreference Resolution: Recent Successes and Future Challenges
Authors: Ng, Vincent
Keywords: computer science
technical report
Issue Date: 23-Dec-2003
Publisher: Cornell University
Citation: http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cis/TR2003-1918
Abstract: State-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.
URI: http://hdl.handle.net/1813/5630
Appears in Collections:Computing and Information Science Technical Reports

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