Machine Learning for Coreference Resolution: Recent Successes and Future Challenges
| dc.contributor.author | Ng, Vincent | en_US |
| dc.date.accessioned | 2007-04-04T19:28:35Z | |
| dc.date.available | 2007-04-04T19:28:35Z | |
| dc.date.issued | 2003-12-23 | en_US |
| dc.description.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. | en_US |
| dc.format.extent | 614245 bytes | |
| dc.format.mimetype | application/postscript | |
| dc.identifier.citation | http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cis/TR2003-1918 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1813/5630 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Cornell University | en_US |
| dc.subject | computer science | en_US |
| dc.subject | technical report | en_US |
| dc.title | Machine Learning for Coreference Resolution: Recent Successes and Future Challenges | en_US |
| dc.type | technical report | en_US |
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