Cross-Document Coreference Resolution: A Key Technology for Learning by Reading

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dc.contributor.authorMayfield, James
dc.contributor.authorAlexander, David
dc.contributor.authorDorr, Bonnie
dc.contributor.authorEisner, Jason
dc.contributor.authorElsayed, Tamer
dc.contributor.authorFinin, Tim
dc.contributor.authorFink, Clay
dc.contributor.authorFreedman, Marjorie
dc.contributor.authorGarera, Nikesh
dc.contributor.authorMcNamee, Paul
dc.contributor.authorMohammad, Saif
dc.contributor.authorOard, Douglas
dc.contributor.authorPiatko, Christine
dc.contributor.authorSayeed, Asad
dc.contributor.authorSyed, Zareen
dc.contributor.authorWeischedel, Ralph
dc.contributor.authorXu, Tan
dc.contributor.authorYarowsky, David
dc.date.accessioned2018-12-03T18:53:09Z
dc.date.available2018-12-03T18:53:09Z
dc.date.issued2009-03-23
dc.descriptionProceedings of the AAAI 2009 Spring Symposium on Learning by Reading and Learning to Readen_US
dc.description.abstractAutomatic knowledge base population from text is an important technology for a broad range of approaches to learning by reading. Effective automated knowledge base population depends critically upon coreference resolution of entities across sources. Use of a wide range of features, both those that capture evidence for entity merging and those that argue against merging, can significantly improve machine learning-based cross-document coreference resolution. Results from the Global Entity Detection and Recognition task of the NIST Automated Content Extraction (ACE) 2008 evaluation support this conclusion.en_US
dc.description.urihttps://www.aaai.org/Papers/Symposia/Spring/2009/SS-09-07/SS09-07-011.pdfen_US
dc.format.extent6 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/M2H708457
dc.identifier.citationJames Mayfield, David Alexander, Bonnie Dorr, Jason Eisner, Tamer Elsayed, Tim Finin, Clay Fink, Marjorie Freedman, Nikesh Garera, Paul McNamee, Saif Mohammad, Douglas Oard, Christine Piatko, Asad Sayeed, Zareen Syed, Ralph Weischedel, Tan Xu and David Yarowsky, Cross-Document Coreference Resolution: A Key Technology for Learning by ReadingProceedings of the AAAI 2009 Spring Symposium on Learning by Reading and Learning to Read, 2009, https://www.aaai.org/Papers/Symposia/Spring/2009/SS-09-07/SS09-07-011.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/12159
dc.language.isoen_USen_US
dc.publisherAAAIen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectinformation extractionen_US
dc.subjectlanguageen_US
dc.subjectnatural language processingen_US
dc.subjectReadingen_US
dc.subjectCross-Document Coreferenceen_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titleCross-Document Coreference Resolution: A Key Technology for Learning by Readingen_US
dc.typeTexten_US

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