Cross-Document Coreference Resolution: A Key Technology for Learning by Reading
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Author/Creator ORCID
Date
2009-03-23
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Citation of Original Publication
James 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.pdf
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Abstract
Automatic 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.