James Mayfield, Bonnie
Dorr, Jason Eisner, Tim Finin,
Saif Mohammad, Douglas Oard, Ralph Weischedel,
David Yarowsky, and others
In Proceedings of the AAAI Spring Symposium on Learning by Reading and Learning to Read (AAAI-09), Menlo Park, CA.
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.
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