Proceedings of the AAAI 2009 Spring Symposium on Learning by Reading and Learning to Read

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

James Mayfield, David Alexander, Bonnie J Dorr, Jason Eisner, Tamer Elsayed, Tim Finin, Clay Fink, Marjorie Freedman, Nikesh Garera, Paul McNamee, Doug Oard, Christine Piatko, Zareen Syed, Ralph Weischedel, David Yarowsky, and et al.

March 23, 2009

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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Tags: information extraction, language, natural language processing

Type: InProceedings

Publisher: AAAI Press

Downloads: 3488 downloads

Google Scholar Citations: 6 citations