Cross-Document Coreference Resolution: A Key Technology for Learning by Reading
James Mayfield, David Alexander, Bonnie Jean Dorr, Jason M. Eisner, Tamer Elsayed, Tim Finin, Fink, Clay, Marjorie R. Freedman, Nikesh Garera, Paul McNamee, Saif M. Mohammad, Douglas W. Oard, Christine Piatko, Asad Sayeed, Zareen Syed, Ralph Weischedel, Xu Tan, David Yarowsky · Maryland Shared Open Access Repository (USMAI Consortium) · 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.