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

Authors: James Mayfield, and et al.

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

Date: March 23, 2009

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.

Type: InProceedings

Publisher: AAAI Press

Tags: natural language processing, information extraction, language

Google Scholar: tkrlAlnfGzIJ

Number of Google Scholar citations: 6 [show citations]

Number of downloads: 820

Available for download as

size: 339771 bytes

Bookmark at: Digg | Del.icio.us | Connotea | CiteULike