Cross-document coreference resolution: A key technology for learning by reading

Autor:	Mayfield, J., Alexander, D., Dorr, B., Eisner, J., Tamer Elsayed, Finin, T., Fink, C., Freedman, M., Garera, N., Mcnamee, P., Mohammad, S., Oard, D., Piatko, C., Sayeed, A., Syed, Z., Weischedel, R., Xu, T., Yarowsky, D.
Předmět:	language Reading Cross-Document Coreference information extraction UMBC Ebiquity Research Group natural language processing GeneralLiterature_REFERENCE(e.g. dictionaries encyclopedias glossaries)
Zdroj:	Scopus-Elsevier
Popis:	Proceedings of the AAAI 2009 Spring Symposium on Learning by Reading and Learning to Read 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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7ce082d82e0788b44be3f98c85eded1 http://www.scopus.com/inward/record.url?eid=2-s2.0-70350542750&partnerID=MN8TOARS Zobrazit plný text záznamu