Entity Disambiguation for Knowledge Base Population

The integration of facts derived from information extraction systems into existing knowledge bases requires a system to disambiguate entity mentions in the text. This is challenging due to issues such as non-uniform variations in entity names, mention ambiguity, and entities absent from a knowledge base. We present a state of the art system for entity disambiguation that not only addresses these challenges but also scales to knowledge bases with several million entries using very little resources. Further, our approach achieves performance of up to 95% on entities mentioned from newswire and 80% on a public test set that was designed to include challenging queries.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Mark Dredze, Paul McNamee, Delip Rao, Adam Gerber, Tim Finin
• Keywords: COLING 2010, Computational Linguistics, Entity Mentions, Information Extraction Systems, Knowledge Base