Paper: Entity Disambiguation for Knowledge Base Population

ACL ID C10-1032
Title Entity Disambiguation for Knowledge Base Population
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010

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 challeng- ing due to issues such as non-uniform variations in entity names, mention ambiguity, and entities absent from a knowl- edge base. We present a state of the art system for entity dis- ambiguation that not only addresses these challenges but also scales to knowledge bases with several million entries using very little resources. Further, our approach achieves perfor- mance of up to 95% on entities mentioned from newswire and 80% on a public test set that was designed to include challenging queries.