Dredze, MarkMcNamee, PaulRao, DelipGerber, AdamFinin, Tim2018-11-142018-11-142010-08-23http://hdl.handle.net/11603/11986Proceedings of the 23rd International Conference on Computational LinguisticsThe 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.9 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.information extractionknowledge basenatural language processingnatural language processingUMBC Ebiquity Research GroupText