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Entity Disambiguation for Knowledge Base Population

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by Mark Dredze , Paul Mcnamee , Delip Rao , Adam Gerber , Tim Finin
Citations:50 - 4 self
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BibTeX

@MISC{Dredze_entitydisambiguation,
    author = {Mark Dredze and Paul Mcnamee and Delip Rao and Adam Gerber and Tim Finin},
    title = {Entity Disambiguation for Knowledge Base Population},
    year = {}
}

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Abstract

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. 1

Keyphrases

entity disambiguation    knowledge base population    knowledge base    art system    information extraction system    mention ambiguity    entity name    little resource    public test set    entity mention    non-uniform variation    challenging query   

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