2012 NoNounPhraseLeftBehindDetecting

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Subject Headings: Entity Mention Recognition and Linking Task, Document to Wikipedia Interlinking Task.


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Entity linking systems link noun-phrase mentions in text to their corresponding Wikipedia articles. However, NLP applications would gain from the ability to detect and type all entities mentioned in text, including the long tail of entities not prominent enough to have their own Wikipedia articles. In this paper we show that once the Wikipedia entities mentioned in a corpus of textual assertions are linked, this can further enable the detection and fine-grained typing of the unlinkable entities. Our proposed method for detecting unlinkable entities achieves 24% greater accuracy than a Named Entity Recognition baseline, and our method for fine-grained typing is able to propagate over 1,000 types from linked Wikipedia entities to unlinkable entities. Detection and typing of unlinkable entities can increase yield for NLP applications such as typed question answering.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2012 NoNounPhraseLeftBehindDetectingThomas Lin
Oren Etzioni
No Noun Phrase Left Behind: Detecting and Typing Unlinkable Entities2012
AuthorThomas Lin +, Mausam + and Oren Etzioni +
titleNo Noun Phrase Left Behind: Detecting and Typing Unlinkable Entities +
year2012 +