No noun phrase left behind: Detecting and typing unlinkable entities
|No noun phrase left behind: Detecting and typing unlinkable entities|
|Author(s)||Lin T., Mausam, Etzioni O.|
|Published in||EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference|
|Keyword(s)||Unknown (Extra: Long tail, Named entity recognition, Noun phrase, Question Answering, Wikipedia, Wikipedia articles, Natural language processing systems)|
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No noun phrase left behind: Detecting and typing unlinkable entities is a 2012 conference paper written in English by Lin T., Mausam, Etzioni O. and published in EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference.
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 un-linkable 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.
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