NDLI: Entity Disambiguation and Linking over Queries using Encyclopedic Knowledge

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Entity Disambiguation and Linking over Queries using Encyclopedic Knowledge

Content Provider	Semantic Scholar
Author	Nguyen, Truc-Vien T.
Copyright Year	2013
Abstract	Literature has seen a large amount of work on entity recognition and semantic disambiguation in text but very limited on the effect in noisy text data. In this paper, we present an approach for recognizing and disambiguating entities in text based on the high coverage and rich structure of an online encyclopedia. This work was carried out on a collection of query logs from the Bridgeman Art Library. As queries are noisy unstructured text, pure natural language processing as well as computational techniques can create problems, we need to contend with the impact noise and the demands it places on query analysis. In order to cope with the noisy input, we use machine learning method with statistical measures derived from Wikipedia. It provides a huge electronic text from the Internet, which is also noisy. Our approach is an unsupervised approach and do not need any manual annotation made by human experts. We show that data collection from Wikipedia can be used statistically to derive good performance for entity recognition and semantic disambiguation over noisy unstructured text. Also, as no natural language specific tool is needed, the method can be applied to other languages in a similar manner with little adaptation.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.researchgate.net/profile/Truc-Vien_T_Nguyen/publication/258160625_Entity_Disambiguation_and_Linking_over_Queries_using_Encyclopedic_Knowledge/links/00b495274e5b714f21000000.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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