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Entity recognition in information extraction
Abstract Detecting and resolving entities is an impDetecting and resolving entities is an important step in information retrieval applications. Humans are able to recognize entities by context, but information extraction systems (IES) need to apply sophisticated algorithms to recognize an entity. The development and implementation of an entity recognition algorithm is described in this paper. The implemented system is integrated with an IES that derives triples from unstructured text. By doing so, the triples are more valuable in query answering because they refer to identified entities. By extracting the information from Wikipedia encyclopedia, a dictionary of entities and their contexts is built. The entity recognition computes a score for context similarity which is based on cosine similarity with a tf-idf weighting scheme and the string similarity. The implemented system shows a good accuracy on Wikipedia articles, is domain independent, and recognizes entities of arbitrary types.nd recognizes entities of arbitrary types.
Abstractsub Detecting and resolving entities is an impDetecting and resolving entities is an important step in information retrieval applications. Humans are able to recognize entities by context, but information extraction systems (IES) need to apply sophisticated algorithms to recognize an entity. The development and implementation of an entity recognition algorithm is described in this paper. The implemented system is integrated with an IES that derives triples from unstructured text. By doing so, the triples are more valuable in query answering because they refer to identified entities. By extracting the information from Wikipedia encyclopedia, a dictionary of entities and their contexts is built. The entity recognition computes a score for context similarity which is based on cosine similarity with a tf-idf weighting scheme and the string similarity. The implemented system shows a good accuracy on Wikipedia articles, is domain independent, and recognizes entities of arbitrary types.nd recognizes entities of arbitrary types.
Bibtextype inproceedings  +
Doi 10.1007/978-3-319-05476-6_12  +
Has author Hanafiah N. + , Quix C. +
Has extra keyword Database systems + , Cosine similarity + , Domain independents + , Entity recognition + , Information extraction systems + , Retrieval applications + , String similarity + , Unstructured texts + , Wikipedia articles + , Information retrieval systems +
Issn 3029743  +
Issue PART 1  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 113–122  +
Published in Lecture Notes in Computer Science +
Title Entity recognition in information extraction +
Type conference paper  +
Volume 8397 LNAI  +
Year 2014 +
Creation dateThis property is a special property in this wiki. 6 November 2014 15:20:01  +
Categories Publications without keywords parameter  + , Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 15:20:01  +
DateThis property is a special property in this wiki. 2014  +
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