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Augmenting concept definition in gloss vector semantic relatedness measure using wikipedia articles
Abstract Semantic relatedness measures are widely uSemantic relatedness measures are widely used in text mining and information retrieval applications. Considering these automated measures, in this research paper we attempt to improve Gloss Vector relatedness measure for more accurate estimation of relatedness between two given concepts. Generally, this measure, by constructing concepts definitions (Glosses) from a thesaurus, tries to find the angle between the concepts' gloss vectors for the calculation of relatedness. Nonetheless, this definition construction task is challenging as thesauruses do not provide full coverage of expressive definitions for the particularly specialized concepts. By employing Wikipedia articles and other external resources, we aim at augmenting these concepts' definitions. Applying both definition types to the biomedical domain, using MEDLINE as corpus, UMLS as the default thesaurus, and a reference standard of 68 concept pairs manually rated for relatedness, we show exploiting available resources on the Web would have positive impact on final measurement of semantic relatedness.final measurement of semantic relatedness.
Abstractsub Semantic relatedness measures are widely uSemantic relatedness measures are widely used in text mining and information retrieval applications. Considering these automated measures, in this research paper we attempt to improve Gloss Vector relatedness measure for more accurate estimation of relatedness between two given concepts. Generally, this measure, by constructing concepts definitions (Glosses) from a thesaurus, tries to find the angle between the concepts' gloss vectors for the calculation of relatedness. Nonetheless, this definition construction task is challenging as thesauruses do not provide full coverage of expressive definitions for the particularly specialized concepts. By employing Wikipedia articles and other external resources, we aim at augmenting these concepts' definitions. Applying both definition types to the biomedical domain, using MEDLINE as corpus, UMLS as the default thesaurus, and a reference standard of 68 concept pairs manually rated for relatedness, we show exploiting available resources on the Web would have positive impact on final measurement of semantic relatedness.final measurement of semantic relatedness.
Bibtextype inproceedings  +
Doi 10.1007/978-981-4585-18-7-70  +
Has author Pesaranghader A. + , Rezaei A. +
Has extra keyword Biomedical text minings + , Medline + , NAtural language processing + , Semantic relatedness + , UMLS + , Web mining + , Wikipedia + , Bioinformatics + , Data mining + , Thesauri + , Natural language processing systems +
Has keyword Bioinformatics + , Biomedical Text Mining + , MEDLINE + , Natural Language Processing + , Semantic relatedness + , UMLS + , Web mining + , Wikipedia +
Isbn 9789814585170  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 623–630  +
Published in Lecture Notes in Electrical Engineering +
Title Augmenting concept definition in gloss vector semantic relatedness measure using wikipedia articles +
Type conference paper  +
Volume 285 LNEE  +
Year 2014 +
Creation dateThis property is a special property in this wiki. 6 November 2014 18:32:18  +
Categories 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 18:32:18  +
DateThis property is a special property in this wiki. 2014  +
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