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Augmenting concept definition in gloss vector semantic relatedness measure using Wikipedia articles

Semantic 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,...

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Main Authors: Pesaranghader, Ahmad, Pesaranghader, Ali, Rezaei, Azadeh
Format: Conference or Workshop Item
Language:English
Published: Springer 2013
Online Access:http://psasir.upm.edu.my/id/eprint/60362/1/Augmenting%20concept%20definition%20in%20gloss%20vector%20semantic%20relatedness%20measure%20using%20Wikipedia%20articles.pdf
http://psasir.upm.edu.my/id/eprint/60362/
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spelling my.upm.eprints.603622018-05-21T03:27:29Z http://psasir.upm.edu.my/id/eprint/60362/ Augmenting concept definition in gloss vector semantic relatedness measure using Wikipedia articles Pesaranghader, Ahmad Pesaranghader, Ali Rezaei, Azadeh Semantic 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. Springer 2013 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/60362/1/Augmenting%20concept%20definition%20in%20gloss%20vector%20semantic%20relatedness%20measure%20using%20Wikipedia%20articles.pdf Pesaranghader, Ahmad and Pesaranghader, Ali and Rezaei, Azadeh (2013) Augmenting concept definition in gloss vector semantic relatedness measure using Wikipedia articles. In: First International Conference on Advanced Data and Information Engineering (DaEng-2013), 16-18 Dec. 2013, Kuala Lumpur, Malaysia. (pp. 623-630). 10.1007/978-981-4585-18-7_70
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Semantic 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.
format Conference or Workshop Item
author Pesaranghader, Ahmad
Pesaranghader, Ali
Rezaei, Azadeh
spellingShingle Pesaranghader, Ahmad
Pesaranghader, Ali
Rezaei, Azadeh
Augmenting concept definition in gloss vector semantic relatedness measure using Wikipedia articles
author_facet Pesaranghader, Ahmad
Pesaranghader, Ali
Rezaei, Azadeh
author_sort Pesaranghader, Ahmad
title Augmenting concept definition in gloss vector semantic relatedness measure using Wikipedia articles
title_short Augmenting concept definition in gloss vector semantic relatedness measure using Wikipedia articles
title_full Augmenting concept definition in gloss vector semantic relatedness measure using Wikipedia articles
title_fullStr Augmenting concept definition in gloss vector semantic relatedness measure using Wikipedia articles
title_full_unstemmed Augmenting concept definition in gloss vector semantic relatedness measure using Wikipedia articles
title_sort augmenting concept definition in gloss vector semantic relatedness measure using wikipedia articles
publisher Springer
publishDate 2013
url http://psasir.upm.edu.my/id/eprint/60362/1/Augmenting%20concept%20definition%20in%20gloss%20vector%20semantic%20relatedness%20measure%20using%20Wikipedia%20articles.pdf
http://psasir.upm.edu.my/id/eprint/60362/
_version_ 1643837337783762944
score 13.223943

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