Joint Distance and Information Content Word Similarity Measure

Joint Distance and Information Content Word Similarity Measure

Measuring semantic similarity between words is very important to many applications related to information retrieval and natural language processing. In the paper, we have discovered that word similarity metrics suffer from the drawback of obtaining equal similarities of two words, if they have the...

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Bibliographic Details
Main Authors: Issa, Atoum, Bong, Chih How
Format: Book Section
Language:English
Published: Springer Berlin Heidelberg 2014
Subjects:
T Technology (General)
Online Access:http://ir.unimas.my/8458/
http://ir.unimas.my/8458/
http://ir.unimas.my/8458/
http://ir.unimas.my/8458/1/Joint%20Distance%20and%20Information%20Content%20Word%20Similarity%20Measure%20%28abstract%29.pdf
id unimas-8458
recordtype eprints
spelling unimas-84582015-08-03T08:01:41Z http://ir.unimas.my/8458/ Joint Distance and Information Content Word Similarity Measure Issa, Atoum Bong, Chih How T Technology (General) Measuring semantic similarity between words is very important to many applications related to information retrieval and natural language processing. In the paper, we have discovered that word similarity metrics suffer from the drawback of obtaining equal similarities of two words, if they have the same path and depth values in WordNet. Likewise information content methods which depend on word probability of a corpus tend to posture the same drawback. This paper proposes a new hybrid semantic similarity to overcome the drawbacks by exploiting advantages of Li and Lin methods. On a benchmark set of human judgments on Miller Charles and Rubenstein Goodenough data sets, the proposed approach outperforms existing methods in distance and information content based methods. Springer Berlin Heidelberg 2014 Book Section NonPeerReviewed text en http://ir.unimas.my/8458/1/Joint%20Distance%20and%20Information%20Content%20Word%20Similarity%20Measure%20%28abstract%29.pdf Issa, Atoum and Bong, Chih How (2014) Joint Distance and Information Content Word Similarity Measure. In: Soft Computing Applications and Intelligent Systems. Springer Berlin Heidelberg, pp. 257-267. http://www.researchgate.net/publication/268520347_Joint_Distance_and_Information_Content_Word_Similarity_Measure DOI: 10.1007/978-3-642-40567-9_22
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Sarawak
building UNIMAS Institutional Repository
collection Online Access
language English
topic T Technology (General)
spellingShingle T Technology (General)
Issa, Atoum
Bong, Chih How
Joint Distance and Information Content Word Similarity Measure
description Measuring semantic similarity between words is very important to many applications related to information retrieval and natural language processing. In the paper, we have discovered that word similarity metrics suffer from the drawback of obtaining equal similarities of two words, if they have the same path and depth values in WordNet. Likewise information content methods which depend on word probability of a corpus tend to posture the same drawback. This paper proposes a new hybrid semantic similarity to overcome the drawbacks by exploiting advantages of Li and Lin methods. On a benchmark set of human judgments on Miller Charles and Rubenstein Goodenough data sets, the proposed approach outperforms existing methods in distance and information content based methods.
format Book Section
author Issa, Atoum
Bong, Chih How
author_facet Issa, Atoum
Bong, Chih How
author_sort Issa, Atoum
title Joint Distance and Information Content Word Similarity Measure
title_short Joint Distance and Information Content Word Similarity Measure
title_full Joint Distance and Information Content Word Similarity Measure
title_fullStr Joint Distance and Information Content Word Similarity Measure
title_full_unstemmed Joint Distance and Information Content Word Similarity Measure
title_sort joint distance and information content word similarity measure
publisher Springer Berlin Heidelberg
publishDate 2014
url http://ir.unimas.my/8458/
http://ir.unimas.my/8458/
http://ir.unimas.my/8458/
http://ir.unimas.my/8458/1/Joint%20Distance%20and%20Information%20Content%20Word%20Similarity%20Measure%20%28abstract%29.pdf
first_indexed 2018-09-06T15:34:00Z
last_indexed 2018-09-06T15:34:00Z
_version_ 1610872825463177216

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