Semantic measure based on features in lexical knowledge sources

Semantic measures between concepts require some of cognitive capabilities such as categorization and reasoning to estimate semantic association among concepts. For this reason, this problem has numerous applications in artificial intelligence, natural language processing, information retrieval, text...

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Main Authors:	Ummi Zakiah Zainodin, Nazlia Omar, Abdulgabbar Saif
Format:	Article
Language:	English
Published:	Penerbit Universiti Kebangsaan Malaysia 2017
Online Access:	http://journalarticle.ukm.my/11842/ http://journalarticle.ukm.my/11842/ http://journalarticle.ukm.my/11842/1/17781-54213-1-PB.pdf

id	ukm-11842
recordtype	eprints
spelling	ukm-118422018-07-09T04:07:06Z http://journalarticle.ukm.my/11842/ Semantic measure based on features in lexical knowledge sources Ummi Zakiah Zainodin, Nazlia Omar, Abdulgabbar Saif, Semantic measures between concepts require some of cognitive capabilities such as categorization and reasoning to estimate semantic association among concepts. For this reason, this problem has numerous applications in artificial intelligence, natural language processing, information retrieval, text clustering, and text categorization. Measuring lexical semantic relatedness generally requires certain background information about the concept or terms. Semantic measures between concepts are divided into two main sources: knowledge based and unstructured corpora. Both resources play important role in the task of measuring lexical semantic relatedness. Knowledge-based semantic measures have been proposed to estimate semantic similarity between two concepts using several approaches such as ontology-based, graph-based and concept's vector approaches. This paper reviews existing semantic similarity measures which depend on the lexical source and discusses the various approaches on semantic measures which include the path-based, information content, gloss-based and feature-based measures. This paper also focuses on semantic measures that are based on features using lexical knowledge sources and discusses some issues that arise in these measures. Penerbit Universiti Kebangsaan Malaysia 2017-06 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/11842/1/17781-54213-1-PB.pdf Ummi Zakiah Zainodin, and Nazlia Omar, and Abdulgabbar Saif, (2017) Semantic measure based on features in lexical knowledge sources. Asia-Pacific Journal of Information Technology and Multimedia, 6 (1). pp. 39-55. ISSN 2289-2192 http://ejournal.ukm.my/apjitm/issue/view/899
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
description	Semantic measures between concepts require some of cognitive capabilities such as categorization and reasoning to estimate semantic association among concepts. For this reason, this problem has numerous applications in artificial intelligence, natural language processing, information retrieval, text clustering, and text categorization. Measuring lexical semantic relatedness generally requires certain background information about the concept or terms. Semantic measures between concepts are divided into two main sources: knowledge based and unstructured corpora. Both resources play important role in the task of measuring lexical semantic relatedness. Knowledge-based semantic measures have been proposed to estimate semantic similarity between two concepts using several approaches such as ontology-based, graph-based and concept's vector approaches. This paper reviews existing semantic similarity measures which depend on the lexical source and discusses the various approaches on semantic measures which include the path-based, information content, gloss-based and feature-based measures. This paper also focuses on semantic measures that are based on features using lexical knowledge sources and discusses some issues that arise in these measures.
format	Article
author	Ummi Zakiah Zainodin, Nazlia Omar, Abdulgabbar Saif,
spellingShingle	Ummi Zakiah Zainodin, Nazlia Omar, Abdulgabbar Saif, Semantic measure based on features in lexical knowledge sources
author_facet	Ummi Zakiah Zainodin, Nazlia Omar, Abdulgabbar Saif,
author_sort	Ummi Zakiah Zainodin,
title	Semantic measure based on features in lexical knowledge sources
title_short	Semantic measure based on features in lexical knowledge sources
title_full	Semantic measure based on features in lexical knowledge sources
title_fullStr	Semantic measure based on features in lexical knowledge sources
title_full_unstemmed	Semantic measure based on features in lexical knowledge sources
title_sort	semantic measure based on features in lexical knowledge sources
publisher	Penerbit Universiti Kebangsaan Malaysia
publishDate	2017
url	http://journalarticle.ukm.my/11842/ http://journalarticle.ukm.my/11842/ http://journalarticle.ukm.my/11842/1/17781-54213-1-PB.pdf
first_indexed	2023-09-18T20:01:16Z
last_indexed	2023-09-18T20:01:16Z
_version_	1777406864394289152

Semantic measure based on features in lexical knowledge sources

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