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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2017
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| Online Access: | http://journalarticle.ukm.my/11842/ http://journalarticle.ukm.my/11842/1/17781-54213-1-PB.pdf |
| _version_ | 1848812841193177088 |
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| author | Ummi Zakiah Zainodin, Nazlia Omar, Abdulgabbar Saif, |
| author_facet | Ummi Zakiah Zainodin, Nazlia Omar, Abdulgabbar Saif, |
| author_sort | Ummi Zakiah Zainodin, |
| building | UKM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T00:08:42Z |
| format | Article |
| id | oai:generic.eprints.org:11842 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:08:42Z |
| publishDate | 2017 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org: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 |
| spellingShingle | Ummi Zakiah Zainodin, Nazlia Omar, Abdulgabbar Saif, Semantic measure based on features in lexical knowledge sources |
| title | 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_short | Semantic measure based on features in lexical knowledge sources |
| title_sort | semantic measure based on features in lexical knowledge sources |
| url | http://journalarticle.ukm.my/11842/ http://journalarticle.ukm.my/11842/ http://journalarticle.ukm.my/11842/1/17781-54213-1-PB.pdf |