Word sense disambiguation using hybrid swarm intelligence approach

Word sense disambiguation (WSD) is the process of identifying an appropriate sense for an ambiguous word. With the complexity of human languages in which a single word could yield different meanings, WSD has been utilized by several domains of interests such as search engines and machine translation...

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Main Authors:	Al-Saiagh, Wafaa, Sabrina, Tiun, Al-Saffar, Ahmed Ali Mohammed, Suryanti, Awang, Al- khaleefa, A. S.
Format:	Article
Language:	English
Published:	Public Library of Science 2018
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/23586/ http://umpir.ump.edu.my/id/eprint/23586/ http://umpir.ump.edu.my/id/eprint/23586/ http://umpir.ump.edu.my/id/eprint/23586/1/Word%20sense%20disambiguation%20using%20hybrid.pdf

id	ump-23586
recordtype	eprints
spelling	ump-235862019-01-03T01:19:21Z http://umpir.ump.edu.my/id/eprint/23586/ Word sense disambiguation using hybrid swarm intelligence approach Al-Saiagh, Wafaa Sabrina, Tiun Al-Saffar, Ahmed Ali Mohammed Suryanti, Awang Al- khaleefa, A. S. QA75 Electronic computers. Computer science Word sense disambiguation (WSD) is the process of identifying an appropriate sense for an ambiguous word. With the complexity of human languages in which a single word could yield different meanings, WSD has been utilized by several domains of interests such as search engines and machine translations. The literature shows a vast number of techniques used for the process of WSD. Recently, researchers have focused on the use of meta-heuristic approaches to identify the best solutions that reflect the best sense. However, the application of meta-heuristic approaches remains limited and thus requires the efficient exploration and exploitation of the problem space. Hence, the current study aims to propose a hybrid meta-heuristic method that consists of particle swarm optimization (PSO) and simulated annealing to find the global best meaning of a given text. Different semantic measures have been utilized in this model as objective functions for the proposed hybrid PSO. These measures consist of JCN and extended Lesk methods, which are combined effectively in this work. The proposed method is tested using a three-benchmark dataset (SemCor 3.0, SensEval-2, and SensEval-3). Results show that the proposed method has superior performance in comparison with state-of-the-art approaches. Public Library of Science 2018 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/23586/1/Word%20sense%20disambiguation%20using%20hybrid.pdf Al-Saiagh, Wafaa and Sabrina, Tiun and Al-Saffar, Ahmed Ali Mohammed and Suryanti, Awang and Al- khaleefa, A. S. (2018) Word sense disambiguation using hybrid swarm intelligence approach. PLoS ONE, 13 (12). pp. 1-19. ISSN 1932-6203 https://doi.org/10.1371/journal.pone.0208695 https://doi.org/10.1371/journal.pone.0208695
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Al-Saiagh, Wafaa Sabrina, Tiun Al-Saffar, Ahmed Ali Mohammed Suryanti, Awang Al- khaleefa, A. S. Word sense disambiguation using hybrid swarm intelligence approach
description	Word sense disambiguation (WSD) is the process of identifying an appropriate sense for an ambiguous word. With the complexity of human languages in which a single word could yield different meanings, WSD has been utilized by several domains of interests such as search engines and machine translations. The literature shows a vast number of techniques used for the process of WSD. Recently, researchers have focused on the use of meta-heuristic approaches to identify the best solutions that reflect the best sense. However, the application of meta-heuristic approaches remains limited and thus requires the efficient exploration and exploitation of the problem space. Hence, the current study aims to propose a hybrid meta-heuristic method that consists of particle swarm optimization (PSO) and simulated annealing to find the global best meaning of a given text. Different semantic measures have been utilized in this model as objective functions for the proposed hybrid PSO. These measures consist of JCN and extended Lesk methods, which are combined effectively in this work. The proposed method is tested using a three-benchmark dataset (SemCor 3.0, SensEval-2, and SensEval-3). Results show that the proposed method has superior performance in comparison with state-of-the-art approaches.
format	Article
author	Al-Saiagh, Wafaa Sabrina, Tiun Al-Saffar, Ahmed Ali Mohammed Suryanti, Awang Al- khaleefa, A. S.
author_facet	Al-Saiagh, Wafaa Sabrina, Tiun Al-Saffar, Ahmed Ali Mohammed Suryanti, Awang Al- khaleefa, A. S.
author_sort	Al-Saiagh, Wafaa
title	Word sense disambiguation using hybrid swarm intelligence approach
title_short	Word sense disambiguation using hybrid swarm intelligence approach
title_full	Word sense disambiguation using hybrid swarm intelligence approach
title_fullStr	Word sense disambiguation using hybrid swarm intelligence approach
title_full_unstemmed	Word sense disambiguation using hybrid swarm intelligence approach
title_sort	word sense disambiguation using hybrid swarm intelligence approach
publisher	Public Library of Science
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/23586/ http://umpir.ump.edu.my/id/eprint/23586/ http://umpir.ump.edu.my/id/eprint/23586/ http://umpir.ump.edu.my/id/eprint/23586/1/Word%20sense%20disambiguation%20using%20hybrid.pdf
first_indexed	2023-09-18T22:35:23Z
last_indexed	2023-09-18T22:35:23Z
_version_	1777416560618504192

Word sense disambiguation using hybrid swarm intelligence approach

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