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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6301655/ https://www.ncbi.nlm.nih.gov/pubmed/30571777 http://dx.doi.org/10.1371/journal.pone.0208695 |
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author | AL-Saiagh, Wafaa Tiun, Sabrina AL-Saffar, Ahmed Awang, Suryanti Al-khaleefa, A. S. |
author_facet | AL-Saiagh, Wafaa Tiun, Sabrina AL-Saffar, Ahmed Awang, Suryanti Al-khaleefa, A. S. |
author_sort | AL-Saiagh, Wafaa |
collection | PubMed |
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 | Online Article Text |
id | pubmed-6301655 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-63016552019-01-08 Word sense disambiguation using hybrid swarm intelligence approach AL-Saiagh, Wafaa Tiun, Sabrina AL-Saffar, Ahmed Awang, Suryanti Al-khaleefa, A. S. PLoS One Research Article 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 2018-12-20 /pmc/articles/PMC6301655/ /pubmed/30571777 http://dx.doi.org/10.1371/journal.pone.0208695 Text en © 2018 AL-Saiagh et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article AL-Saiagh, Wafaa Tiun, Sabrina AL-Saffar, Ahmed Awang, Suryanti Al-khaleefa, A. S. Word sense disambiguation using hybrid swarm intelligence approach |
title | 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_short | Word sense disambiguation using hybrid swarm intelligence approach |
title_sort | word sense disambiguation using hybrid swarm intelligence approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6301655/ https://www.ncbi.nlm.nih.gov/pubmed/30571777 http://dx.doi.org/10.1371/journal.pone.0208695 |
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