Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm
Word sense disambiguation (WSD) refers to determining the right meaning of a vague word using its context. The WSD intermediately consolidates the performance of final tasks to achieve high accuracy. Mainly, a WSD solution improves the accuracy of text summarisation, information retrieval, and machi...
| Main Authors: | Hamad, A.H., Mahmood, A.A., Abed, S.A., Ying, X. |
|---|---|
| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.29325 / |
| Published: |
IOS Press BV
2021
|
| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122016027&doi=10.3233%2fJIFS-210934&partnerID=40&md5=d12db3484c46e6d859fc6930d658d274 http://eprints.utp.edu.my/29325/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
utp-eprints.29325 |
|---|---|
| recordtype |
eprints |
| spelling |
utp-eprints.293252022-03-25T01:33:58Z Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm Hamad, A.H. Mahmood, A.A. Abed, S.A. Ying, X. Word sense disambiguation (WSD) refers to determining the right meaning of a vague word using its context. The WSD intermediately consolidates the performance of final tasks to achieve high accuracy. Mainly, a WSD solution improves the accuracy of text summarisation, information retrieval, and machine translation. This study addresses the WSD by assigning a set of senses to a given text, where the maximum semantic relatedness is obtained. This is achieved by proposing a swarm intelligence method, called firefly algorithm (FA) to find the best possible set of senses. Because of the FA is based on a population of solutions, it explores the problem space more than exploiting it. Hence, we hybridise the FA with a one-point search algorithm to improve its exploitation capacity. Practically, this hybridisation aims to maximise the semantic relatedness of an eligible set of senses. In this study, the semantic relatedness is measured by proposing a glosses-overlapping method enriched by the notion of information content. To evaluate the proposed method, we have conducted intensive experiments with comparisons to the related works based on benchmark datasets. The obtained results showed that our method is comparable if not superior to the related works. Thus, the proposed method can be considered as an efficient solver for the WSD task. © 2021 - IOS Press. All rights reserved. IOS Press BV 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122016027&doi=10.3233%2fJIFS-210934&partnerID=40&md5=d12db3484c46e6d859fc6930d658d274 Hamad, A.H. and Mahmood, A.A. and Abed, S.A. and Ying, X. (2021) Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm. Journal of Intelligent and Fuzzy Systems, 41 (6). pp. 7047-7061. http://eprints.utp.edu.my/29325/ |
| institution |
Universiti Teknologi Petronas |
| collection |
UTP Institutional Repository |
| description |
Word sense disambiguation (WSD) refers to determining the right meaning of a vague word using its context. The WSD intermediately consolidates the performance of final tasks to achieve high accuracy. Mainly, a WSD solution improves the accuracy of text summarisation, information retrieval, and machine translation. This study addresses the WSD by assigning a set of senses to a given text, where the maximum semantic relatedness is obtained. This is achieved by proposing a swarm intelligence method, called firefly algorithm (FA) to find the best possible set of senses. Because of the FA is based on a population of solutions, it explores the problem space more than exploiting it. Hence, we hybridise the FA with a one-point search algorithm to improve its exploitation capacity. Practically, this hybridisation aims to maximise the semantic relatedness of an eligible set of senses. In this study, the semantic relatedness is measured by proposing a glosses-overlapping method enriched by the notion of information content. To evaluate the proposed method, we have conducted intensive experiments with comparisons to the related works based on benchmark datasets. The obtained results showed that our method is comparable if not superior to the related works. Thus, the proposed method can be considered as an efficient solver for the WSD task. © 2021 - IOS Press. All rights reserved. |
| format |
Article |
| author |
Hamad, A.H. Mahmood, A.A. Abed, S.A. Ying, X. |
| spellingShingle |
Hamad, A.H. Mahmood, A.A. Abed, S.A. Ying, X. Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm |
| author_sort |
Hamad, A.H. |
| title |
Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm |
| title_short |
Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm |
| title_full |
Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm |
| title_fullStr |
Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm |
| title_full_unstemmed |
Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm |
| title_sort |
semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm |
| publisher |
IOS Press BV |
| publishDate |
2021 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122016027&doi=10.3233%2fJIFS-210934&partnerID=40&md5=d12db3484c46e6d859fc6930d658d274 http://eprints.utp.edu.my/29325/ |
| _version_ |
1741197223862992896 |
| score |
11.62408 |