Word sense disambiguation using hybrid swarm intelligence approach

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. (Published)

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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.

Item Type: Article
Uncontrolled Keywords: Word sense disambiguation (WSD); particle swarm optimization (PSO); Hybrid
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Noorul Farina Arifin
Date Deposited: 03 Jan 2019 01:19
Last Modified: 03 Jan 2019 01:19
URI: http://umpir.ump.edu.my/id/eprint/23586
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