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...

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Veröffentlicht in:Journal of intelligent & fuzzy systems 2021-01, Vol.41 (6), p.7047-7061
Hauptverfasser: Hamad, Aws Hamed, Mahmood, Ali Abdulkareem, Abed, Saad Adnan, Ying, Xu
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container_issue 6
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container_title Journal of intelligent & fuzzy systems
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creator Hamad, Aws Hamed
Mahmood, Ali Abdulkareem
Abed, Saad Adnan
Ying, Xu
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.
doi_str_mv 10.3233/JIFS-210934
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subjects Algorithms
Heuristic methods
Information content
Information retrieval
Machine translation
Search algorithms
Semantics
Summarization
Swarm intelligence
Word sense disambiguation
title Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm
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