An optimized Lesk-based algorithm for word sense disambiguation

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dc.contributor.authorAyetiran, Eniafe Festus
dc.contributor.authorAgbele, Kehinde
dc.date.accessioned2021-02-02T10:16:06Z
dc.date.available2021-02-02T10:16:06Z
dc.date.issued2018-10-05
dc.description.abstractComputational complexity is a characteristic of almost all Lesk-based algorithms for word sense disambiguation (WSD). In this paper, we address this issue by developing a simple and optimized variant of the algorithm using topic composition in documents based on the theory underlying topic models. The knowledge resource adopted is the English WordNet enriched with linguistic knowledge from Wikipedia and Semcor corpus. Besides the algorithm’s e ciency, we also evaluate its e ectiveness using two datasets; a general domain dataset and domain-speci c dataset. The algorithm achieves a superior performance on the general domain dataset and superior performance for knowledge-based techniques on the domain-specific dataset.en_US
dc.identifier.citationAyetiran, E. F., & Agbele, K. (2016). An Optimized Lesk-Based Algorithm for Word Sense Disambiguation, Open Computer Science, 8(1), 165-172. doi: https://doi.org/10.1515/comp-2018-0015en_US
dc.identifier.issn2299-1093
dc.identifier.urihttps://doi.org/10.1515/comp-2018-0015
dc.identifier.urihttp://repository.elizadeuniversity.edu.ng/handle/20.500.12398/795
dc.language.isoenen_US
dc.publisherDe Gruyter: Open Computer Scienceen_US
dc.subjectoptimized Lesk,en_US
dc.subjectDistributional hypothesis,en_US
dc.subjectTopic compositionen_US
dc.titleAn optimized Lesk-based algorithm for word sense disambiguationen_US
dc.typeArticleen_US
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