Please use this identifier to cite or link to this item: http://repository.elizadeuniversity.edu.ng/handle/20.500.12398/119
Title: An optimized Lesk-based algorithm for word sense disambiguation
Authors: Ayetiran, Eniafe F.
Agbele, Kehinde K.
Keywords: Lesk
optimized
hypothesis
distributional
composition
topic
Issue Date: 24-Aug-2018
Publisher: De Gruyter
Citation: Ayetiran, E. F., & Agbele, K. (2016). An optimized Lesk-based algorithm for word sense disambiguation. Open Computer Science, 8(1), 165-172.
Abstract: Computational 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 eficiency, we also evaluate its efectiveness using two datasets; a general domain dataset and domain-specific dataset. The algorithm achieves a superior performance on the general domain dataset and superior performance for knowledge-based techniques on the domain-specific dataset.
URI: https://doi.org/10.1515/comp-2018-0015
http://repository.elizadeuniversity.edu.ng/handle/20.500.12398/119
Appears in Collections:Research Articles

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