Abstract
Word Sense Disambiguation (WSD) is a problem of figuring out the correct sense of a word in a given context. We introduce an unsupervised knowledge-source approach for word sense disambiguation using a bee colony optimization algorithm that is constructive in nature. Our algorithm, using WordNet, optimizes the search space by globally disambiguating a document by constructively determining the sense of a word using the previously disambiguated words. Heuristic methods for unsupervised word sense disambiguation mostly give less importance to the context words while determining the sense of the target word. In this paper, we put more emphasis on the context and the part of speech of a word while determining its correct sense. We make use of a modified simplified Lesk algorithm as a relatedness measure. Our approach is then compared with recent unsupervised heuristics such as ant colony optimization, genetic algorithms, and simulated annealing, and shows promising results. We finally introduce a voting strategy to our algorithm that ends up further improving our results.
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Kumar, S., El Ariss, O. (2018). Word Sense Disambiguation Using Swarm Intelligence: A Bee Colony Optimization Approach. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9623. Springer, Cham. https://doi.org/10.1007/978-3-319-75477-2_34
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DOI: https://doi.org/10.1007/978-3-319-75477-2_34
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