Abstract
Word Sense Disambiguation (WSD) is the field that seeks to determine the correct sense of a word in a given context. In this paper, we present a WSD method based on random walks over a dependency tree, whose nodes are word-senses from the WordNet. Besides, our method incorporates prior knowledge about the frequency of use of the word-senses. We observed that our results outperform several graph-based WSD methods in All-Word task of SensEval-2 and SensEval-3, including the baseline, where the nouns and verbs part-of-speech show the better improvement in their F-measure scores.
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Acknowledgments
For this study, the authors acknowledge the support of the “Programa Nacional de Innovación para la Competitividad y Productividad”, Perú, under the contract 124-PNICP-PIAP-2015.
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Sobrevilla-Cabezudo, M.A., Oncevay-Marcos, A., Melgar, A. (2018). SenseDependency-Rank: A Word Sense Disambiguation Method Based on Random Walks and Dependency Trees. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_15
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DOI: https://doi.org/10.1007/978-3-319-77113-7_15
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