Abstract
Word sense ambiguity comes about the use of lexemes associated with more than one sense. In this research work, an improvement has been proposed and evaluated for our previously developed Assamese Word-Sense Disambiguation (WSD) system where potential outcomes of using semantic features were evaluated up to a limited extent. As semantic relationship information has a good effect in most of the natural language processing (NLP) tasks, in this work, the system is developed based on supervised learning approach using Naïve Bayes classifier with syntactic as well as semantic features. The performance measure of the overall system has been improved up to 91.11% in terms of F1-measure as compared to 86% of the previously developed system by incorporating the Semantically Related Words (SRW) feature in our feature set.
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Borah, P.P., Talukdar, G., Baruah, A. (2019). WSD for Assamese Language. In: Kalita, J., Balas, V., Borah, S., Pradhan, R. (eds) Recent Developments in Machine Learning and Data Analytics. Advances in Intelligent Systems and Computing, vol 740. Springer, Singapore. https://doi.org/10.1007/978-981-13-1280-9_11
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DOI: https://doi.org/10.1007/978-981-13-1280-9_11
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