Citation :

Vinto, Neeru Mago, Raj Kumari, "Enhancing Hindi Word Sense Disambiguation Using Supervised Logistic Regression and Contextual Features," International Journal of Electronics and Communication Engineering, vol. 13, no. 7, pp. 51-61, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I7P104

Abstract

Natural languages are ambiguous by nature. Word sense ambiguity is one of the numerous layers of ambiguity. In many applications of natural language processing, sense ambiguity resolution is essential. In this work, word sense ambiguity is addressed, and a supervised method for Hindi word sense disambiguation has been suggested. A supervised method for Hindi word sense disambiguation is employed, incorporating contextual feature modeling and systematic preprocessing to effectively resolve ambiguity. After applying tokenization, POS tagging, stop-word removal, and Lemmatization, a context window is constructed over open-class words. A logistic regression model is used, and to extract feature TF-IDF technique is used. An average accuracy of 78.93% is shown by experimental findings on 20 polysemous Hindi words, which is higher than previous published work on the same dataset.

Keywords

Hindi Language, Word Sense Disambiguation, Supervised techniques, Knowledge-based, Unsupervised techniques.

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