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Machine Learning Approach for Kashmiri Word Sense Disambiguation

Machine Learning Approach for Kashmiri Word Sense Disambiguation

Aadil Ahmad Lawaye, Tawseef Ahmad Mir, Mahmood Hussain Mir, Ghayas Ahmed
Copyright: © 2024 |Pages: 24
ISBN13: 9798369307281|ISBN13 Softcover: 9798369346037|EISBN13: 9798369307298
DOI: 10.4018/979-8-3693-0728-1.ch006
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MLA

Lawaye, Aadil Ahmad, et al. "Machine Learning Approach for Kashmiri Word Sense Disambiguation." Empowering Low-Resource Languages With NLP Solutions, edited by Partha Pakray, et al., IGI Global, 2024, pp. 113-136. https://doi.org/10.4018/979-8-3693-0728-1.ch006

APA

Lawaye, A. A., Mir, T. A., Mir, M. H., & Ahmed, G. (2024). Machine Learning Approach for Kashmiri Word Sense Disambiguation. In P. Pakray, P. Dadure, & S. Bandyopadhyay (Eds.), Empowering Low-Resource Languages With NLP Solutions (pp. 113-136). IGI Global. https://doi.org/10.4018/979-8-3693-0728-1.ch006

Chicago

Lawaye, Aadil Ahmad, et al. "Machine Learning Approach for Kashmiri Word Sense Disambiguation." In Empowering Low-Resource Languages With NLP Solutions, edited by Partha Pakray, Pankaj Dadure, and Sivaji Bandyopadhyay, 113-136. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-0728-1.ch006

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Abstract

Studying the senses of words in a given data is crucial for analysing and understanding natural languages. The meaning of an ambiguous word varies based on the context of usage and identifying its correct meaning in the given situation is a famous problem known as word sense disambiguation (WSD) in natural language processing (NLP). In this chapter, the authors discuss the important WSD research works carried out in the context of different languages using different techniques. They also explore a supervised approach based on the hidden Markov model (HMM) to address the WSD problem in the Kashmiri language, which lacks research in the NLP domain. The performance of the proposed approach is also examined in detail along with future improvement directions. The average results produced by the proposed system are accuracy=72.29%, precision=0.70, recall= 0.70, and F1-measure=0.70.

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