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        <title>Word Sense Disambiguation for Marathi language using Supervised Learning</title>
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        <namePart type="given">Gulati</namePart>
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            <title>Proceedings of the 20th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Pawar</namePart>
            <namePart type="family">Jyoti</namePart>
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            <namePart type="given">Lalitha</namePart>
            <namePart type="given">Devi</namePart>
            <namePart type="family">Sobha</namePart>
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    <abstract>The task of disambiguating word senses, often referred to as Word Sense Disambiguation (WSD), is a substantial difficulty in the realm of natural language processing. Marathi is widely acknowledged as a language that has a relatively restricted range of resources. Consequently, there has been a paucity of academic research undertaken on the Marathi language. There has been little research conducted on supervised learning for Marathi Word Sense Disambiguation (WSD) mostly owing to the scarcity of sense-annotated corpora. This work aims to construct a sense-annotated corpus for the Marathi language and further use supervised learning classifiers, such as Naïve Bayes, Support Vector Machine, Random Forest, and Logistic Regression, to disambiguate polysemous words in Marathi. The performance of these classifiers is evaluated.</abstract>
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        <url>https://aclanthology.org/2023.icon-1.76</url>
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%0 Conference Proceedings
%T Word Sense Disambiguation for Marathi language using Supervised Learning
%A Rasika, Ransing
%A Archana, Gulati
%Y Jyoti, D. Pawar
%Y Sobha, Lalitha Devi
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F rasika-archana-2023-word
%X The task of disambiguating word senses, often referred to as Word Sense Disambiguation (WSD), is a substantial difficulty in the realm of natural language processing. Marathi is widely acknowledged as a language that has a relatively restricted range of resources. Consequently, there has been a paucity of academic research undertaken on the Marathi language. There has been little research conducted on supervised learning for Marathi Word Sense Disambiguation (WSD) mostly owing to the scarcity of sense-annotated corpora. This work aims to construct a sense-annotated corpus for the Marathi language and further use supervised learning classifiers, such as Naïve Bayes, Support Vector Machine, Random Forest, and Logistic Regression, to disambiguate polysemous words in Marathi. The performance of these classifiers is evaluated.
%U https://aclanthology.org/2023.icon-1.76
%P 754-759

Markdown (Informal)

[Word Sense Disambiguation for Marathi language using Supervised Learning](https://aclanthology.org/2023.icon-1.76) (Rasika & Archana, ICON 2023)

• Word Sense Disambiguation for Marathi language using Supervised Learning (Rasika & Archana, ICON 2023)

ACL

• Ransing Rasika and Gulati Archana. 2023. Word Sense Disambiguation for Marathi language using Supervised Learning. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 754–759, Goa University, Goa, India. NLP Association of India (NLPAI).