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    <titleInfo>
        <title>Automatic Sanskrit Poetry Classification Based on Kāvyaguṇa</title>
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        <namePart type="given">Amruta</namePart>
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            <title>Proceedings of the 21st International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Sobha</namePart>
            <namePart type="family">Lalitha Devi</namePart>
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        <name type="personal">
            <namePart type="given">Karunesh</namePart>
            <namePart type="family">Arora</namePart>
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    <abstract>Kāvyaguṇa denotes the syntactic and phonetic attributes or qualities of Sanskrit poetry that enhance its artistic appeal, commonly classified into three categories: Mādhyurya (Sweetness), Oja (Floridity), and Prasāda (Lucidity). This paper presents the Kāvyaguṇa Classifier, a machine learning module, designed to classify Sanskrit literary texts into three distinct guṇas, by employing a diverse range of machine learning algorithms, including Random Forest, Gradient Boosting, XGBoost, Multi-Layer Perceptron and Support Vector Machine. For vectorization, we employed two methods: the neural network-based Word2vec and a custom feature engineering approach grounded in the theoretical understanding of Kāvyaguṇas as described in Sanskrit poetics. The feature engineering model significantly outperformed, achieving an accuracy of up to 90.6%</abstract>
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        <url>https://aclanthology.org/2024.icon-1.13/</url>
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        <date>2024-12</date>
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%0 Conference Proceedings
%T Automatic Sanskrit Poetry Classification Based on Kāvyaguṇa
%A Barbadikar, Amruta
%A Kulkarni, Amba
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F barbadikar-kulkarni-2024-automatic
%X Kāvyaguṇa denotes the syntactic and phonetic attributes or qualities of Sanskrit poetry that enhance its artistic appeal, commonly classified into three categories: Mādhyurya (Sweetness), Oja (Floridity), and Prasāda (Lucidity). This paper presents the Kāvyaguṇa Classifier, a machine learning module, designed to classify Sanskrit literary texts into three distinct guṇas, by employing a diverse range of machine learning algorithms, including Random Forest, Gradient Boosting, XGBoost, Multi-Layer Perceptron and Support Vector Machine. For vectorization, we employed two methods: the neural network-based Word2vec and a custom feature engineering approach grounded in the theoretical understanding of Kāvyaguṇas as described in Sanskrit poetics. The feature engineering model significantly outperformed, achieving an accuracy of up to 90.6%
%U https://aclanthology.org/2024.icon-1.13/
%P 109-119

Markdown (Informal)

[Automatic Sanskrit Poetry Classification Based on Kāvyaguṇa](https://aclanthology.org/2024.icon-1.13/) (Barbadikar & Kulkarni, ICON 2024)

• Automatic Sanskrit Poetry Classification Based on Kāvyaguṇa (Barbadikar & Kulkarni, ICON 2024)

ACL

• Amruta Barbadikar and Amba Kulkarni. 2024. Automatic Sanskrit Poetry Classification Based on Kāvyaguṇa. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 109–119, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).