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%

Processing and understanding of figurative speech is a challenging task for computers as well as humans. In this paper, we present a case of Upamā alaṅkāra (simile). The verbal cognition of the Upamā alaṅkāra by a human is presented as a dependency tree, which involves the identification of various components such as upamāna (vehicle), upameya (topic), sādhāran.a-dharma (common property) and upamādyotaka (word indicating similitude). This involves the repetition of elliptical elements. Further, we show, how the same dependency tree may be represented without any loss of information, even without repetition of elliptical elements. Such a representation would be useful for the computational processing of the alaṅkāras.