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%

Multi-component compounding is a prevalent phenomenon in Sanskrit, and understanding the implicit structure of a compound’s components is crucial for deciphering its meaning. Earlier approaches in Sanskrit have focused on binary compounds and neglected the multi-component compound setting. This work introduces the novel task of nested compound type identification (NeCTI), which aims to identify nested spans of a multi-component compound and decode the implicit semantic relations between them. To the best of our knowledge, this is the first attempt in the field of lexical semantics to propose this task. We present 2 newly annotated datasets including an out-of-domain dataset for this task. We also benchmark these datasets by exploring the efficacy of the standard problem formulations such as nested named entity recognition, constituency parsing and seq2seq, etc. We present a novel framework named DepNeCTI: Dependency-based Nested Compound Type Identifier that surpasses the performance of the best baseline with an average absolute improvement of 13.1 points F1-score in terms of Labeled Span Score (LSS) and a 5-fold enhancement in inference efficiency. In line with the previous findings in the binary Sanskrit compound identification task, context provides benefits for the NeCTI task. The codebase and datasets are publicly available at: https://github.com/yaswanth-iitkgp/DepNeCTI

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.

Parsing has been gaining popularity in recent years and attracted the interest of NLP researchers around the world. It is challenging when the language under study is a free-word order language that allows ellipsis like Telugu. In this paper, an attempt is made to parse subordinate clauses especially, non-finite verb clauses and relative clauses in Telugu which are highly productive and constitute a large chunk in parsing tasks. This study adopts a knowledge-driven approach to parse subordinate structures using linguistic cues as rules. Challenges faced in parsing ambiguous structures are elaborated alongside providing enhanced tags to handle them. Results are encouraging and this parser proves to be efficient for Telugu.

The common wisdom about Sanskrit is that it is free word order language. This word order poses challenges such as handling non-projectivity in parsing. The earlier works on the word order of Sanskrit have shown that there are syntactic structures in Sanskrit which cannot be covered under even the non-planarity. In this paper, we study these structures further to investigate if they can fall under well-nestedness or not. A small manually tagged corpus of the verses of Śrīmad-Bhagavad-Gītā was considered for this study. It was noticed that there are as many well-nested trees as there are ill-nested ones. From the linguistic point of view, we could get a list of relations that are involved in the planarity violations. All these relations had one thing in common - that they have unilateral expectancy. It was this loose binding, as against the mutual expectancy with certain other relations, that allowed them to cross the phrasal boundaries.

Computationally analyzing Sanskrit texts requires proper segmentation in the initial stages. There have been various tools developed for Sanskrit text segmentation. Of these, Gérard Huet’s Reader in the Sanskrit Heritage Engine analyzes the input text and segments it based on the word parameters - phases like iic, ifc, Pr, Subst, etc., and sandhi (or transition) that takes place at the end of a word with the initial part of the next word. And it enlists all the possible solutions differentiating them with the help of the phases. The phases and their analyses have their use in the domain of sentential parsers. In segmentation, though, they are not used beyond deciding whether the words formed with the phases are morphologically valid. This paper tries to modify the above segmenter by ignoring the phase details (except for a few cases), and also proposes a probability function to prioritize the list of solutions to bring up the most valid solutions at the top.