COMPUTATIONAL APPROACHES TO SANSKRIT GRAMMAR USING ARTIFICIAL INTELLIGENCE

Abstract

Sanskrit is one of the world's oldest and most scientifically structured languages, possessing a rich grammatical tradition that has influenced linguistic studies for centuries. The grammatical framework developed by Pāṇini in the Aṣṭādhyāyī represents one of the most sophisticated systems of linguistic analysis ever created. Comprising nearly 4,000 concise grammatical rules (sūtras), the Paninian system provides a formal and systematic method for describing Sanskrit phonology, morphology, syntax, and semantics. Due to its rule-based nature, Sanskrit grammar offers significant opportunities for computational modeling and artificial intelligence applications. In recent years, advances in Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) have created new possibilities for automating Sanskrit language analysis and preserving its vast literary heritage. Computational approaches to Sanskrit grammar aim to develop intelligent systems capable of processing Sanskrit texts, identifying grammatical structures, analyzing word formations, and generating linguistic interpretations. The complexity of Sanskrit grammar, including Sandhi (euphonic combinations), Samāsa (compound formation), Vibhakti (case endings), and intricate syntactic relationships, presents unique challenges for computational analysis. Traditional manual interpretation requires extensive expertise and considerable time, making automated solutions increasingly important for linguistic research, education, and digital humanities initiatives. Artificial Intelligence techniques provide powerful tools for addressing these challenges. NLP algorithms can perform tokenization, morphological analysis, syntactic parsing, and semantic interpretation of Sanskrit texts. Machine learning models can learn grammatical patterns from annotated corpora and improve linguistic analysis accuracy. Deep learning architectures further enhance the ability to recognize complex grammatical structures and contextual relationships within Sanskrit literature. These technologies facilitate the development of intelligent systems capable of supporting Sanskrit learning, digital text processing, machine translation, and automated grammatical analysis. This study explores computational approaches to Sanskrit grammar using Artificial Intelligence and proposes an AI-based framework for automated grammatical analysis. The framework integrates Sanskrit text preprocessing, morphological analysis, Paninian rule processing, machine learning classification, and grammar interpretation modules. Performance evaluation is conducted using standard computational linguistics metrics to assess system effectiveness. The findings are expected to demonstrate that AI technologies can significantly enhance Sanskrit language processing while preserving linguistic accuracy and grammatical authenticity. The research contributes to the fields of Sanskrit studies, computational linguistics, digital humanities, and artificial intelligence by promoting innovative approaches to the preservation and analysis of classical linguistic knowledge.