Synthesizing Sanskrit Linguistics with NLP Models for Ancient Text Semantic Analysis

The Convergence of Ancient Grammar and Modern Computational Linguistics

Sanskrit, one of the oldest and most structurally precise languages, presents a unique opportunity for Natural Language Processing (NLP) models. Its highly systematic grammatical framework, as defined by Pāṇini's Aṣṭādhyāyī, offers a rule-based structure that can be leveraged to improve semantic analysis, machine translation, and text decoding of ancient manuscripts.

The Structural Advantages of Sanskrit for NLP

Sanskrit's grammar is built upon:

• Morphological Richness: Highly inflected forms with precise declensions and conjugations.
• Context-Free Syntax: Word order is flexible due to strong inflectional markers.
• Sandhi Rules: Phonetic merging of words at boundaries, requiring disambiguation.
• Compound Formation (Samāsa): Complex word compounding that can encode entire phrases into single terms.

Challenges in Traditional NLP Approaches

Modern NLP models trained on contemporary languages struggle with Sanskrit due to:

• Limited parallel corpora for machine translation.
• High morphological variability leading to data sparsity.
• Lack of pre-trained embeddings optimized for ancient linguistic structures.

Enhancing NLP Models with Sanskrit Grammar

By integrating Sanskrit's grammatical rules into neural architectures, models can achieve higher accuracy in:

• Morphological Segmentation: Splitting compound words using vṛtti analysis.
• Dependency Parsing: Leveraging case markers (vibhakti) for syntactic relationships.
• Semantic Role Labeling: Utilizing kāraka theory to identify agent, object, and instrument roles.

Case Study: Sandhi Splitting with Transformer Models

A hybrid approach combining rule-based Sandhi disambiguation with a fine-tuned BERT model achieved a 92% F1-score in splitting merged words in the Rigveda, compared to 78% using purely statistical methods (based on published research from the University of Hyderabad).

Building a Sanskrit-Optimized Neural Architecture

A proposed architecture for Sanskrit NLP includes:

1. Preprocessing Layer: Sandhi-splitter and compound decomposer.
2. Embedding Layer: Position-sensitive morpheme embeddings.
3. Grammar-Informed Attention: Attention heads weighted by kāraka role priorities.
4. Rule-Constrained Decoding: Output filtered through Pāṇinian production rules.

Training Data Requirements

Effective models require:

• Digitized versions of commentaries (bhāṣya) for supervised learning.
• Parallel corpora of multiple translations (e.g., Wilson vs. Griffith Rigveda translations).
• Synthetically generated samples using grammar-based mutation.

The Future of Computational Philology

Beyond machine translation, these techniques enable:

• Authorship Attribution: Detecting stylistic patterns across centuries.
• Textual Reconstruction: Predicting lacunae in damaged manuscripts.
• Cognitive Modeling: Testing how ancient scholars processed linguistic complexity.

A Vision of the Digital Pāṇini

Imagine an AI that doesn't merely process Sanskrit but applies meta-rules (paribhāṣā) to generate new, grammatically perfect interpretations of Vedic mantras – not as a statistical approximation, but as a computational embodiment of the Aṣṭādhyāyī itself.

Implementation Roadmap

A phased development approach would involve:

1. Phase 1 (12 months): Build a Sandhi-aware tokenizer with 95%+ accuracy on classical texts.
2. Phase 2 (18 months): Train a kāraka-role prediction model using dependency treebanks.
3. Phase 3 (24 months): Develop a generative model constrained by Pāṇinian production rules.

Ethical Considerations

Key challenges include:

• Avoiding algorithmic bias in interpreting philosophical texts.
• Preserving traditional interpretative schools (śākhā) in digital representations.
• Validating outputs against living oral traditions.