A Word-Level Digital Reader of the Prasthanatrayi with Sankara's Bhasya: Corpus, Method, and an Open, Offline Reading Aid for the Advaita Vedanta Canon
Development of an open, fully offline, word-level digital reader for the Prasthanatrayi with Sankara's Bhasya, addressing the linguistic complexity of continuous euphonic combination (sandhi) and dense compounds. Implementation of a hybrid NLP pipeline combining rule-based sandhi splitting with LLM-assisted morphological analysis, verified through an adversarial two-pass protocol and a durable human-review loop. High accuracy achieved, with over 99% agreement between high-confidence analyses and
Analysis
TL;DR
- Development of an open, fully offline, word-level digital reader for the Prasthanatrayi with Sankara's Bhasya, addressing the linguistic complexity of continuous euphonic combination (sandhi) and dense compounds.
- Implementation of a hybrid NLP pipeline combining rule-based sandhi splitting with LLM-assisted morphological analysis, verified through an adversarial two-pass protocol and a durable human-review loop.
- High accuracy achieved, with over 99% agreement between high-confidence analyses and an authoritative inflectional lexicon, while errors are concentrated in low-confidence tiers targeted for human correction.
- Delivery as a single, self-contained HTML file requiring no server or network, functioning simultaneously as a clickable reader and a comprehensive concordance for lemmatized search.
Why It Matters
This project demonstrates a sophisticated application of hybrid AI techniques (rule-based systems plus LLMs) to handle highly complex, low-resource, or structurally unique languages like Sanskrit, where standard tokenization fails due to sandhi and samasa. It provides a replicable model for creating accessible, offline digital humanities tools that preserve scholarly rigor while enhancing usability for students and researchers.
Technical Details
- Corpus Scope: Covers the entire Prasthanatrayi (ten principal Upanishads, Brahmasutra, Bhagavadgita) with Sankara's commentaries, comprising 2,971 verses/sutras/prose sections and 36,881 analyzed word-occurrences in the root text.
- Pipeline Architecture: Utilizes a rule-based sandhi splitter operating over an inflected-form lexicon and attested-corpus look-ups, augmented by LLM-assisted morphological analysis.
- Verification Protocol: Employs an adversarial two-pass verification process and a durable human-review loop where corrections persist across regenerations, ensuring data integrity.
- Performance Metrics: Intrinsic evaluation shows >99% agreement with authoritative lexicons for high-confidence forms; band-blind adjudication reveals predictable quality degradation in low-confidence tiers, which are specifically targeted for review.
- Delivery Format: Distributed as a single self-contained HTML file with no external dependencies, enabling offline access and serving as both a reader and a concordance tool.
Industry Insight
- Hybrid approaches that combine deterministic linguistic rules with generative AI models offer superior reliability for specialized domains (like classical languages) compared to pure LLM solutions, particularly when handling complex morphological structures.
- The "offline-first" and self-contained delivery model presents a viable strategy for distributing sensitive or specialized academic resources without relying on cloud infrastructure, enhancing privacy and accessibility.
- Integrating human-in-the-loop verification into automated pipelines creates a sustainable quality assurance mechanism, allowing AI systems to improve iteratively while maintaining high standards of accuracy for scholarly applications.
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