This research introduces a novel quantitative framework for analyzing the internal chronology of the Rigveda, the oldest substantial text in the Indo-European family. While a broad scholarly consensus has existed for over a century regarding the text's "Old" and "New" layers, this study provides the first multi-dimensional computational validation of that structure. Key Technical Contributions: Corpus Engineering: Development of a normalized SQLite corpus database comprising 227,403 verses across 39 Hindu and Avestan scriptures. Vedic NLP Tools: Implementation of a high-precision accent-mark stripping methodology for Devanagari pattern matching and a context-aware disambiguation protocol to eliminate false positives in low-frequency vocabulary (e.g., distinguishing "spoke" from "enmity"). Composite Stratigraphy: Profiling of the ten Rigvedic mandalas across 12 vocabulary indicators from five semantic domains (geography, military, theology, economy, and technology). Principal Findings: The algorithm recovers the established Oldenberg (1888) classification with zero errors, but further reveals that the text represents a continuous gradient rather than discrete clusters. By tracing the Sarasvati River through this gradient—from a mighty "mountain-to-ocean" river in the most archaic layers to a cataloged name in the latest—the paper demonstrates a single, evolving civilizational transformation recorded across all semantic dimensions. This work provides the methodology and open-source data for scholars to extend quantitative analysis to the broader Vedic and Indo-European corpus.
Tarak Parikh (Tue,) studied this question.