Dating Sanskrit texts using linguistic features and neural networks

Abstract

Abstract Deriving historical dates or datable stratifications for texts in Classical Sanskrit, such as the epics Mahābhārata and Rāmāyaṇa, is a considerable challenge for text-historical research. This paper provides empirical evidence for subtle but noticeable diachronic changes in the fundamental linguistic structures of Classical Sanskrit, and argues that Classical Sanskrit shows enough diachronic variation for dating texts on the basis of linguistic developments. Building on this evidence, it evaluates machine learning algorithms that predict approximate dates of composition for Sanskrit texts. The paper introduces the required background, discusses the relevance of linguistic features for temporal classification, and presents a text-historical evaluation of Book 6 of the Mahābhārata, whose historical stratification is disputed in Indological research.