Bayesian Identification of Cognates and Correspondences

Abstract

This paper presents a Bayesian approach to comparing languages: identifying cognates and the regular correspondences that compose them. A simple model of language is extended to include these notions in an account of parent languages. An expression is developed for the posterior probability of child language forms given a parent language. Bayes' Theorem offers a schema for evaluating choices of cognates and correspondences to explain semantically matched data. An implementation optimising this value with gradient descent is shown to distinguish cognates from non-cognates in data from Polish and Russian.