Evolution of the lexicon: a probabilistic point of view

TL;DR

This paper analyzes the probabilistic limits of estimating language divergence times using lexicon evolution, considering both word replacement and gradual lexical modifications, highlighting their impact on accuracy.

Contribution

It introduces a probabilistic framework that accounts for both word replacement and lexical modification, improving the understanding of language divergence estimation.

Findings

Limits on accuracy due to probabilistic nature of word replacement

Gradual lexical modifications significantly influence language evolution

Incorporating lexical modifications enhances temporal separation estimates

Abstract

The Swadesh approach for determining the temporal separation between two languages relies on the stochastic process of words replacement (when a complete new word emerges to represent a given concept). It is well known that the basic assumptions of the Swadesh approach are often unrealistic due to various contamination phenomena and misjudgments (horizontal transfers, variations over time and space of the replacement rate, incorrect assessments of cognacy relationships, presence of synonyms, and so on). All of this means that the results cannot be completely correct. More importantly, even in the unrealistic case that all basic assumptions are satisfied, simple mathematics places limits on the accuracy of estimating the temporal separation between two languages. These limits, which are purely probabilistic in nature and which are often neglected in lexicostatistical studies, are analyzed in detail in this article. Furthermore, in this work we highlight that the evolution of a language's lexicon is also driven by another stochastic process: gradual lexical modification of words. We show that this process equally also represents a major contribution to the reshaping of the vocabulary of languages over the centuries and we also show, from a purely probabilistic perspective, that taking into account this second random process significantly increases the precision in determining the temporal separation between two languages.