Phylogenetic typology

TL;DR

This paper introduces a new method for estimating linguistic variable frequencies that accounts for shared ancestry among languages, using phylogenetic data and Markov models to analyze global word-order correlations.

Contribution

It presents a novel approach combining phylogenetic inference and statistical modeling to analyze linguistic data across diverse language families and isolates.

Findings

Effective control for language relatedness in frequency estimation

Phylogenetic models reveal insights into word-order correlations

Method applicable to large and small language datasets

Abstract

In this article we propose a novel method to estimate the frequency distribution of linguistic variables while controlling for statistical non-independence due to shared ancestry. Unlike previous approaches, our technique uses all available data, from language families large and small as well as from isolates, while controlling for different degrees of relatedness on a continuous scale estimated from the data. Our approach involves three steps: First, distributions of phylogenies are inferred from lexical data. Second, these phylogenies are used as part of a statistical model to statistically estimate transition rates between parameter states. Finally, the long-term equilibrium of the resulting Markov process is computed. As a case study, we investigate a series of potential word-order correlations across the languages of the world.