Statistical field theory for dialectology

TL;DR

This paper introduces a statistical field theory model for linguistic evolution, capturing spatial-temporal dialect changes and enabling future change prediction using large-scale survey data.

Contribution

It develops a novel statistical physics-based model that incorporates social and spatial factors to predict linguistic change at detailed levels.

Findings

Model accurately fits 20th-century US dialect data.

Bias field has a measurable half-life affecting predictability.

Surface tension influences dialect region coarsening.

Abstract

Is it possible to develop a `physics of language' which can explain the spatial, temporal and social patterns we see, and which can predict future change like we forecast the weather? Such a theory is likely to involve ideas from statistical physics. A substantial literature already applies these ideas to language. However, we lack a model which can match the spatial-temporal detail of historical changes at the level of individual linguistic features, and which offers a principled mechanism to predict the future. Here we present a statistical field theory for the evolution of linguistic variables which takes steps to fill this gap. Linguistic variant frequencies are represented as a stochastic state field with spatial interaction and social conformity, coupled to a latent bias field with Onsager Machlup action that reduces overfitting to data. We derive parameter inference procedures and demonstrate them using examples of large-scale dialect survey data from the twentieth century United States. The bias field has a characteristic half-life, which determines the horizon over which linguistic change can be predicted. Inferred model parameters provide evidence for surface-tension-driven coarsening of dialect regions, with population-density gradients exerting systematic forces on interfaces.