Computer Simulation of Dialect Feature Diffusion

Abstract

This paper describes the independent construction and implementation of two cellular automata that model dialect feature diffusion as the adaptive aspect of the complex system of speech. We show how a feature, once established, can spread across an area, and how the distribution of a dialect feature as it stands in Linguistic Atlas data could either spread or diminish. Cellular automata use update rules to determine the status of a feature at a given location with respect to the status of its neighboring locations. In each iteration all locations in a matrix are evaluated, and then the new status for each one is displayed all at once. Throughout hundreds of iterations, we can watch regional distributional patterns emerge as a consequence of these simple update rules. We validate patterns with respect to the linguistic distributions known to occur in the Linguistic Atlas Project.Nobody has ever doubted, though some people grumble about it, that language always changes over time, and that it changes differently in different places giving rise to what we perceive as language varieties, dialects. Changes in a language get started somewhere, among a few speakers, and then diffuse unevenly across space and time through large populations of speakers. There are no cases in history when a natural language has not done so. And yet we know little about the operation of change in action. Historical linguistics offers us theories about change, such as the chain shift hypothesis, but these theories have most often been proposed top-down from the point of view of abstract language systems, not bottom up from where the speakers talk. In this paper we would like to demonstrate by means of two computer simulations how we believe the process of language change and diffusion operates in populations of speakers. We offer a program called a ''cellular automaton'' (CA) that shows how simple update rules for individual nodes in a matrix can lead to complex behavior from which patterns emerge, patterns of the kind we typically see in data from the Linguistic Atlas Project. The simulation available with the paper will allow users to see for themselves how manipulation of the rules changes the outcomes of the process.

Global Syntactic Variation in Seven Languages: Toward a Computational Dialectology

“…What features do we use for dialectometry? Most previous work relies on phonetic or phonological features (Kretzschmar, 1992 , 1996 ; Heeringa, 2004 ; Labov et al, 2005 ; Nerbonne, 2006 , 2009 ; Grieve et al, 2011 , 2013 ; Wieling and Nerbonne, 2011 , 2015 ; Grieve, 2013 ; Nerbonne and Kretzschmar, 2013 ; Kretzschmar et al, 2014 ; Kruger and van Rooy, 2018 ) for the simple reason that phonetic representations are relatively straight-forward: a vowel is a vowel and the measurements are the same across varieties and languages. Previous work on syntactic variation has focused on either (i) an incomplete set of language-specific variants, ranging from only a few features to hundreds (Sanders, 2007 , 2010 ; Szmrecsanyi, 2009 , 2013 , 2014 ; Grieve, 2011 , 2012 , 2016 ; Collins, 2012 ; Schilk and Schaub, 2016 ; Szmrecsanyi et al, 2016 ; Calle-Martin and Romero-Barranco, 2017 ; Grafmiller and Szmrecsanyi, 2018 ; Tamaredo, 2018 ) or (ii) language-independent representations such as function words (Argamon and Koppel, 2013 ) or sequences of part-of-speech labels (Hirst and Feiguina, 2007 ; Kroon et al, 2018 ).…”

Creation of Regions for Dialect Features Using a Cellular Automaton

“…Some rule sets create chaotic patterns that, if they do repeat themselves, do so after very long cycles of generations. A few rule sets create complex behavior, where stable patterns appear (see Kretzschmar, Juuso & Bailey 2014). The choice of different rules that results in these four classes of outcomes for CAs demonstrates that the definite procedure can be used to solve a variety of problems.…”

“… 7. Our CA is capable of using other rule sets and neighborhood sizes, some of which produce the other output types described by Wolfram (2002), including chaotic and complex behavior. We are interested here in output from the CA that quickly resolves into a set of regions, not in complex behavior as documented in Kretzschmar, Juuso, and Bailey (2014). …”

Quantifying the spatial pattern of dialect words spreading from a central population

“…This model predicts that linguistic features first diffuse from city to city, skipping the rural area in between. Kretzschmar [ 14 ] used cellular automaton (CA) as a computational model to investigate temporal changes in linguistic features across areas. Fagyal et al .…”