Classifying evolutionary forces in language change using neural networks

Evolutionary Human Sciences, vol. 2, pp. e50

Abstract

A fundamental problem in research into language and cultural change is the difficulty of distinguishing processes of stochastic drift (also known as neutral evolution) from processes that are subject to selection pressures. In this article, we describe a new technique based on deep neural networks, in which we reformulate the detection of evolutionary forces in cultural change as a binary classification task. Using residual networks for time series trained on artificially generated samples of cultural change, we demonstrate that this technique is able to efficiently, accurately and consistently learn which aspects of the time series are distinctive for drift and selection, respectively. We compare the model with a recently proposed statistical test, the Frequency Increment Test, and show that the neural time series classification system provides a possible solution to some of the key problems associated with this test.

Authors 4

1. Folgert Karsdorp

   Royal Netherlands Academy of Arts and Sciences · Meertens Institute

   Affiliation as printed

   Royal Netherlands Academy of Arts and Sciences, Meertens Institute, Amsterdam, The Netherlands

2. Enrique Manjavacas

   University of Antwerp

   Affiliation as printed

   Department of Literature, University of Antwerp, Antwerp, Belgium

3. Lauren Fonteyn Aachen

   Leiden University

   Affiliation as printed

   Leiden University Centre for Linguistics, Leiden University, Leiden, The Netherlands

4. Mike Kestemont

   University of Antwerp

   Affiliation as printed

   Department of Literature, University of Antwerp, Antwerp, Belgium