Classifying Evolutionary Forces in Language Change Using Neural Networks

F.B. Karsdorp, Enrique Manjavacas, Lauren Fonteyn, Mike Kestemont

Research output: Contribution to journal/periodicalArticleScientificpeer-review


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 to 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.
Original languageEnglish
Article numbere50
Pages (from-to)1-40
JournalEvolutionary Human Sciences
Publication statusPublished - 2020


  • language change
  • neural network
  • drift
  • selection
  • cultural evolution


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