ArticleEvolutionary human sciences2020
Classifying evolutionary forces in language change using neural networks.
Article in Evolutionary human sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Slovotvir: Grassroots reform of the Ukrainian language reveals the role of drift and selection in language change.PNAS nexus · 2026Article
- The interplay between age structure and cultural transmission.PLoS computational biology · 2023Article
- Drift as a Driver of Language Change: An Artificial Language Experiment.Cognitive science · 2022Article
- How individuals change language.PloS one · 2021Article
- Classifying evolutionary forces in language change using neural networks.Evolutionary human sciences · 2020Article
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4 authors.
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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.
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