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Article published In: Linguistics in the Netherlands 2025
Edited by Kristel Doreleijers, Remco Knooihuizen and Eva van Lier
[Nota Bene 2:2] 2025
► pp. 448–473

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Automatic dialect classification of the Southern Dutch dialects

ORCiD logo with link | Leiden University
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Available under the Creative Commons Attribution (CC BY) 4.0 license.

For any use beyond this license, please contact the publisher at rights@benjamins.nl.

Open Access publication of this article was funded through a Transformative Agreement with Leiden University.

Published online: 31 October 2025
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https://doi.org/10.1075/nb.00043.sun
Abstract
Since the 1980s, computational methods have been introduced to dialectology (known as dialectometry, cf. Goebl, Hans. 1984. Dialektometrische studien: Anhand italoromanischer, raetoromanischer und galloromanischer sprachmaterialien aus AIS und ALF. (Beihefte zur Zeitschrift für romanische Philologie 191–193). Niemeyer, Tübingen.Google Scholar logo with link to Google Scholar, Heeringa, Wilbert. 2004. Measuring dialect pronunciation using Levenshtein distance. (PhD thesis, University of Groningen).). Many of these methods were designed for data from dialect surveys or linguistic atlases, typically elicited items uttered in isolation. Scholars have turned to corpus-based approaches to seek dialect patterns from more naturalistic speech, which can tell us more about the context and magnitude of the variants used (Kuparinen, Olli & Yves Scherrer. 2024. Corpus-based dialectometry with topic models. Journal of Linguistic Geography 12(1). 1–12. Google Scholar logo with link to Google Scholar).
Transcriptions of spontaneous speech pose challenges for traditional approaches to automatic dialect classification: it is impossible to go through all the transcriptions manually; these are not systematic word lists; and we should not only extract the frequency of some known features, as we might overlook features that are not yet discovered.
This paper employs topic modelling to automatically detect dialect groups in the southern Dutch dialects. This method is data-driven and can overcome the issues mentioned above. The result shows that southern Dutch dialects can be divided into 2 to 4 major groups, coinciding with the traditional classification (Taeldeman, Johan. 2001. De regenboog van de Vlaamse dialecten. In Johan Taeldeman, Magda Devos & Johan De Caluwe (eds.), Het taallandschap in Vlaanderen, 49–58. Ghent: Academia Press.Google Scholar logo with link to Google Scholar).
Article outline
  • 1.Introduction
  • 2.Traditional dialect classification
    • 2.1Dialect classification with characteristic features and isoglosses
    • 2.2Characteristics and problems with the isogloss approach
  • 3.Quantitative approaches to dialect classification
    • 3.1An aggregate perspective to dialect variation
    • 3.2Atlas-based methods in dialectometry
    • 3.3Corpus-based approaches in dialectometry
  • 4.Challenges and research questions
    • 4.1Challenges to corpus-based dialectometry
    • 4.2Research questions
  • 5.Data and methodology
    • 5.1Data
    • 5.2Methodology
      • 5.2.1Pre-processing of the data
      • 5.2.2Topic modelling
      • 5.2.3Evaluation criteria and model comparison
  • 6.Results
    • 6.1Model comparison
    • 6.2Dialect classification
      • 6.2.1Two-group division
      • 6.2.2Three-group division
      • 6.2.3Four-group division
  • 7.Discussion and conclusion
  • Supplementary material
  • Acknowledgements
  • Notes
  • References
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