@inproceedings{blum-list-2026-using,
    title = "Using Correspondence Patterns to Identify Irregular Words in Cognate Sets Through Leave-One-Out Validation",
    author = "Blum, Frederic  and
      List, Johann-Mattis",
    editor = "Tahmasebi, Nina  and
      Cassotti, Pierluigi  and
      Montariol, Syrielle  and
      Kutuzov, Andrey  and
      Huebscher, Netta  and
      Spaziani, Elena  and
      Baes, Naomi",
    booktitle = "The Proceedings for the 6th International Workshop on Computational Approaches to Language Change ({LC}hange{'}26)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.lchange-1.6/",
    pages = "75--86",
    ISBN = "979-8-89176-362-3",
    abstract = "Regular sound correspondences constitute the principal evidence in historical language comparison. Despite the heuristic focus on regularity, it is often more an intuitive judgement than a quantified evaluation, and irregularity is more common than expected from the Neogrammarian model. Given the recent progress of computational methods in historical linguistics and the increased availability of standardized lexical data, we are now able to improve our workflows and provide such a quantitative evaluation. Here, we present the balanced average recurrence of correspondence patterns as a new measure of regularity. We also present a new computational method that uses this measure to identify cognate sets that lack regularity with respect to their correspondence patterns. We validate the method through two experiments, using simulated and real data. In the experiments, we employ leave-one-out validation to measure the regularity of cognate sets in which one word form has been replaced by an irregular one, checking how well our method identifies the forms causing the irregularity. Our method achieves an overall accuracy of 85{\%} with the datasets based on real data. We also show the benefits of working with subsamples of large datasets and how increasing irregularity in the data influences our results. Reflecting on the broader potential of our new regularity measure and the irregular cognate identification method based on it, we conclude that they could play an important role in improving the quality of existing and future datasets in computer-assisted language comparison."
}