@inproceedings{tat-etal-2026-transparent,
    title = "Transparent Semantic Change Detection with Dependency-Based Profiles",
    author = "Tat, Bach Phan  and
      Heylen, Kris  and
      Geeraerts, Dirk  and
      De Pascale, Stefano  and
      Speelman, Dirk",
    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-1.8/",
    pages = "97--109",
    ISBN = "979-8-89176-362-3",
    abstract = "Most modern computational approaches to lexical semantic change detection (LSC) rely on embedding-based distributional word representations with neural networks. Despite the strong performance on LSC benchmarks, they are often opaque. We investigate an alternative method which relies purely on dependency co-occurrence patterns of words. We demonstrate that it is effective for semantic change detection and even outperforms a number of distributional semantic models. We provide an in-depth quantitative and qualitative analysis of the predictions, showing that they are plausible and interpretable."
}