@inproceedings{tat-etal-2026-reframe,
    title = "{R}e{FRAME} or Remain: Unsupervised Lexical Semantic Change Detection with Frame Semantics",
    author = "Tat, Bach Phan  and
      Heylen, Kris  and
      De Pascale, Stefano  and
      Geeraerts, Dirk  and
      Speelman, Dirk",
    editor = "Mohammad, Saif M.  and
      Ousidhoum, Nedjma",
    booktitle = "Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*{SEM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.starsem-conference.5/",
    pages = "83--97",
    ISBN = "979-8-89176-413-2",
    abstract = "The majority of contemporary computational methods for lexical semantic change (LSC) detection are based on neural embedding distributional representations. Although these models perform well on LSC benchmarks, their results are often difficult to interpret. We explore an alternative approach that relies solely on frame semantics. We show that this method is effective for detecting semantic change and can even outperform many distributional semantic models. Finally, we present a detailed quantitative and qualitative analysis of its predictions, demonstrating that they are both plausible and highly interpretable."
}