@inproceedings{aida-etal-2026-statistical,
    title = "Statistical Semantic Change Detection via Usage Similarities",
    author = "Aida, Taichi  and
      Mochihashi, Daichi  and
      Takamura, Hiroya  and
      Ogiso, Toshinobu  and
      Komachi, Mamoru",
    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-1.2/",
    pages = "20--26",
    ISBN = "979-8-89176-362-3",
    abstract = "Semantic change detection comprises two subtasks: classification, which predicts whether a target word has undergone a semantic shift, and ranking, which orders words according to the degree of their semantic change. While most prior studies concentrated on ranking subtask, the classification subtask plays an equally important role, since many practical scenarios require a yes/no decision on semantic change rather than a global ranking. In this work, we propose a novel statistical method that predicts the presence or absence of semantic change. While most existing approaches infer semantic change by comparing word embeddings across time periods or domains, our method directly models the diachronic/synchronic consistency of usage-level similarity scores. Our experiments on SemEval-2020 Task 1 and WUGS datasets demonstrate that the proposed formulation outperforms existing state-of-the-art embedding-based methods, and robustly detects semantic change across languages in both diachronic and synchronic settings."
}