@inproceedings{radchankava-2026-using,
    title = "Using {BERT} to Explore Lexical Semantic Change of Prepositions",
    author = "Radchankava, Liudmila",
    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-1.10/",
    pages = "124--130",
    ISBN = "979-8-89176-362-3",
    abstract = "This paper presents a semi-supervised approach to investigating lexical semantic change in English prepositions using contextualized word embeddings from BERT. Due to their hybrid lexico-grammatical nature and high degree of polysemy, prepositions have received limited attention in computational studies of semantic change. We address this gap by first applying BERT-based embeddings in combination with a k-nearest neighbors classifier to the task of preposition sense disambiguation, achieving competitive performance without relying on external lexical resources. The trained model is then applied to diachronic data from the Corpus of Historical American English to analyze semantic change over time. By measuring classifier confidence and correlating it with usage year, we detect systematic differences between simple and compound prepositions. Our results confirm linguistic hypotheses that simple prepositions remain largely semantically stable, while compound prepositions exhibit measurable semantic change. The study demonstrates that BERT embeddings provide an effective tool for exploring diachronic semantic phenomena in functionally complex word classes and can be extended to other languages and datasets."
}