@inproceedings{miletic-schulte-im-walde-2025-modeling,
    title = "Modeling the Evolution of {E}nglish Noun Compounds with Feature-Rich Diachronic Compositionality Prediction",
    author = "Mileti{\'c}, Filip  and
      Schulte Im Walde, Sabine",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.984/",
    pages = "20071--20092",
    ISBN = "979-8-89176-251-0",
    abstract = "We analyze the evolution of English noun compounds, which we represent as vectors of time-specific values. We implement a wide array of methods to create a rich set of features, using them to classify compounds for present-day compositionality and to assess the informativeness of the corresponding linguistic patterns. Our best results use BERT {--} reflecting the similarity of compounds and sentence contexts {--} and we further capture relevant and complementary information across approaches. Leveraging these feature differences, we find that the development of low-compositional meanings is reflected by a parallel drop in compositionality and sustained semantic change. The same distinction is echoed in transformer processing: compositionality estimates require far less contextualization than semantic change estimates."
}