@inproceedings{hsiao-michaelis-2025-annotating,
    title = "Annotating {E}nglish Verb-Argument Structure via Usage-Based Analogy",
    author = "Hsiao, Allen Minchun  and
      Michaelis, Laura A.",
    editor = "Bonial, Claire  and
      Torgbi, Melissa  and
      Weissweiler, Leonie  and
      Blodgett, Austin  and
      Beuls, Katrien  and
      Van Eecke, Paul  and
      Tayyar Madabushi, Harish",
    booktitle = "Proceedings of the Second International Workshop on Construction Grammars and NLP",
    month = sep,
    year = "2025",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/2025.cxgsnlp-1.16/",
    pages = "158--164",
    ISBN = "979-8-89176-318-0",
    abstract = "This paper introduces a usage-based framework that models argument structure annotation as nearest-neighbor classification over verb{--}argument structure (VAS) embeddings. Instead of parsing sentences separately, the model aligns new tokens with previously observed constructions in an embedding space derived from semi-automatic corpus annotations. Pilot studies show that cosine similarity captures both form and meaning, that nearest-neighbor classification generalizes to dative alternation verbs, and that accuracy in locative alternation depends on the corpus source of exemplars. These results suggest that analogical classification is shaped by both structural similarity and corpus alignment, highlighting key considerations for scalable, construction-based annotation of new sentence inputs."
}