@inproceedings{colli-battistelli-2026-polysemy,
    title = "Polysemy and Ambiguity: The Case of the {F}rench Modal Verb Devoir",
    author = "Colli, Anna  and
      Battistelli, Delphine",
    editor = "Bunt, Harry",
    booktitle = "Proceedings of the 22nd Joint {ACL} - {ISO} Workshop on Interoperable Semantic Annotation and Representation ({ISA}-22) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/revision-previews/2026.isa-1.2/",
    doi = "10.63317/4jddt6ku3yck",
    pages = "13--21",
    abstract = "This article focus on a methodology for representing the semantics of polysemous markers whose meanings cannot (or do not have to) be disambiguated, even in context. We name this task (multi-)sense representation and present here the French modal verb devoir as a case study. Specifically, we reframe this task {---} traditionally treated as a multi-class problem {---} as a multi-label classification problem to account for instances that remain ambiguous due to contextual and intentional factors. In order to fine-tune our model (CamemBERT), we implement an active learning loop to enhance the annotation process and we demonstrate that combining global and local features yields the best results (F1-micro = 0.83; F1-macro = 0.79). The model is then applied on two distinct corpora, showing that the automatic analysis of devoir{'}s modal senses provides deeper insights into modal verb usage and facilitates comparisons across corpora differing in medium (spoken vs. written) or genre (e.g. legal discourse). Furthermore, our multi-label approach enables the detection and analysis of double-labeled instances, offering valuable applications, as for example legal discourse interpretation and second language acquisition."
}