@inproceedings{kulcsar-etal-2025-extracting,
    title = "Extracting Behaviors from {G}erman Clinical Interviews in Support of Autism Spectrum Diagnosis",
    author = "Kulcsar, Margareta A.  and
      Grant, Ian Paul  and
      Poesio, Massimo",
    editor = "Evang, Kilian  and
      Kallmeyer, Laura  and
      Pogodalla, Sylvain",
    booktitle = "Proceedings of the 16th International Conference on Computational Semantics",
    month = sep,
    year = "2025",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/2025.iwcs-1.15/",
    pages = "153--165",
    ISBN = "979-8-89176-316-6",
    abstract = "Accurate identification of behaviors is essential for diagnosing developmental disorders such as Autism Spectrum Disorder (ASD). We frame the extraction of behaviors from text as a specialized form of event extraction grounded in the TimeML framework and evaluate two approaches: a pipeline model and an end-to-end model that directly extracts behavior spans from raw text. We introduce two novel datasets: a new clinical annotation of an existing Reddit corpus of parent-authored posts in English and a clinically annotated corpus of German ASD diagnostic interviews. On the English dataset, the end-to-end BERT model achieved an F1 score of 73.4{\%} in behavior classification, outperforming the pipeline models (F1: 66.8{\%} and 53.65{\%}). On the German clinical dataset, the end-to-end model reached an even higher F1 score of 80.1{\%}, again outperforming the pipeline (F1: 78.7{\%}) and approaching the gold-annotated upper bound (F1: 92.9{\%}). These results demonstrate that behavior classification benefits from direct extraction, and that our method generalizes across domains and languages."
}