@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://aclanthology.org/2025.iwcs-main.14/",
pages = "143--155",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Extracting Behaviors from German Clinical Interviews in Support of Autism Spectrum Diagnosis
%A Kulcsar, Margareta A.
%A Grant, Ian Paul
%A Poesio, Massimo
%Y Evang, Kilian
%Y Kallmeyer, Laura
%Y Pogodalla, Sylvain
%S Proceedings of the 16th International Conference on Computational Semantics
%D 2025
%8 September
%I Association for Computational Linguistics
%C Düsseldorf, Germany
%@ 979-8-89176-316-6
%F kulcsar-etal-2025-extracting
%X 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.
%U https://aclanthology.org/2025.iwcs-main.14/
%P 143-155
Markdown (Informal)
[Extracting Behaviors from German Clinical Interviews in Support of Autism Spectrum Diagnosis](https://aclanthology.org/2025.iwcs-main.14/) (Kulcsar et al., IWCS 2025)
ACL