@inproceedings{van-noort-etal-2025-mapping,
    title = "Mapping semantic networks to {D}utch word embeddings as a diagnostic tool for cognitive decline",
    author = "van Noort, Maithe  and
      Korenar, Michal  and
      Bloem, Jelke",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-main.1560/",
    doi = "10.18653/v1/2025.emnlp-main.1560",
    pages = "30632--30647",
    ISBN = "979-8-89176-332-6",
    abstract = "We explore the possibility of semantic networks as a diagnostic tool for cognitive decline by using Dutch verbal fluency data to investigate the relationship between semantic networks and cognitive health. In psychology, semantic networks serve as abstract representations of the semantic memory system. Semantic verbal fluency data can be used to estimate said networks. Traditionally, this is done by counting the number of raw items produced by participants in a verbal fluency task. We used static and contextual word embedding models to connect the elicited words through semantic similarity scores, and extracted three network distance metrics. We then tested how well these metrics predict participants' cognitive health scores on the Mini-Mental State Examination (MMSE). While the significant predictors differed per model, the traditional number-of-words measure was not significant in any case. These findings suggest that semantic network metrics may provide a more sensitive measure of cognitive health than traditional scoring."
}