@inproceedings{a-rodriguez-etal-2025-famwa,
    title = "{FAMWA}: A new taxonomy for classifying word associations (which humans improve at but {LLM}s still struggle with)",
    author = "A. Rodriguez, Maria  and
      Candito, Marie  and
      Huyghe, Richard",
    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/author-page-yu-wang-polytechnic/2025.iwcs-main.17/",
    pages = "175--188",
    ISBN = "979-8-89176-316-6",
    abstract = "Word associations have a longstanding tradition of being instrumental for investigating the organization of the mental lexicon. Despite their wide application in psychology and psycholinguistics, analyzing word associations remains challenging due to their inherent heterogeneity and variability, shaped by linguistic and extralinguistic factors. Existing word-association taxonomies often suffer limitations due to a lack of comprehensive frameworks that capture their complexity.To address these limitations, we introduce a linguistically motivated taxonomy consisting of co-existing meaning-related and form-related relations, while accounting for the directionality of word associations.We applied the taxonomy to a dataset of 1,300 word associations (FAMWA) and assessed it using various LLMs, analyzing their ability to classify word associations.The results show an improved inter-annotator agreement for our taxonomies compared to previous studies ($\kappa$ = .60 for meaning and $\kappa$ = .58 for form). However, models such as GPT-4o perform only modestly in relation labeling (with accuracies of 46.2{\%} for meaning and 78.3{\%} for form), which calls into question their ability to fully grasp the underlying principles of human word associations."
}