@inproceedings{chenwei-etal-2025-context,
    title = "Context and {POS} in Action: A Comparative Study of {C}hinese Homonym Disambiguation in Human and Language Models",
    author = "Chenwei, Xie  and
      Ma, Matthew King-Hang  and
      Wang, Wenbo  and
      Wang, William Shiyuan",
    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/ingest-emnlp/2025.emnlp-main.1404/",
    pages = "27596--27613",
    ISBN = "979-8-89176-332-6",
    abstract = "Ambiguity is pervasive in language, yet we resolve it effortlessly and unconsciously, often aided by context and part-of-speech (POS) cues. This study investigates how context similarity and POS influence homonym disambiguation in humans and large language models (LLMs). To enable comparable analyses between humans and LLMs, we first built an expert-curated sentence-pair dataset, manipulating context similarity and homonym POS categories (nouns vs. verbs). Participants (n = 55) and LLMs (via prompting) were asked to rate the sense similarity of target homonyms embedded within each sentence on a 7-point Likert scale. We found that context similarity influenced both groups similarly, but only humans utilized POS information, likely contributing to their superior performance. Model-derived metrics (surprisal, entropy) predicted human reaction times, and angular similarity between homonym representations accounted for additional variance, highlighting the roles of both expectation-based and semantic processes. Psycholinguistic factors like age of acquisition affected only human responses, underscoring distinct language acquisition mechanisms. Together, our findings illustrate how context and POS information interactively shape homonym resolution in humans, while exposing the limitations of current language models in capturing these nuanced processes. Dataset and codes are publicly available at https://github.com/neurothew/context-and-pos-in-action."
}