@inproceedings{kanashiro-pereira-cheng-2026-cinet,
    title = "{C}i{N}et-Handai-Kyodai at {S}em{E}val-2026 Task 5: Combining {LLM} Prompting, Semantic Similarity, and Synthetic Gaze for Graded Sense Plausibility",
    author = "Kanashiro Pereira, Lis  and
      Cheng, Fei",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.245/",
    pages = "1950--1956",
    ISBN = "979-8-89176-414-9",
    abstract = "We present a hybrid system for SemEval-2026 Task 5 on graded word-sense plausibility in narrative contexts. Our approach combines prompt-based large language model (LLM) scoring with three complementary features: semantic embedding similarity, story-conditioned definition generation, and a synthetic gaze signal based on predicted fixation time. We combine these signals using an ordinary least squares regressor. On the official test set, our best system achieves 90.10 Acc{\ensuremath{\pm}}SD and 79.19 Spearman correlation. The system surpasses the reported human reference score on Acc{\ensuremath{\pm}}SD, highlighting the value of combining LLM-based judgments with targeted linguistic and cognitive-inspired features."
}