CiNet-Handai-Kyodai at SemEval-2026 Task 5: Combining LLM Prompting, Semantic Similarity, and Synthetic Gaze for Graded Sense Plausibility
Year: 2026
Type: conference-paper
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 ±SD and 79.19 Spearman correlation.The system surpasses the reported human reference score on Acc±SD, highlighting the value of combining LLM-based judgments with targeted linguistic and cognitive-inspired features.
Authors: Lis Kanashiro Pereira, Fei Cheng
Institution: Kyoto University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166869928