ZYC at SemEval-2026 Task 5: Application of BERT-based Contextual Embeddings Similarity for WSD
Year: 2026
Type: conference-paper
Abstract: We investigate contextual embedding manipulation for Word Sense Disambiguation (WSD) as part of SemEval-2026 Task 5. We propose four approaches built on BERT-like pretrained models, experimenting with the informativeness of similarity calculations and classification methods.We introduce scratch-trained crossattention mechanisms inspired by GLiNER to compute similarity between definition or synonym representations and the full context.Our best performance achieved 57% accuracy with a Spearman correlation of 0.20.Our results suggest that finetuning strategy and training curriculum matter more than pretrained model choice for this novel task, and we identify several directions for future improvement.
Authors: Sunny Zhou, Jordan Youner, Dean Cahill
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166848053