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 pretrainedmodels, experimenting with the informativeness of similarity calculations and classificationmethods. We introduce scratch-trained cross-attention 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 trainng curriculum matter more than pretrained model choice for this novel task, and we identify several directions for future improvement. View our code base at: https://github.com/heliosraz/SemEval52026

This paper introduces a new dataset for classifying memes by their template and communicative intent.It includes a broad selection of meme templates and examples scraped from imgflip and a smaller hand-annotated set of memes scraped from Reddit.The Reddit memes have been annotated for meta-category using a novel annotation scheme that classifies memes by the structure of the perspective they are being used to communicate.YOLOv11 and ChatGPT 4o are used to provide baseline modeling results.We find that YOLO struggles with template classification on real-world data but outperforms ChatGPT in classifying meta-categories.