How do embedding models handle polysemy in natural language?

Contextual embeddings resolve polysemy by generating different representations based on context.

Static embeddings assign one vector per word, failing on polysemy. Contextual models like transformers generate dynamic embeddings conditioned on surrounding tokens, enabling disambiguation of meanings such as 'bank' (river vs finance).