Contextual embedding is a representation technique in natural language processing that captures the meaning of words based on their surrounding context, enabling models to understand polysemy and nuances in language. Unlike static embeddings, contextual embeddings dynamically adjust word representations depending on the sentence they appear in, leading to more accurate language understanding and generation.