Word embedding is a technique in natural language processing that involves mapping words or phrases from a vocabulary to vectors of real numbers, capturing semantic meanings and relationships. It enables algorithms to understand context, similarity, and analogy between words, enhancing tasks like sentiment analysis, translation, and information retrieval.