Word embedding is a technique in natural language processing that transforms words into numerical vectors. These vectors capture the meanings of words based on their context in large text datasets. By representing words in this way, similar words are positioned closer together in a multi-dimensional space, allowing algorithms to understand relationships and similarities between them. This method is commonly used in various applications, such as machine learning and artificial intelligence, to improve tasks like sentiment analysis and language translation. Popular models for generating word embeddings include Word2Vec and GloVe, which help computers process and analyze human language more effectively.