Word embedding is a natural language processing (NLP) technique that transforms words into numerical vectors in a continuous vector space. This representation captures semantic meanings and relationships between words, allowing algorithms to understand context and similarities. Popular word embedding models include Word2Vec, GloVe, and FastText, which use various approaches to generate embeddings based on word co-occurrences and contexts in large text corpora. By representing words as dense vectors rather than sparse one-hot encodings, word embeddings enable more effective computational operations, such as clustering, classification, and similarity measurements, enhancing the performance of NLP applications.