Word embedding is a technique used in natural language processing to represent words as continuous vectors in a high-dimensional space. These word vectors capture semantic relationships between words based on their context in a corpus of text. Word embedding models such as Word2Vec, GloVe, and FastText have been widely used in various NLP tasks such as sentiment analysis, machine translation, and named entity recognition. They have shown to be effective in capturing syntactic and semantic similarities between words, allowing for improved performance in downstream tasks.