CBOW, or Continuous Bag of Words, is a model used in natural language processing to predict a target word based on its surrounding context words. It takes a set of context words as input and aims to output the probability of the target word appearing in that context. This approach helps in understanding the relationships between words in a given text. In CBOW, the context words are represented as a vector, and the model learns to map these vectors to the target word. This technique is part of the Word2Vec framework, which is widely used for generating word embeddings that capture semantic meanings and relationships in language.