Word2vec is a neural network-based technique used for natural language processing tasks such as word embedding, text classification, and language translation. It is capable of learning the meaning and context of words by analyzing large amounts of textual data. Word2vec generates high-dimensional vector representations of words based on their relationships with other words in the corpus. These representations can be used to identify semantic similarities between different words and to perform operations like analogy detection and word association. Word2vec was first introduced by Google in 2013 and has since become one of the most popular techniques for natural language processing.
