Word2Vec

Word2Vec is a shallow neural network model trained to predict a word from its context (CBOW) or to predict context from a word (Skip-Gram). Training on large corpora produces dense word vectors where semantically related words cluster together and analogical relationships emerge as linear offsets — the famous 'king − man + woman ≈ queen' property. Word2Vec was a landmark advance in word embeddings, predating transformers, and its representations remain useful as input features for classical and lightweight NLP models.