 Bag-of-words and skip-gram models are the basis of the word2vec program.Instead of using neural net language models to produce actual probabilities, it is common to instead use the distributed representation encoded in the networks' "hidden" layers as representations of words; each word is then mapped onto an -dimensional real vector called the word embedding, where is the size of the layer just before the output layer.The representations in skip-gram models have the distinct characteristic that they model semantic relations between words as linear combinations, capturing a form of compositionality.