In contrast to the conventional ways of using context words to predict the next word or the central word, the Skip-Gram model (Mikolov et al., 2013b) aims to leverage the central word to predict its context words.
assuming that the central word is w I and one of its neighboring word is w O , P(w O |w I ) is modeled in the following way: where W denotes the dictionary consisting of all words, U w ∈ R d and V w ∈ R d represent the d-dimensional 'output' and 'input' embedding vectors of word w, respectively.