The Skip-Gram model learns vector representations of words based on their patterns of co-occurrence in the training corpus as follows: it assigns to each word c in the vocabulary V , a "context" and a "target" vector, respectively u c and v c , which are to be used in order to predict the words that appear around each occurrence of c within a window of ∆ tokens.
the log probability of any target word w to occur at any position within distance ∆ of a context word c is taken to be proportional to the inner product between u c and v w , i.e., letting Further, Skip-Gram assumes that the conditional probability of each possible set of words in a window around a context word c factorizes as the product of the respective conditional probabilities: (2) (Mikolov et al., 2013a) proposed learning the Skip-Gram parameters on a training corpus by using maximum likelihood estimation under (1) and (2).