This trains the distributed representation to be useful as an input layer for prediction of words from their neighbors in a Skipgram likelihood.
to maximize summed across all words w st in all sentences w s , where b is the skip-gram window (truncated by the ends of the sentence) and p V (w sj |w st ) is a neural network classifier that takes vector representations for w st and w sj as input (see Section 2).