introduced a modification of this model in which the target vocabulary holds a variable number of vectors for each word form, intended to represent its different senses.
the training objective of such a model now has the following shape: the word sense embeddings are trained to maximize the log-probability of context words c i given a word's sense s plus the log-probability of that sense given the word w. For our purposes, this prior is a constant, p(s|w) = 1 n , as we do not have information on the probability of each sense of a given word.