This example has a target word w (apricot), and 4 context words in the L = ±2 window, resulting in 4 positive training instances (on the left below): For training a binary classifier we also need negative examples. In fact skipgram with negative sampling (SGNS) uses more negative examples than positive examples (with the ratio between them set by a parameter k). So for each of these (w, c pos ) training instances we'll create k negative samples, each consisting of the target w plus a 'noise word' c neg . A noise word is a random word from the lexicon, constrained not to be the target word w. The right above shows the setting where k = 2, so we'll have 2 negative examples in the negative training set − for each positive example w, c pos .