and P(b) = .01: If we consider one word/context pair (w, c pos ) with its k noise words c neg 1 ...c neg k , we can express these two goals as the following loss function L to be minimized (hence the −); here the first term expresses that we want the classifier to assign the real context word c pos a high probability of being a neighbor, and the second term expresses that we want to assign each of the noise words c neg i a high probability of being a non-neighbor, all multiplied because we assume independence: