Skip-gram model is to find word representations that are useful for predicting the surrounding words in a sentence or a document

given a sequence of training words w1, w2, w3, . . . , wT , the objective of the Skip-gram model is to maximize the average log probability

Hierarchical Softmax

Negative Sampling

Noise Contrastive Estimation

differentiate data from noise by means of logistic regression

Distributed Representations of Words and Phrases and their Compositionality