<cite>Cotterell et al. (2017)</cite> showed that SG is a form of exponential family PCA that factorizes the matrix of word/context cooccurrence counts (rather than shifted positive PMI values). With this interpretation, <cite>they</cite> generalize SG from matrix to tensor factorization, and provide a theoretical basis for modeling higher-order SG (or additional context, such as morphological features of words) within a word embeddings framework. Specifically, <cite>Cotterell et al.</cite> recast higher-order SG as maximizing the log-likelihood