(2013b), a method for learning vector representations of words based on their distributional contexts.
their model describes the probability of a token w i at position i using logistic regression with a factored parameterisation, where i+j is a vector encoding the context over a window of size k centred around position i, W is the vocabulary and the parameters V and U ∈ R |W |×d are matrices referred to as the context and word embeddings.