I'm reading about Skip-Gram and how it represents words as vectors. If I'm correct it not just represents a single words as a vector, but the word and it's neighbours.
So if I have the dataset "the quick brown fox jumped over the lazy dog", Skip-Gram (with a window of 1) learns vectors for
([the, brown], quick) (where the words between
 represent the neighbours),
([quick, fox], brown), ... rather than for the single words "quick" and "brown".
Is this correct, and if not how does it use the window/neighbours?