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?
Skip-Gram Window?
information retrieval