I am currently trying to understand the architecture behind the word2vec neural net learning algorithm, for representing words as vectors based on their context.
After reading Tomas Mikolov paper I came across what he defines as a projection layer. Even though this term is widely used when referred to word2vec, I couldn't find a precise definition of what it actually is in the neural net context.
My question is, in the neural net context, what is a projection layer? Is it the name given to a hidden layer whose links to previous nodes share the same weights? Do its units actually have an activation function of some kind?
￼Another resource that also refers more broadly to the problem can be found in this tutorial, which also refers to a projection layer around page 67.