Embedding Values in word2vec Are the embedding values for a particular word using word2vec Skipgram model the weights of the first layer or the softmax output of the function?
Does the embedding value change according to the training corpus?
The word embeddings are the weights of the first layer i.e. the embedding layer and not the softmax output of the function. The embedding values represent a vector which gives the location of the word with respect to other words in a high dimensional vector space. And yes, the embedding values change according to the training corpus. However, if you are using a given language (for example English) and have a large amount of training data the final values of the vectors will turn out to be pretty close even with training corpus of different contexts.