Word2vec Re-training with new corpus, how the weights will be updated for the existing vocabulary?
Why not initiate each of the word2vec parameters with random generated numbers for each run? I could do this and with careful selection of the random numbers for each parameter (numFeatures, contextWindow, seed) I was able to get random similarity tuples which I wanted for my usecase. Simulating an ensemble architecture.
What do others think of it? Pls do reply.
Scenerio: A word2vec model is trained on corpus C1 with vocabulary V1. If we want to re-train the same model with another corpus C2 having vocabulary V2 using train() API, what will happen out of these two:

For model, weights for V1 intersection V2 will be reset and re-training for with corpus C2 will come up with all together new weights
For model, re-training with corpus C2 will be continued with the existing weights for vocabulary V1 intersection V2.

Which one is correct hypothesis out of the above two?