Why activation function is not needed during the runtime of an Word2Vec model In Word2Vec trainable model, there are two different weight matrix. The matrix $W$ from input-to-hidden layer and the matrix $W'$ from hidden-to-output layer. 

Referring to this article, I understand that the reason we have the matrix $W'$ is basically to compensate for the lack of activation function in the output layer. As activation function is not needed during runtime, there is no activation function in the output layer. But we need to update the input-to-hidden layer weight matrix $W$ through backpropagation to eventually reach to the word embedding most suitable for our usecase. So there is this weight matrix $W'$ in the output layer.

But my question is why activation function is not needed during the runtime? Can anyone please explain?
From this StackOverflow Question


  while no activation is explicitly formulated, we could consider it to
  be a linear classification function. It appears that the dependencies
  that the word2vec models try to model can be achieved with a linear
  relation between the input words.
  
  Adding a non-linear activation function allows the neural network to
  map more complex functions, which could in turn lead to fit the input
  onto something more complex that doesn't retain the dependencies
  word2vec seeks.