text: Title: In softmax layer of word2vec, do we use cosine similarity or dot product?
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  #### Problem description
  
  I have read the paper "Efficient Estimation of Word Representation in Vector Space".  This article says that, we use **cosine similarity**  in softmax layer of word2vec. But someone says that gensim uses **dot product** in softmax layer of word2vec while uses **cosine similarity** between word vectors which have been trained. I have not read the source code, and I wanted to confirm  whether use  dot product in softmax layer and use cosine similarity after trained.
  
  