message: Update word2vec_basic.py
  refine comments
diff: @@ -100,7 +100,7 @@ def word2vec_basic(log_dir):
  # This is the original text but words are replaced by their codes
  # count - map of words(strings) to count of occurrences
  # dictionary - map of words(strings) to their codes(integers)
  - # reverse_dictionary - maps codes(integers) to words(strings)
  + # reverse_dictionary - map of codes(integers) to words(strings)
  data, count, unused_dictionary, reverse_dictionary = build_dataset(
  vocabulary, vocabulary_size)
  del vocabulary # Hint to reduce memory.
  @@ -186,8 +186,9 @@ def word2vec_basic(log_dir):
  # Compute the average NCE loss for the batch.
  # tf.nce_loss automatically draws a new sample of the negative labels each
  # time we evaluate the loss.
  - # Explanation of the meaning of NCE loss:
  + # Explanation of the meaning of NCE loss and why choosing NCE over tf.nn.sampled_softmax_loss:
  # http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
  + # http://papers.nips.cc/paper/5165-learning-word-embeddings-efficiently-with-noise-contrastive-estimation.pdf
  with tf.name_scope('loss'):
  loss = tf.reduce_mean(
  tf.nn.nce_loss(
  