<p>Regarding the two, input-hidden weight matrix and hidden-output weight matrix, there is an interesting research paper.
'A Dual Embedding Space Model for Document Ranking', Mitra et al., arXiv 2016. (<a href="https://arxiv.org/pdf/1602.01137.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1602.01137.pdf</a>).
Similar with your question, this paper studies how these two weight matrix are different, and claims that they encode different characteristics of words.</p>

<p>Overall, from my understanding, it is your choice to use either the input-hidden weight matrix (convention), hidden-output weight matrix, or the combined one as word embeddings, depending on your data and the problem to solve.</p>
why we use input-hidden weight matrix to be the word vectors instead of hidden-output weight matrix?
<p>In word2vec, after training, we get two weight matrixes:1.input-hidden weight matrix; 2.hidden-output weight matrix. and people will use the input-hidden weight matrix as the word vectors(each row corresponds to a word, namely, the word vectors).Here comes to my confusions:</p>

<ol>
<li>why people use input-hidden weight matrix as the word vectors instead of the hidden-output weight matrix.</li>
<li>why don't we just add softmax activation function to the hidden layers rather than output layers, thus preventing time-consuming.</li>
</ol>

<p>Plus, clarifying remarks on the intuition of how word vectors can be obtained like this will be appreciated.</p>