<p>Check the footnote on page 2 of this: <a href="http://arxiv.org/pdf/1402.3722v1.pdf" rel="nofollow noreferrer">http://arxiv.org/pdf/1402.3722v1.pdf</a></p>

<p>This gives a quite clear intuition for the problem.</p>

<p>But you can also use only one vector to represent a word.
Check this (Stanford CS 224n) <a href="https://youtu.be/ERibwqs9p38?t=2064" rel="nofollow noreferrer">https://youtu.be/ERibwqs9p38?t=2064</a></p>

<p>I am not sure how that will be implemented(neither does the video explains).</p>
Why does word2vec use 2 representations for each word?
<p>I am trying to understand why word2vec's skipgram model has 2 representations for each word (the hidden representation which is the word embedding) and the output representation (also called context word embedding) . Is this just for generality where the context can be anything (not just words) or is there a more fundamental reason </p>