<p>I think you will need to read a paper about the training process. Basically the values of the vectors are the node values of the trained neural network.</p>

<p>I tried to read <a href="http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf" rel="nofollow noreferrer">the original paper</a> but I think the paper <a href="https://arxiv.org/abs/1411.2738" rel="nofollow noreferrer">"word2vec Parameter Learning Explained"</a> by Xin Rong has a more detailed explanation. </p>
How does word2vec or skip-gram model convert words to vector?
<p>I have been reading a lot of papers on NLP, and came across many models. I got the SVD Model and representing it in 2-D, but I still did not get how do we make a word vector by giving a corpus to the word2vec/skip-gram model? Is it also co-occurrence matrix representation for each word? Can you explain it by taking an example corpus:</p>

<pre><code>Hello, my name is John.
John works in Google.
Google has the best search engine. 
</code></pre>

<p>Basically, how does skip gram convert <code>John</code> to a vector?</p>