<p>As @sam-h mentions in his comment, this is an area of ongoing research. </p>

<p>There's no standard or automatic approach, so there's no one best-practice to recommend – you'll likely have to sift through the various papers, in the list `sam-h provided and from elsewhere, for ideas. </p>

<p>In many cases, approaches don't use standard word2vec – adding extra steps before or during training – because standard word2vec is oblivious to the fact that a single word-token might have multiple contrasting senses. As a result, the standard word2vec vectors for words with many senses can wind up with a single vector that "mushes together" the many distinct senses.</p>

<p>One interesting write-up that does manage to bootstrap a model of multiple-senses from existing, word-sense-oblivous word-vectors is described in the paper "<a href="https://arxiv.org/abs/1601.03764" rel="nofollow noreferrer">Linear Algebraic Structure of Word Senses, with Applications to Polysemy</a>", which also has a less-formal <a href="http://www.offconvex.org/2016/07/10/embeddingspolysemy/" rel="nofollow noreferrer">blogpost write-up</a>.</p>

<p>Essentially, by assuming the rich space of all standard word-vectors actually draw from a smaller number of "discourses", and interpreting word-vectors as some combination of the alternate "atoms of discourse" (for their difference senses), they can tease-out the alternate senses of word-tokens that began with only a single vector. </p>
How can we implement word sense disambiguation using word2vec representation?
<p>I know how word2vec works, but I am having trouble with finding out how to implement word sense disambiguation using word2vec. Can you help with the process?</p>