<p>You can start with <a href="http://spacy.io" rel="nofollow noreferrer">spacy</a>'s implementation of <a href="https://github.com/explosion/sense2vec" rel="nofollow noreferrer">sense2vec</a>. It is based on the original sense2vec <a href="https://arxiv.org/abs/1511.06388" rel="nofollow noreferrer">paper</a>. From the abstract:</p>

<blockquote>
  <p>This paper presents a novel approach which addresses these concerns by modeling multiple embeddings for each word based on supervised disambiguation, which provides a fast and accurate way for a consuming NLP model to select a sense-disambiguated embedding. We demonstrate that these embeddings can disambiguate both contrastive senses such as nominal and verbal senses as well as nuanced senses such as sarcasm.</p>
</blockquote>
What are the state-of-art algorithms for resolving words polysemy/homonymy?
<p>I tried to solve the word-polysemy problem (fix WordNet-synsets for polysemy words in the text) via word2vec-like neural networks (<a href="https://stackoverflow.com/questions/51330549/using-word2vec-for-polysemy-solving-problems">Using Word2Vec for polysemy solving problems</a>), but it give too poor results.
What are other state-of-art algorithms for resolving words polysemy/homonymy? Can you give me some articles?</p>