title: Boost word2vec related words to improve ElasticSearch results
body: <p>I am trying to build a search engine based on both ElasticSearch and a word2vec model 'trained' on the corpus loaded into ElasticSearch. </p>
  
  <p>Suppose that the following query is provided: "Is the United States a country?". And suppose that my Word2Vec model has found that "United States" is similar to "usa" with a cosine similarity of 0.8 and to "america" with a cosine similarity of 0.7. I would now like ElasticSearch to search for they main query but also subsituting those 'synonyms' but taking into account the weights. </p>
  
  <p>My current take on this is to perform a query_string and boosting the individual 'synonyms', e.g.</p>
  
  <pre><code>"query": "Is the ((United States)^1 OR (usa)^0.8 OR (america)^0.7) a country?"
  </code></pre>
  
  <p>As a note, I will probably provide <code>(United States)</code> a boost of <code>0.5</code> as there are two words, which will increase the score if found together, therefore dividing the initial boost by the amount of words seems like a plausible approach.</p>
  
  <p>This would however mean that I would have to write my own piece of code to identify those synonyms and create these resulting queries. Furthermore, I am not sure whether this is the 'best' way to do it. </p>
  
  <p>Could you provide your take on this method and let me know what you believe would be a smarter approach?</p>