title: How to evaluate word2vec build on a specific context files
body: <p>Using gensim word2vec, built a CBOW model with a bunch of litigation files for representation of word as vector in a Named-Entity-recognition problem, but I want to known how to evaluate my representation of words. If I use any other datasets like wordsim353(NLTK) or other online datasets of google, it doesn't work because I built the model specific to my domain dataset of files. How do I evaluate my word2vec's representation of word vectors .I want words belonging to similar context to be closer in vector space.How do I ensure that the build model is doing it ?</p>
  
  <p>I started by using a techniques called <strong>odd one out</strong>. Eg:</p>
  
   
  
  <pre class="lang-python prettyprint-override"><code>model.wv.doesnt_match("breakfast cereal dinner lunch".split()) --&gt; 'cereal'
  </code></pre>
  
  <p>I created my own dataset(for validating) using the words in the training of word2vec .Started evaluating with taking three words of similar context and an odd word out of context.But the accuracy of my model is only 30 % .</p>
  
  <p>Will the above method really helps in evaluating my w2v model ? Or Is there a better way ?</p>
  
  <p>I want to go with word_similarity measure but I need a reference score(Human assessed) to evaluate my model or is there any techniques to do it? Please ,do suggest any ideas or techniques .</p>