title: word2vec gensim multiple languages
body: <p>This problem is going completely over my head. I am training a Word2Vec model using gensim. I have provided data in multiple languages i.e. English and Hindi. When I am trying to find the words closest to 'man', this is what I am getting:</p>
  
  <pre><code>model.wv.most_similar(positive = ['man'])
  Out[14]: 
  [('woman', 0.7380284070968628),
   ('lady', 0.6933152675628662),
   ('monk', 0.6662989258766174),
   ('guy', 0.6513140201568604),
   ('soldier', 0.6491742134094238),
   ('priest', 0.6440571546554565),
   ('farmer', 0.6366012692451477),
   ('sailor', 0.6297377943992615),
   ('knight', 0.6290514469146729),
   ('person', 0.6288090944290161)]
  --------------------------------------------
  </code></pre>
  
  <p>Problem is, these are all English words. Then I tried to find similarity between same meaning Hindi and English words, </p>
  
  <pre><code>model.similarity('man', 'आदमी')
  __main__:1: DeprecationWarning: Call to deprecated `similarity` (Method will 
  be removed in 4.0.0, use self.wv.similarity() instead).
  Out[13]: 0.078265618974427215
  </code></pre>
  
  <p>This accuracy should have been better than all the other accuracies. The Hindi corpus I have has been made by translating the English one. Hence the words appear in similar contexts. Hence they should be close.</p>
  
  <p>This is what I am doing here:</p>
  
  <pre><code>#Combining all the words together.
  all_reviews=HindiWordsList + EnglishWordsList
  
  #Training FastText model
  cpu_count=multiprocessing.cpu_count()
  model=Word2Vec(size=300,window=5,min_count=1,alpha=0.025,workers=cpu_count,max_vocab_size=None,negative=10)
  model.build_vocab(all_reviews)
  model.train(all_reviews,total_examples=model.corpus_count,epochs=model.iter)
  model.save("word2vec_combined_50.bin")
  </code></pre>