title: How to find Sentence Similarity using deep learning?
body: <p>I am trying to find sentence similarity through word emebeddings and then applying cosine similarity score. Tried CBOW/Skip Gram methods for embedding but did not solve the problem.</p>
  
  <p>I am doing this for product review data. I have two columns:</p>
  
  <pre><code>SNo         Product_Title                                Customer_Review   
   1       101.x battery works well                    I have an Apple phone and it's not that
            with Samsung smart phone                     that great.
  
   2       112.x battery works well                     I have samsung smart tv and I tell that it's
           with Samsung smart phone                     not wort buying.
  
   3      112.x battery works well                      This charger works very well with samsung 
          with Samsung smart phone.                      phone. It is fast charging.
  </code></pre>
  
  <p>The first two reviews are <code>irrelevant</code> as semantic meaning of <code>Product_Title</code> and <code>Customer_Review</code>  are completely different.</p>
  
  <p>How can an algorithm find this semantic meaning of sentences and score them.</p>
  
  <p>My Approach:</p>
  
  <ol>
  <li><p>Text pre-processing</p></li>
  <li><p>Train CBOW/Skip gram using Gensim on my data-set</p></li>
  <li><p>Do Sentence level encoding via averaging all word vectors in that sentence </p></li>
  <li><p>Take cosine similarity of <code>product_title</code> and <code>reviews</code>.</p></li>
  </ol>
  
  <p>Problem: It was not able to find the context from the sentence and hence the result was very poor.</p>
  
  <p>Approch 2:</p>
  
  <p>Used pre-trained BERT without pre-processing sentences. The result was not improving either.</p>
  
  <p>1.Any other approach that would capture the context/semantics of sentences.</p>
  
  <p>2.How can we train BERT on our data-set from scratch without using pre-trained model?</p>