title: Feature selection for Named entity using SVM
body: <p>I have some user comments data from which I want to find the name of consumer electronic brands.
  For instance consider these ne_chinked example sentence which talk about "PS4", "nokia 720 lumia" ,"apple ipad", "sony bravia":-</p>
  
  <pre><code>In [52]: nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize('When is the PS4 releasing')))
  Out[52]: Tree('S', [('When', 'WRB'), ('is', 'VBZ'), ('the', 'DT'), Tree('ORGANIZATION', [('PS4', 'NNP')]), ('releasing', 'NN')])
  
  In [53]: nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize('I couldnt find the nokia 720 lumia in stores')))
  Out[53]: Tree('S', [('I', 'PRP'), ('couldnt', 'VBP'), ('find', 'JJ'), ('the', 'DT'), ('nokia', 'NN'), ('720', 'CD'), ('lumia', 'NN'), ('in', 'IN'), ('stores', 'NNS')])
  
  In [54]: nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize('I just bought apple ipad and its really awesome')))
  Out[54]: Tree('S', [('I', 'PRP'), ('just', 'RB'), ('bought', 'VBD'), ('apple', 'JJ'), ('ipad', 'NN'), ('and', 'CC'), ('its', 'PRP$'), ('really', 'RB'), ('awesome', 'JJ')])
  
  In [55]: nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize('I would like to buy 1 Sony bravia led television')))
  Out[55]: Tree('S', [('I', 'PRP'), ('would', 'MD'), ('like', 'VB'), ('to', 'TO'), ('buy', 'VB'), ('1', 'CD'), ('Sony', 'NNP'), ('bravia', 'IN'), ('led', 'VBN'), ('television', 'NN')])  
  </code></pre>
  
  <p>The problem is how do I represent the data for the svm to learn. I read tens  of research papers but none of them have disclosed how they represented the feature data to the svm.
  Can anybody please help </p>