title: nltk: Text classification using custom feature set
body: <p>I have a dataset that looks like this:</p>
  
  <pre><code>featureDict = {identifier1: [[first 3-gram], [second 3-gram], ... [last 3-gram]],
                 ...
                 identifierN: [[first 3-gram], [second 3-gram], ... [last 3-gram]]}
  </code></pre>
  
  <p>Plus I have a dict of labels for the same set of documents:</p>
  
  <pre><code>labelDict = {identifier1: label1,
               ...
               identifierN: labelN}
  </code></pre>
  
  <p>I want to figure out the most appropriate nltk container in which I can store this information in one place and seamlessly apply the nltk classifiers.</p>
  
  <p>Additionally, before I use any classifiers on this dataset I'd also like to use a tf-idf filter on this features space.</p>
  
  <p>References and documentation will be helpful.</p>