title: Training n-gram NER with Stanford NLP
body: <p>Recently I have been trying to train n-gram entities with Stanford Core NLP. I have followed the following tutorials - <a href="http://nlp.stanford.edu/software/crf-faq.shtml#b" rel="noreferrer">http://nlp.stanford.edu/software/crf-faq.shtml#b</a></p>
  
  <p>With this, I am able to specify only unigram tokens and the class it belongs to. Can any one guide me through so that I can extend it to n-grams. I am trying to extract known entities like movie names from chat data set.  </p>
  
  <p>Please guide me through in case I have mis-interpretted the Stanford Tutorials and the same can be used for the n-gram training. </p>
  
  <p>What I am stuck with is the following property</p>
  
  <pre><code>#structure of your training file; this tells the classifier
  #that the word is in column 0 and the correct answer is in
  #column 1
  map = word=0,answer=1
  </code></pre>
  
  <p>Here the first column is the word (unigram) and the second column is the entity, for example </p>
  
  <pre><code>CHAPTER O
  I   O
  Emma    PERS
  Woodhouse   PERS
  </code></pre>
  
  <p>Now that I need to train known entities (say movie names) like <strong>Hulk</strong>, <strong>Titanic</strong> etc as movies, it would be easy with this approach. But in case I need to train <strong>I know what you did last summer</strong> or <strong>Baby's day out</strong>, what is the best approach ? </p>