title: NLTK : combining stanford tagger and personal tagger
body: <p>The goal of my project is to answer queries such as, for example:
  "I am looking for American women between 20 and 30 years old who work in Google"
  I then have to process the query and to look into a DB to find the answer.</p>
  
  <p>For this, I would need to combine the Stanford 3-class NERTagger and my own tagger. Indeed, my NER tagger can tag ages, nationalities and gender. But I need the Stanford tagger to tag organizations as I don't have any training file for this.</p>
  
  <p>Right now, I have a code like this:</p>
  
  <pre><code>def __init__(self, q):
      self.userQuery = q
  def get_tagged_tokens(self):
      st = NERTagger('C:\stanford-ner-2015-01-30\my-ner-model.ser.gz','C:\stanford-ner-2015-01-30\stanford-ner.jar')
      result = st.tag(self.userQuery.split())[0]
      return result
  </code></pre>
  
  <p>And I would like to have something like this:</p>
  
  <pre><code>def get_tagged_tokens(self):
      st = NERTagger('C:\stanford-ner-2015-01-30\my-ner-model.ser.gz','C:\stanford-ner-2015-01-30\stanford-ner.jar')
      st_def = NERTagger('C:\stanford-ner-2015-01-30\classifiers\english.all.3class.distsim.crf.ser.gz','C:\stanford-ner-2015-01-30\stanford-ner.jar')
      tagger = BackoffTagger([st, st_def])
      result = st.tag(self.userQuery.split())[0]
      return result
  </code></pre>
  
  <p>This would mean that the tagger first uses my tagger and then the stanford one to tag untagged words. </p>
  
  <p>Is it possible to combine my model with the Stanford model just to tag organizations? If yes, what is the best way to perform this?</p>
  
  <p>Thank you!</p>