title: nltk tag tag_sents give different results
body: <p>I essentially want to use the nltk StanfordNERTagger in order to purify a list of names (eg. there are organizations in there I want to remove) and I stumbled on weird issue.  It seems the tag results of one sentence depend on what other sentences are given, which isn't very intuitive.</p>
  
  <p>Here is how to reproduce:</p>
  
  <pre><code>from nltk.tag import StanfordNERTagger
  tagger = StanfordNERTagger('/path/to/english.all.3class.distsim.crf.ser.gz','/path/to/stanford-ner-2017-06-09/stanford-ner.jar',encoding='utf-8')
  things_to_tag = ["Star Trek".split(),
                   "Star Jones".split(),
                   "Star Wars".split()]
  
  # tagging using tag_sents
  print tagger.tag_sents( things_to_tag )
  
  # tagging using tag
  for t in things_to_tag:
      print tagger.tag(t)
  </code></pre>
  
  <p>Output:</p>
  
  <pre><code>[[(u'Star', u'ORGANIZATION'), (u'Trek', u'ORGANIZATION')],
  [(u'Star', u'ORGANIZATION'), (u'Jones', u'ORGANIZATION')],
  [(u'Star', u'ORGANIZATION'), (u'Wars', u'ORGANIZATION')]]
  
  [(u'Star', u'O'), (u'Trek', u'O')]
  [(u'Star', u'PERSON'), (u'Jones', u'PERSON')]
  [(u'Star', u'O'), (u'Wars', u'O')]
  </code></pre>
  
  <p>I also tried removing <code>Star Wars</code> from the list, and again the results change ('Trek' becomes Person, and 'Star' becomes O).  </p>
  
  <p>I looked into <code>nltk/tag/stanford.py</code> and it's not really clear why this would happen.  I was hoping someone could lend a hand in identifying what the issue might be, or at least confirm I'm not the only one seeing this.</p>
  
  <p>nltk version 3.2.5
  python version 2.7.13</p>