title: Using nltk to analyze a random document based on reading a given document
body: <p>I am relatively new to this, so I am not sure if this is correct.  I would like to analyze a document based on another document I have analyzed.  </p>
  
  <p>This is what I have so far.  </p>
  
  <pre><code>import nltk
  sent_one = "Here is a sentence I would like to break up into words"
  sent_two = "This is new.  How is the weather up there?"
  tokens = nltk.word_tokenize(sent_one)
  tokens_two = nltk.word.tokenize(sent_two)
  new_set = nltk.pos_tag(tokens)
  second_set = nltk.pos_tag(tokens_two)
  
  t0 = nltk.DefaultTagger('NN') 
  t1 = nltk.UnigramTagger(new_set, backoff = t0) 
  t2 = nltk.BigramTagger(new_set, backoff = t1)
  t2.evaluate(tokens_two)
  </code></pre>
  
  <p>This is the error that I get 
  Traceback (most recent call last): 
  in   t1 = nltk.UnigramTagger(new_set, backoff = t0)
  ValueError: too many values to unpack (expected 2)</p>
  
  <p>I want to use this to analyze sent_two I understand that this means that the UnigramTagger is unable to recognize sent_one as a list of words/parts of speech, but I am not sure how to correct this. </p>