title: Python and NLTK: Baseline tagger
body: <p>I am writing a code for a baseline tagger. Based on the Brown corpus it assigns the most common tag to the word. So if the word "works" is tagged as verb 23 times and as a plural noun 30 times then based on that in the user input sentence it would tagged as plural noun. If the word was not found in the corpus, then it is tagged as a noun by default. 
  The code I have so far returns every tag for the word not just the most frequent one. How can I achieve it only returning the frequent tag per word?</p>
  
  <pre><code>import nltk 
  from nltk.corpus import brown
  
  def findtags(userinput, tagged_text):
      uinput = userinput.split()
      fdist = nltk.FreqDist(tagged_text)
      result = []
      for item in fdist.items():
          for u in uinput:
              if u==item[0][0]:
                  t = (u,item[0][1])
                  result.append(t)
          continue
          t = (u, "NN")
          result.append(t)
      return result
  
  def main():
      tags = findtags("the quick brown fox", brown.tagged_words())
      print tags
  
  if __name__ == '__main__':
      main()
  </code></pre>