import nltk

 from nltk.corpus import brown

 def pos_features(sentence,i,history):

     features = {"suffix(1)":sentence[i][-1:],

                 "suffix(2)":sentence[i][-2:],

                 "suffix(3)":sentence[i][-3:]}

     if i == 0:

         features["prev-word"]="<STAR>"

         features["prev_tag"] ="<STAR>"

     else:

         features["prev_word"]=sentence[i-1]

         features["prev_tag"]=history[i-1]

     return features

 class ConsecutivePosTagger(nltk.TaggerI):

     def __init__(self,train_sents):

         train_set=[]

         for tagged_sent in train_sents:

             history=[]

             untagged_sent = nltk.tag.untag(tagged_sent)

             for i,(word,tag) in enumerate(tagged_sent):

                 featureset=pos_features(untagged_sent,i,history)

                 train_set.append((featureset,tag))

                 history.append(tag)

         self.classifier=nltk.NaiveBayesClassifier.train(train_set)

     def tag(self,sentence):

         history=[]

         for i,word in enumerate(sentence):

             featureset=pos_features(sentence,i,history)

             tag=self.classifier.classify(featureset)

             history.append(tag)

         return zip(sentence,history)

 def test_ConsecutivePosTagger():

     tagged_sents=brown.tagged_sents(categories='news')

     size = int(len(tagged_sents) * 0.1)

     train_sents, test_sents = tagged_sents[size:], tagged_sents[:size]

     tagger = ConsecutivePosTagger(train_sents)

     print tagger.evaluate(test_sents)