Sequence Classification

Natural Language Processing with Python

Charpter 6.1

 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)

流程为：

结果为：

0.796940194715