Part of Speech Tagging

Natural Language Processing with Python

Charpter 6.1

suffix_fdist处代码稍微改动。

 import nltk
 from nltk.corpus import brown

 def common_suffixes_fun():
     suffix_fdist=nltk.FreqDist()
     for word in brown.words():
         word=word.lower()
         suffix_fdist[word[-1:]] +=1
         suffix_fdist[word[-2:]] +=1
         suffix_fdist[word[-3:]] +=1
     most_freqent_items=[it for it in sorted(suffix_fdist.items(),key=lambda x:(-x[1],x[0]))[:100]]
     return [su[0] for su in most_freqent_items]

 common_suffixes = common_suffixes_fun()

 def pos_features(word):
     features={}
     for su in common_suffixes:
         features['endswith(%s)' % su]=word.lower().endswith(su)
     return features

 def test_pos():
     tagged_words = brown.tagged_words(categories='news')[:5000]
     featuresets=[(pos_features(word),tag) for (word,tag) in tagged_words]

     size= int(len(tagged_words)*0.1)
     train_set, test_set = featuresets[size:],featuresets[:size]
     classifier=nltk.NaiveBayesClassifier.train(train_set)

     print nltk.classify.accuracy(classifier,test_set)
     classifier.show_most_informative_features(5)

运行结果为：

0.652
Most Informative Features
endswith(o) = True TO : NN = 423.2 : 1.0
endswith(es) = True DOZ : NN = 319.5 : 1.0
endswith(om) = True WPO : NN = 319.5 : 1.0
endswith(as) = True BEDZ : IN = 303.3 : 1.0
endswith(s) = True BEDZ : IN = 303.3 : 1.0