Part of Speech Tagging

Natural Language Processing with Python

Charpter 6.1

suffix_fdist处代码稍微改动。

 1 import nltk
 2 from nltk.corpus import brown
 3
 4 def common_suffixes_fun():
 5     suffix_fdist=nltk.FreqDist()
 6     for word in brown.words():
 7         word=word.lower()
 8         suffix_fdist[word[-1:]] +=1
 9         suffix_fdist[word[-2:]] +=1
10         suffix_fdist[word[-3:]] +=1
11     most_freqent_items=[it for it in sorted(suffix_fdist.items(),key=lambda x:(-x[1],x[0]))[:100]]
12     return [su[0] for su in most_freqent_items]
13
14 common_suffixes = common_suffixes_fun()
15
16 def pos_features(word):
17     features={}
18     for su in common_suffixes:
19         features[‘endswith(%s)‘ % su]=word.lower().endswith(su)
20     return features
21
22 def test_pos():
23     tagged_words = brown.tagged_words(categories=‘news‘)[:5000]
24     featuresets=[(pos_features(word),tag) for (word,tag) in tagged_words]
25
26     size= int(len(tagged_words)*0.1)
27     train_set, test_set = featuresets[size:],featuresets[:size]
28     classifier=nltk.NaiveBayesClassifier.train(train_set)
29
30     print nltk.classify.accuracy(classifier,test_set)
31     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