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我有这个代码。我有两个特点。如何一起训练这两个特征?

from textblob import TextBlob, Word, Blobber
from textblob.classifiers import NaiveBayesClassifier
from textblob.taggers import NLTKTagger
import re
import nltk



def get_word_before_you_feature(mystring):
    keyword = 'you'
    before_keyword, keyword, after_keyword = mystring.partition(keyword)
    before_keyword = before_keyword.rsplit(None, 1)[-1]
    return {'word_after_you': before_keyword}


def get_word_after_you_feature(mystring):
    keyword = 'you'
    before_keyword, keyword, after_keyword = mystring.partition(keyword)
    after_keyword = after_keyword.split(None, 1)[0]
    return {'word_after_you': after_keyword}
    classifier = nltk.NaiveBayesClassifier.train(train)



lang_detector = NaiveBayesClassifier(train, feature_extractor=get_word_after_you_feature)
lang_detector = NaiveBayesClassifier(train, feature_extractor=get_word_before_you_feature)


print(lang_detector.accuracy(test))
print(lang_detector.show_informative_features(5))

这是我得到的输出。

word_before_you = 'do' 裁判:generi = 2.2:1.0
word_before_you = 'when'generi:裁判 = 1.1:1.0

它似乎只获得了最后一个功能。如何让分类器训练两个特征而不是一个。

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1 回答 1

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您定义lang_detector了两次,第二个定义只是覆盖了第一个。定义一个特征提取器函数,该函数返回特征字典,每个特征名称作为键。在您的情况下,您将定义get_word_features(mystring)并且它可以返回这样的字典:

return { 
     'word_after_you': after_keyword, 
     'word_before_you': before_keyword 
      }

其余的就像您一直在做的那样:将特征检测器函数传递给分类器的构造函数,并检查结果。

lang_detector = NaiveBayesClassifier(train, feature_extractor=get_word_features)
lang_detector.show_most_informative_features(5)
于 2014-10-03T22:51:28.933 回答