词干化和词化差异

分别尝试一些单词的词干提取和词形还原：

from nltk.stem import WordNetLemmatizer 
from nltk.stem import PorterStemmer 
stemmer = PorterStemmer() 
lemmatizer = WordNetLemmatizer() 
print(stemmer.stem('stones')) 
print(stemmer.stem('speaking')) 
print(stemmer.stem('bedroom')) 
print(stemmer.stem('jokes')) 
print(stemmer.stem('lisa')) 
print(stemmer.stem('purple')) 
print('----------------------') 
print(lemmatizer.lemmatize('stones')) 
print(lemmatizer.lemmatize('speaking'))
print(lemmatizer.lemmatize('bedroom'))
print(lemmatizer.lemmatize('jokes'))
print(lemmatizer.lemmatize('lisa'))
print(lemmatizer.lemmatize('purple'))

结果是：

stone 
speak 
bedroom 
joke 
lisa 
purpl 
---------------------- 
stone 
speaking 
bedroom
joke
lisa
purple

词干提取的方法可以在不知道语境的情况下对词汇使用，这就是为什么它相较词形还原方法速度更快但准确率更低。