Python – 词性标注

标注是文本处理的一个基本特征，它将单词标记为语法范畴。我们利用分词和pos_tag 函数为每个单词创建标记。

import nltk

text = nltk.word_tokenize("A Python is a serpent which eats eggs from the nest")
tagged_text=nltk.pos_tag(text)
print(tagged_text)

运行上面的程序时，我们得到以下输出 −

[('A', 'DT'), ('Python', 'NNP'), ('is', 'VBZ'), ('a', 'DT'), ('serpent', 'NN'), 
('which', 'WDT'), ('eats', 'VBZ'), ('eggs', 'NNS'), ('from', 'IN'), 
('the', 'DT'), ('nest', 'JJS')]

标记说明

我们可以通过下面的程序使用内建值来描述每个标记的含义。

import nltk

nltk.help.upenn_tagset('NN')
nltk.help.upenn_tagset('IN')
nltk.help.upenn_tagset('DT')

当我们运行上述程序时，我们得到以下输出 −

NN：名词，通用，单数或质量 
    common-carrier cabbage knuckle-duster Casino afghan shed thermostat
    investment slide humour falloff slick wind hyena override subhumanity
    machinist ...
IN：介词或连词，从属 
    astride among uppon whether out inside pro despite on by throughout
    below within for towards near behind atop around if like until below
    next into if beside ...
DT：限定词 
    all an another any both del each either every half la many much nary
    neither no some such that the them these this those

标记语料库

我们还可以标记语料库数据并查看该语料库中每个单词的标记结果。

import nltk

from nltk.tokenize import sent_tokenize
from nltk.corpus import gutenberg
sample = gutenberg.raw("blake-poems.txt")
tokenized = sent_tokenize(sample)
for i in tokenized[:2]:
            words = nltk.word_tokenize(i)
            tagged = nltk.pos_tag(words)
            print(tagged)

运行上述程序时，我们得到以下输出 −

[([', 'JJ'), (Poems', 'NNP'), (by', 'IN'), (William', 'NNP'), (Blake', 'NNP'), (1789', 'CD'), 
(]', 'NNP'), (SONGS', 'NNP'), (OF', 'NNP'), (INNOCENCE', 'NNP'), (AND', 'NNP'), (OF', 'NNP'), 
(EXPERIENCE', 'NNP'), (and', 'CC'), (THE', 'NNP'), (BOOK', 'NNP'), (of', 'IN'), 
(THEL', 'NNP'), (SONGS', 'NNP'), (OF', 'NNP'), (INNOCENCE', 'NNP'), (INTRODUCTION', 'NNP'), 
(Piping', 'VBG'), (down', 'RP'), (the', 'DT'), (valleys', 'NN'), (wild', 'JJ'), 
(,', ','), (Piping', 'NNP'), (songs', 'NNS'), (of', 'IN'), (pleasant', 'JJ'), (glee', 'NN'),
 (,', ','), (On', 'IN'), (a', 'DT'), (cloud', 'NN'), (I', 'PRP'), (saw', 'VBD'), 
 (a', 'DT'), (child', 'NN'), (,', ','), (And', 'CC'), (he', 'PRP'), (laughing', 'VBG'), 
 (said', 'VBD'), (to', 'TO'), (me', 'PRP'), (:', ':'), (``', '``'), (Pipe', 'VB'),
 (a', 'DT'), (song', 'NN'), (about', 'IN'), (a', 'DT'), (Lamb', 'NN'), (!', '.'), (u"''", "''")]