Python 标记词语

标记是文本处理的一个重要特性，我们将单词标记为语法类别。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)

当我们运行上面的程序时，我们会得到以下输出 –

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

标签描述

我们可以使用以下程序来描述每个标签的含义，该程序显示了内置的值。

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: noun, common, singular or mass
    common-carrier cabbage knuckle-duster Casino afghan shed thermostat
    investment slide humour falloff slick wind hyena override subhumanity
    machinist ...
IN: preposition or conjunction, subordinating
    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: determiner
    all an another any both del each either every half la many much nary
    neither no some such that the them these this those

标注语料库

我们也可以对语料库数据进行标注，并查看语料库中每个单词的标注结果。

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)

执行上述程序后，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"''", "''")]