標記單詞

標記是文本處理的基本特徵，我們將單詞標記爲語法分類。藉助tokenization和pos_tag函數來爲每個單詞創建標籤。

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)

執行上面示例代碼，得到以下結果 -

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

標籤說明

可以使用以下顯示內置值的程序來描述每個標記的含義。

import nltk

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

當運行上面的程序時，我們得到以下輸出 -

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

標記語料庫

還可以標記語料庫數據並查看該語料庫中每個單詞的標記結果。參考以下實現代碼 -

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)

執行上面示例代碼，得到以下結果 -

[([', '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"''", "''")]

Python文本處理教程