Python – Tagging Words

Tagging is an essential feature of text processing where we tag the words into grammatical categorization. We take help of tokenization and pos_tag function to create the tags for each word.

import nltk

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

When we run the above program, we get the following output −

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

Tag Descriptions

We can describe the meaning of each tag by using the following program which shows the in-built values.

import nltk'NN')'IN')'DT')

When we run the above program, we get the following output −

>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

Tagging a Corpus

We can also tag a corpus data and see the tagged result for each word in that corpus.

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)

When we run the above program we get the following output −

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