Python - Tagging Words


Advertisements

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")
tagged_text=nltk.pos_tag(text)
print(tagged_text)

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

nltk.help.upenn_tagset('NN')
nltk.help.upenn_tagset('IN')
nltk.help.upenn_tagset('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)
            print(tagged)

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

Useful Video Courses


Video

Python Online Training

187 Lectures 17.5 hours

Malhar Lathkar

Video

Python Essentials Online Training

55 Lectures 8 hours

Arnab Chakraborty

Video

Learn Python Programming in 100 Easy Steps

136 Lectures 11 hours

In28Minutes Official

Video

Python with Data Science

Best Seller

75 Lectures 13 hours

Eduonix Learning Solutions

Video

Python 3 from scratch to become a developer in demand

Best Seller

70 Lectures 8.5 hours

Lets Kode It

Video

Python Data Science basics with Numpy, Pandas and Matplotlib

Most Popular

63 Lectures 6 hours

Abhilash Nelson

Advertisements