Python Text Processing - Stemming Algorithms

In the areas of Natural Language Processing we come across situation where two or more words have a common root. For example, the three words - agreed, agreeing and agreeable have the same root word agree. A search involving any of these words should treat them as the same word which is the root word. So, it becomes essential to link all the words into their root word. The NLTK library has methods to do this linking and give the output showing the root word.

There are three most used stemming algorithms available in nltk. They give slightly different result. The below example shows the use of all the three stemming algorithms and their result.

Example - Usage of Stemming Algorithms

main.py

import nltk
from nltk.stem.porter import PorterStemmer
from nltk.stem.lancaster import LancasterStemmer
from nltk.stem import SnowballStemmer 

porter_stemmer = PorterStemmer()
lanca_stemmer = LancasterStemmer()
sb_stemmer = SnowballStemmer("english",)

word_data = "Aging head of famous crime family decides to transfer his position to one of his subalterns" 
# First Word tokenization
nltk_tokens = nltk.word_tokenize(word_data)
#Next find the roots of the word
print('***PorterStemmer****\n')
for w_port in nltk_tokens:
   print("Actual: %s  || Stem: %s"  % (w_port,porter_stemmer.stem(w_port)))

print('\n***LancasterStemmer****\n')
for w_lanca in nltk_tokens:
   print("Actual: %s  || Stem: %s"  % (w_lanca,lanca_stemmer.stem(w_lanca)))
print('\n***SnowballStemmer****\n')

for w_snow in nltk_tokens:
   print("Actual: %s  || Stem: %s"  % (w_snow,sb_stemmer.stem(w_snow)))

Output

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

***PorterStemmer****

Actual: Aging  || Stem: age
Actual: head  || Stem: head
Actual: of  || Stem: of
Actual: famous  || Stem: famou
Actual: crime  || Stem: crime
Actual: family  || Stem: famili
Actual: decides  || Stem: decid
Actual: to  || Stem: to
Actual: transfer  || Stem: transfer
Actual: his  || Stem: hi
Actual: position  || Stem: posit
Actual: to  || Stem: to
Actual: one  || Stem: one
Actual: of  || Stem: of
Actual: his  || Stem: hi
Actual: subalterns  || Stem: subaltern

***LancasterStemmer****

Actual: Aging  || Stem: ag
Actual: head  || Stem: head
Actual: of  || Stem: of
Actual: famous  || Stem: fam
Actual: crime  || Stem: crim
Actual: family  || Stem: famy
Actual: decides  || Stem: decid
Actual: to  || Stem: to
Actual: transfer  || Stem: transf
Actual: his  || Stem: his
Actual: position  || Stem: posit
Actual: to  || Stem: to
Actual: one  || Stem: on
Actual: of  || Stem: of
Actual: his  || Stem: his
Actual: subalterns  || Stem: subaltern

***SnowballStemmer****

Actual: Aging  || Stem: age
Actual: head  || Stem: head
Actual: of  || Stem: of
Actual: famous  || Stem: famous
Actual: crime  || Stem: crime
Actual: family  || Stem: famili
Actual: decides  || Stem: decid
Actual: to  || Stem: to
Actual: transfer  || Stem: transfer
Actual: his  || Stem: his
Actual: position  || Stem: posit
Actual: to  || Stem: to
Actual: one  || Stem: one
Actual: of  || Stem: of
Actual: his  || Stem: his
Actual: subalterns  || Stem: subaltern