World's Best AI Learning Platform with profoundly Demanding Certification Programs
Designed by IITians, only for AI Learners.
Download our e-book of Introduction To Python
Related Blog
Top Discussion
USA(+256704300651)Top Powerful Love Spells That Really Work By Powerful Revenge Spell Caster in# Canada, #Australia #,UK,//USA Black magic IN >@>USA,/ UK,/ Kuwait,/ Germany, +256704300651--- …Extremely Powerful Death Spells Caster To Kill An Enemy With No Evidence / Ex Husband How can I write Python code to change a date string from "mm/dd/yy hh: mm" format to "YYYY-MM-DD HH: mm" format? +256704300651- Psychic lost love Spell Caster // Divorce Spell // Marriage Proposal A SPELL CASTER/╬ ₩௹]+256704300651₩௹]╬MAMA ZAMIE A TRADITIONAL HEALER /SANGOMA AND A SPIRITUAL HEALER in BELGIUM, +256704300651 == WASHINGTON DC// TOP DEATH SPELLS @POWERFUL REVENGE DEATH SPELL CASTER IN USA,CANADA, UK, CALIFONIA, MEXICO, IRELAND, NY, CANADA, LONDON, SWEDEN, BOTSWANA Exception Type: JSONDecodeError at /update/ Exception Value: Expecting value: line 1 column 1 (char 0) FORTUNER TELLER-(( +256704300651))- Most Effective death revenge Spells Caster USA, UK, BAHAMAS, BAHRAIN, NEW ZEALAND,SPAIN AUSTRALIA SOUTH SUDAN Join Discussion
Top Courses
Master's In Artificial Intelligence Job Guarantee Program
4.5 (1,292 Ratings)
559 Learners
Webinars
Live Masterclass on : "How Machine Get Trained in Machine Learning?"
Jun 1st (5:32 PM) 515 Registered
Python - Stemming Algorithms
Neha Kumawat
a year ago
Table of Contents
- Introduction
- Stemming Algorithms
Introduction
In the areas of Natural Language Processing, we come across a 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.
Stemming Algorithms
There are three most used stemming algorithms available in nltk.
- PorterStemmer
- LancasterStemmer
- SnowballStemmer
They give a slightly different result. The below example shows the use of all the three stemming algorithms and their result.
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))
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
I hope you enjoyed reading this article and finally, you came
to know about how to perform stemming and its different types.
Like the Blog, then Share it with your friends and colleagues to make this AI community stronger.
To learn more about nuances of Artificial Intelligence, Python Programming, Deep Learning, Data Science and Machine Learning, visit our insideAIML blog page.
Keep Learning. Keep Growing.