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Python | Stemming words with NLTK

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Stemming is the process of producing morphological variants of a root/base word. Stemming programs are commonly referred to as stemming algorithms or stemmers. A stemming algorithm reduces the words “chocolates”, “chocolatey”, and “choco” to the root word, “chocolate” and “retrieval”, “retrieved”, “retrieves” reduce to the stem “retrieve”.

Prerequisite: Introduction to Stemming

Some more example of stemming for root word "like" include:

-> "likes"
-> "liked"
-> "likely"
-> "liking"

Errors in Stemming: There are mainly two errors in stemming – Overstemming and Understemming. Overstemming occurs when two words are stemmed from the same root that are of different stems. Under-stemming occurs when two words are stemmed from the same root that is not of different stems.

Applications of stemming are:  

  • Stemming is used in information retrieval systems like search engines.
  • It is used to determine domain vocabularies in domain analysis.

Stemming is desirable as it may reduce redundancy as most of the time the word stem and their inflected/derived words mean the same.

Below is the implementation of stemming words using NLTK:

Code #1:  

Python3

# import these modules
from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize
 
ps = PorterStemmer()
 
# choose some words to be stemmed
words = ["program", "programs", "programmer", "programming", "programmers"]
 
for w in words:
    print(w, " : ", ps.stem(w))

                    

Output: 

program  :  program
programs  :  program
programmer  :  program
programming  :  program
programmers  :  program

Code #2: Stemming words from sentences

Python3

# importing modules
from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize
 
ps = PorterStemmer()
 
sentence = "Programmers program with programming languages"
words = word_tokenize(sentence)
 
for w in words:
    print(w, " : ", ps.stem(w))

                    

Output : 

Programmers  :  program
program  :  program
with  :  with
programming  :  program
languages  :  language

Code #3: Using reduce():

Algorithm :

  1. Import the necessary modules: PorterStemmer and word_tokenize from nltk, and reduce from functools.
  2. Create an instance of the PorterStemmer class.
  3. Define a sample sentence to be stemmed.
  4. Tokenize the sentence into individual words using word_tokenize.
  5. Use reduce to apply the PorterStemmer to each word in the tokenized sentence, and join the stemmed words back into a string.
  6. Print the stemmed sentence.
install the pip install nltk

Python3

from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize
from functools import reduce
 
ps = PorterStemmer()
 
sentence = "Programmers program with programming languages"
words = word_tokenize(sentence)
 
# using reduce to apply stemmer to each word and join them back into a string
stemmed_sentence = reduce(lambda x, y: x + " " + ps.stem(y), words, "")
 
print(stemmed_sentence)
#This code is contrinuted by Pushpa.

                    

Output:

Programm program with program language

Time complexity:
The time complexity of this code is O(nlogn), where n is the length of the input sentence. The tokenizer and stemmer functions have a linear time complexity of O(n), but the reduce function has a logarithmic time complexity of O(logn) since it processes elements in pairs.

Space complexity:
The space complexity of this code is O(n), where n is the length of the input sentence. This is because the reduce function creates a new string object that has the same length as the input sentence. The tokenizer and stemmer functions do not increase the space complexity significantly.



Last Updated : 15 Apr, 2023
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