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”, “choco” to the root word, “chocolate” and “retrieval”, “retrieved”, “retrieves” reduce to the stem “retrieve”.
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 to same root that are of different stems. Under-stemming occurs when two words are stemmed to same root that are 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:
# 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", "programer", "programing", "programers"]
for w in words:
print(w, " : ", ps.stem(w))
Output:
program : program
programs : program
programer : program
programing : program
programers : program
Code #2: Stemming words from sentences
# importing modules
from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize
ps = PorterStemmer()
sentence = "Programers program with programing languages"
words = word_tokenize(sentence)
for w in words:
print(w, " : ", ps.stem(w))
Output :
Programers : program
program : program
with : with
programing : program
languages : languag