Python | Stemming words with NLTK
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:
- Python3
# import these modulesfrom nltk.stem import PorterStemmerfrom nltk.tokenize import word_tokenize ps = PorterStemmer()# choose some words to be stemmedwords = ["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 modulesfrom nltk.stem import PorterStemmerfrom 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 : languag
Last Updated on November 13, 2021 by admin