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Difference Between Stemming And Lemmatization In NLP

This article describes the difference between Stemming and Lemmatization in NLP.

Difference

Stemming crudely chops words to their root form (often producing non-dictionary words). At the same time, lemmatization uses linguistic knowledge to reduce words to their proper base form (lemma), ensuring a valid dictionary word.

The other differences are mentioned in the table below.

Aspect	Stemming	Lemmatization
Approach	Rule-based truncation	Linguistic + dictionary-based
Output	could be a non-word	Always valid words
Speed	Faster	Slower
Accuracy	Lower	Higher
Context awareness	None	Considers POS/context

Real-Time Scenario

Stemming is often used in search engines for speed, while lemmatization is used in sentiment analysis, chatbots, and machine translation, where accuracy and meaning matter

Posted By -	Karan Gupta

Posted On -	Tuesday, November 25, 2025

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