Stemming vs Lemmatization in NLP: Key Differences, Examples, and When to Use Each
Understand the key differences between stemming and lemmatization in NLP, with examples, applications, and guidance on when to use each technique.
Natural language processing (NLP) systems often need to process thousands or millions of words before they can perform tasks such as text classification, sentiment analysis, search, or information retrieval.
The same concept can appear in different grammatical forms—for example, connect, connected, connecting, and connection. Treating every form as completely different can make text analysis less efficient.
This is where stemming and lemmatization come into play. Both are text normalization techniques used to reduce words to a common base form, but they approach the problem differently.
Stemming generally applies simple rules to remove prefixes or suffixes, while lemmatization uses linguistic information to determine a word's meaningful base form.
Understanding the difference between stemming and lemmatization is important when designing an NLP pipeline because...
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