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How is stemming different from lemmatization?
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Stemming and lemmatization are both text normalization techniques used in Natural Language Processing (NLP) to reduce words to their base or root form. The goal of these processes is to treat different variations of a word (such as plurals, tenses, and derivations) as the same word, allowing algorithms to process text more effectively by reducing vocabulary size and improving the performance of downstream tasks like search, classification, or machine translation.
However, while they serve similar purposes, stemming and lemmatization differ significantly in how they transform words into their base forms.
Stemming:
Definition: Stemming is a rule-based process of chopping off the ends of words to reduce them to their root form, often without regard to whether the result is a valid word. The resulting root word, known as the “stem,” may not always be a dictionary word.
