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Similarities: Both stemming and lemmatization are techniques used in natural language processing (NLP) to reduce words to their base or root form, which helps in normalization and improves text analysis.
Differences:
Stemming usually involves removing suffixes and prefixes from words to get to the root form, but it may result in non-existent or incorrect words. Lemmatization, on the other hand, maps words to their lemma, or dictionary form, which is usually a valid word.
Stemming is faster and simpler compared to lemmatization but may not always produce accurate results. Lemmatization, while more accurate, can be slower due to its reliance on a dictionary or vocabulary.