What is the difference between Stemming and Lemmatization in NLP
Stemming and lemmatization are both techniques used in Natural Language Processing (NLP) to reduce words to their base or root forms, but they differ in their approaches and the results they produce.
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Stemming:
- Stemming is a heuristic-based approach that involves removing prefixes or suffixes from words to obtain their base forms. Stemming algorithms apply a set of rules or algorithms to truncate words, regardless of whether the resulting stems are valid words in the language.
- Stemming is typically faster and less computationally intensive compared to lemmatization, as it relies on simple string manipulation rules without considering linguistic context.
- Stemming may produce stems that are not actual words, but they can still be useful for capturing the core meaning or semantic content of the original words.
- Example: The word "running" may be stemmed to "run", and the word "cats" may be stemmed to "cat".
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Lemmatization:
- Lemmatization, on the other hand, is a more linguistically informed approach that involves reducing words to their base or canonical forms, known as lemmas. Lemmatization takes into account the morphological analysis of words and their part-of-speech (POS) tags to derive their lemmas from a lexical dictionary or database.
- Lemmatization produces valid words in the language and retains the grammatical category or part-of-speech of the original word.
- Lemmatization is usually slower and more complex compared to stemming, as it requires access to a lexicon or database of lemmas and involves more sophisticated linguistic analysis.
- Example: The word "running" may be lemmatized to "run", and the word "cats" may be lemmatized to "cat".
In summary, the main differences between stemming and lemmatization in NLP are:
- Stemming is a heuristic-based approach that truncates words to obtain their base forms, while lemmatization is a linguistically informed approach that derives words' base or canonical forms based on their morphological analysis.
- Stemming is faster and less accurate, producing stems that may not always be valid words, while lemmatization is slower and more accurate, producing valid words with their grammatical categories preserved.
