Lemmatization vs Stemming in Natural language processing. #shorts

Lemmatization and stemming are two common techniques used in natural language processing (NLP) and text mining to reduce words to their base or root form. Both methods aim to normalize words, enabling the analysis of text based on their core meaning rather than their specific inflections or variations. However, there are some differences between lemmatization and stemming.

Stemming is a simpler and more rule-based process. It involves removing prefixes, suffixes, and other affixes from words to extract the stem or root. The resulting stems may not be actual words, but they represent the basic form of the word. For example, applying stemming to the words "running," "runner," and "runs" would all result in the stem "run."

Lemmatization, on the other hand, takes into account the morphological analysis of words and maps them to their lemma or base form. It considers the word's part of speech (POS) and applies linguistic rules and databases to obtain the appropriate base form. This process produces valid words that can be found in a dictionary. For example, the lemma of the words "running," "runner," and "runs" would all be "run."