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What Is Stemming & Lemmatization?

Stemming and lemmatization are techniques used in natural language processing (NLP) to reduce words to their base form, known as the root or lemma.

Stemming:

Stemming involves removing suffixes from words to obtain a stem, which is a truncated form of the word. This process is simple and fast but can be inaccurate, as it doesn't consider the word's context or meaning.

Example:

• Running → Run (stem)
• Jumping → Jump (stem)

Lemmatization:

Lemmatization, on the other hand, uses a dictionary-based approach to reduce words to their lemma, which is the canonical or base form of the word. This process considers the word's context, meaning, and grammar to ensure accuracy.

Example:

• Running → Run (lemma)
• Jumping → Jump (lemma)
• Better → Good (lemma, as "better" is a comparative form of "good")

Key differences:

• Stemming is faster but less accurate, while lemmatization is slower but more accurate.
• Stemming doesn't consider context or meaning, while lemmatization does.

Both techniques are used to:

• Reduce dimensionality in text data
• Improve text classification and clustering
• Enhance search engine querying
• Support machine learning models

Popular stemming algorithms include Porter Stemmer and Snowball Stemmer, while popular lemmatization tools include WordNet and NLTK (Natural Language Toolkit).

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