What is the difference between stemming and lemmatization?

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A Long Short-Term Memory (LSTM) network is an advanced type of Recurrent Neural Network (RNN) designed to overcome the limitations of traditional RNNs, especially the vanishing and exploding gradient problems. These problems make it difficult for standard RNNs to learn long-term dependencies in sequential data. LSTMs solve this using a special architecture with a cell state and gates that control the flow of information.

Difference Between Stemming and Lemmatization

Both stemming and lemmatization are text preprocessing techniques in Natural Language Processing (NLP) used to reduce words to their root form, but they work differently.

1. Stemming

• Definition: A rule-based process that chops off prefixes or suffixes to reduce a word to its root (stem).

• Method: Uses simple heuristics (e.g., removing -ing, -ed, -s).

• Result: The stem may not be a real dictionary word.

• Example:

  • running → run

  • studies → studi

  • better → bett (incorrect root)

👉 Stemming is fast but crude, often producing non-meaningful stems.

2. Lemmatization

• Definition: A more advanced process that reduces words to their base or dictionary form (lemma).

• Method: Uses linguistic knowledge, vocabulary, and morphological analysis.

• Result: Always produces meaningful words.

• Example:

  • running → run

  • studies → study

  • better → good

👉 Lemmatization is accurate but slower, since it requires a deeper understanding of language.

Key Differences at a Glance

Aspect	Stemming	Lemmatization
Approach	Heuristic, rule-based	Linguistic, dictionary-based
Speed	Faster	Slower
Output	May not be a valid word	Always a valid word
Example	studies → studi	studies → study

👉 In short: Stemming is quick but less accurate, while lemmatization is slower but linguistically correct.

🔑Read More:

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Explain RNN (Recurrent Neural Network).

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