Niloy Kumar Kundu

Niloy Kumar Kundu

Junior Machine Learning Engineer at Nodes Digital Limited

2. Stemming and Lemmatization in NLP

2 minute read

Published:

In Natural Language Processing (NLP), reducing words to their root form is an essential step for various tasks like text analysis and classification. Two common techniques for this are Stemming and Lemmatization. Though they serve a similar purpose, they differ in their approach and results. In this post, we’ll explore both techniques and discuss when to use each one.

What is Stemming?

Stemming is the process of reducing inflected or derived words to their base or root form, often by removing prefixes or suffixes. The root form produced by stemming may not always be a valid word, but it is useful for simplifying text data.

Example of Stemming

  • History -> histori
  • Historical -> histori
  • Finally -> fina
  • Final -> fina
  • Finalized -> fina
  • Going -> go
  • Goes -> go
  • Gone -> go

As you can see, stemming reduces words to their base form, but the result may not always be meaningful or a valid word.

What is Lemmatization?

Lemmatization is the process of reducing words to their base or root form, known as a lemma. Unlike stemming, lemmatization takes into account the context and grammatical structure of the word, ensuring that the root form is a valid and meaningful word.

Example of Lemmatization

  • History -> history
  • Historical -> history
  • Finally -> final
  • Final -> final
  • Finalized -> final
  • Going -> go
  • Goes -> go
  • Gone -> go

Lemmatization gives us the correct root form of the word, preserving its meaning.

When to Use Stemming and Lemmatization?

Use Cases

  • Stemming: Suitable for tasks where the exact meaning of the word is not essential. Examples include:
    • Sentiment analysis
    • Spam classification
    • Tasks involving the detection of positive or negative sentiments
  • Lemmatization: Ideal for tasks where preserving the meaning of the word is critical. Examples include:
    • Chatbots
    • Question-answering systems

Key Differences Between Stemming and Lemmatization

  1. Meaning:
    • Stemming does not guarantee a valid or meaningful word.
    • Lemmatization ensures the root word has meaning and is grammatically correct.
  2. Performance:
    • Stemming is faster and computationally less expensive.
    • Lemmatization takes more time due to its complexity.
  3. Use Cases:
    • Stemming is sufficient for tasks like sentiment analysis where the root meaning of the word is not necessary.
    • Lemmatization is preferred in NLP applications like chatbots and question-answering systems, where the actual meaning of the word is essential.

Conclusion

Stemming and lemmatization are both vital tools in NLP for reducing words to their root forms. The choice between the two depends on the specific use case and the importance of preserving the meaning of the words. Understanding these differences helps in selecting the right approach for your NLP tasks.

Whether you’re building a sentiment analysis model or a chatbot, knowing when to apply stemming versus lemmatization can make a significant difference in the performance and accuracy of your application.

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