Text Normalisation Techniques in NLP: Stemming vs. Lemmatization

1. 1Apply stemming to reduce words to their base form using algorithms like Porter Stemmer
2. 2Use lemmatization to convert words to their base or dictionary form using tools like WordNet
3. 3Evaluate the performance of NLP models with and without text normalization
4. 4Configure text preprocessing pipelines to include normalization techniques
5. 5Test the impact of different normalization techniques on model accuracy

NLP engineers and data scientists on a team benefit from understanding text normalization techniques to preprocess text data effectively and improve model outcomes. This knowledge is essential for developing accurate and reliable NLP models.

💡 Stemming and lemmatization are two fundamental text normalization techniques that can significantly improve NLP model accuracy by reducing word variations

📊 Improve NLP model performance with text normalization techniques like stemming and lemmatization!