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 root form using dictionaries and morphological analysis
3. 3Compare the results of stemming and lemmatization to determine the best approach for a specific NLP task
4. 4Configure NLP pipelines to incorporate text normalization techniques
5. 5Test the impact of text normalization on NLP model performance

NLP engineers and data scientists on a team benefit from understanding text normalization techniques to enhance text analysis and machine learning model performance

💡 Stemming and lemmatization are two different text normalization techniques that can significantly impact NLP model performance

📚 Improve NLP with text normalization! Stemming vs. Lemmatization: which one to use? #NLP #TextAnalysis