An Unfair Comparison Between Lemmatization and Stemming: Understanding Their Impact in NLP

1. 1Understand the context of NLP and text data cleaning
2. 2Learn the definitions and differences between lemmatization and stemming
3. 3Apply lemmatization using tools like NLTK or spaCy to reduce words to their base form
4. 4Apply stemming using tools like Porter Stemmer to reduce words to their root form
5. 5Compare the results of lemmatization and stemming on a sample dataset to see their impact on model performance

NLP engineers and data scientists can benefit from understanding the strengths and weaknesses of lemmatization and stemming to improve their text data cleaning pipelines

💡 Lemmatization and stemming are two different techniques for reducing words to their base form, with lemmatization being more accurate but also more computationally expensive

💡 Lemmatization vs Stemming in NLP: which one is better for text data cleaning? 🤔