What is the difference between stemming and lemmatization in NLP?

What is the difference between stemming and lemmatization in NLP?

Stemming and lemmatization are both techniques used in natural language processing to reduce words to their base or root form. Stemming cuts off prefixes or suffixes, while lemmatization considers the context and converts a word to its meaningful base form.

Can you give me examples of each?

Sure! For stemming, the word 'running' might be reduced to 'run', while lemmatization would convert 'better' to 'good'.

Which one is more accurate?

Lemmatization is generally more accurate because it takes into account the meaning and context of the word, whereas stemming may produce non-words.

When should I use stemming over lemmatization?

You might use stemming when speed is more important than accuracy, such as in search engines, while lemmatization is better for applications requiring understanding of the text.