Stemming & lemmatization are two key techniques in natural language processing!
Stemming chops off word endings to get to the base form. For example, ‘running’ becomes ‘run,’ and ‘studies’ might just become ‘studi.’ It’s fast but not always precise.
“Now, lemmatization goes a step further. It reduces words to their dictionary form—so ‘running’ also becomes ‘run,’ but ‘better’ becomes ‘good.’ It considers context, making it more accurate, but it’s a bit slower.”
So, what’s the difference? Stemming is quick and efficient, perfect for large datasets or when speed is key. Lemmatization is more accurate and context-aware, ideal when precision matters.