Lemmatization
Lemmatization is a natural language processing (NLP) technique that reduces words to their base or dictionary form, known as a lemma, by considering their morphological analysis and part-of-speech context. Unlike stemming, which simply chops off word endings, lemmatization uses vocabulary and grammatical rules to return valid words, such as converting 'running' to 'run' or 'better' to 'good'. It is widely used in text preprocessing to normalize text data for tasks like information retrieval, sentiment analysis, and machine learning.
Developers should learn lemmatization when working on NLP projects that require accurate text normalization, such as search engines, chatbots, or text classification systems, as it improves model performance by reducing word variations to a common form. It is particularly useful in applications where semantic meaning is crucial, like document summarization or language translation, as it preserves the grammatical integrity of words compared to simpler methods like stemming.