Lemmatization in natural language processing (NLP) is the process of reducing a word to its base or dictionary form, called the lemma, while taking into account the context and part of speech of the word. Lemmatization is a more sophisticated technique than stemming, as it produces a valid word that belongs to the language, instead of a mere root. For example, the lemma of “am”, “are”, and “is” is “be”. Similarly, the lemma of “ran”, “runs”, and “running” is “run”.

Lemmatization is useful in NLP because it can reduce the dimensionality of text data and improve the accuracy of downstream NLP tasks such as information retrieval, text classification, and sentiment analysis. Lemmatization can also help in word sense disambiguation, which is the process of determining the correct meaning of a word in a particular context.

Lemmatization algorithms use various techniques to identify the lemma of a word, such as using a dictionary lookup, applying morphological analysis, and using machine learning algorithms. Some lemmatization algorithms take into account the part of speech of the word, such as whether it is a noun, verb, adjective, or adverb, to produce more accurate results.

Lemmatization can be computationally expensive compared to stemming, but it is often preferred in applications where accuracy is critical, such as in natural language understanding, machine translation, and language modeling.