What is the purpose of the lemmatization process in NLP?

Lemmatization is a crucial process in Natural Language Processing (NLP) that primarily aims to reduce words to their base or root forms, known as lemmas. This transformation is essential for understanding the underlying meaning of the words in a text, as it allows systems to recognize different inflected forms of a word as being the same base word. For instance, both "running" and "ran" would be reduced to their base form "run".

This normalization helps improve the performance of NLP tasks such as text analysis, sentiment analysis, and information retrieval, as it reduces the dimensionality of the text data and focuses on the core concept behind the words. By converting words into their base forms, lemmatization enhances the ability of algorithms to process and analyze language, ensuring that similar meanings are grouped together.

The other options, while related to language processing in various ways, do not capture the essence of lemmatization as effectively as the process of reducing words to their fundamental forms does.