In natural language processing, what is tokenization primarily used for?

Tokenization is a fundamental process in natural language processing (NLP) that involves breaking down a text into smaller components, typically words or terms. By separating sentences into individual words, tokenization makes it easier for algorithms to process and analyze the text for various applications, such as building language models, performing sentiment analysis, or facilitating text classification. This step is essential because it transforms unstructured text into a format that machine learning models can understand and work with effectively.

While tasks like translating text, identifying meanings, and analyzing logical flow are important in NLP, they often rely on the initial step of tokenization to break down the input text into the entities that can then be processed or transformed as needed. Therefore, tokenization is a critical foundational technique that enables further analysis and manipulation of textual data.