What does the term tokenization refer to in NLP?

Tokenization in Natural Language Processing (NLP) is a fundamental technique that involves splitting input text into smaller pieces known as tokens. These tokens can be individual words, phrases, or even characters, depending on how tokenization is implemented. By breaking down the text into manageable units, tokenization allows models to analyze the structure and meaning of sentences more effectively.

This process is crucial for various NLP tasks such as text classification, sentiment analysis, and machine translation. Once the text is tokenized, further processing can occur, including the calculation of word frequencies or converting tokens into numerical representations. Tokenization serves as a foundational step that sets the stage for deeper analysis and understanding of linguistic data.