What does tokenization refer to in natural language processing (NLP)?

Tokenization in natural language processing (NLP) is fundamentally about the process of breaking down text into individual components, known as tokens. These tokens can be words, phrases, symbols, or even characters, depending on the level of granularity one is interested in.

The purpose of tokenization is to convert the complexities of human language into manageable pieces that can be more easily analyzed by algorithms. This step is critical because it sets the stage for various NLP tasks, such as text classification, sentiment analysis, and machine translation. By transforming a continuous stream of text into discrete tokens, it allows models to understand and process language more effectively.

For example, in a sentence like "Natural language processing is fascinating," tokenization would separate this into individual words: ["Natural", "language", "processing", "is", "fascinating"]. From these tokens, the models can learn language patterns, relationships, and structure.

Thus, the correct choice reflects a core function of NLP that is essential for further processing and analysis. The other options, while relating to NLP, describe different aspects or methodologies that do not capture the essence of tokenization itself.