Tokenization is the process of breaking down a text into individual tokens, which are usually words or groups of words that have a meaning in the context of the text. Tokenization is an important step in natural language processing (NLP) because it enables a computer to analyze the text and perform various operations on it.

There are different approaches to tokenization, but the most common one is to split the text into individual words, which are then treated as separate tokens. This approach assumes that words are the basic building blocks of a text, and that they carry the main semantic information.

However, tokenization can be more complex than just splitting a text into individual words. For example, some words consist of more than one token, such as “New York” or “Donald Trump”. In such cases, a tokenizer may choose to treat the multi-word expression as a single token, or to split it into two or more tokens.

Tokenization can also involve other types of units, such as sentences, paragraphs, or even characters. The choice of tokenization method depends on the specific task and the nature of the text being analyzed.