What is the purpose of tokenization in natural language processing?

Tokenization is a fundamental step in Natural Language Processing (NLP) that involves breaking down a continuous stream of text into smaller, meaningful units called tokens. These tokens can be words, subwords, or even characters, depending on the tokenization strategy. This process is crucial because most NLP models cannot directly process raw text; they require numerical representations of these smaller units. By tokenizing text, models can analyze and understand the structure and meaning of language more effectively. Encrypting textual data (option 1) is about securing information, not preparing it for NLP. Compressing text files (option 2) reduces file size, which is unrelated to linguistic analysis. Translating text between languages (option 4) is a separate NLP task that often uses tokenization, but is not the purpose of tokenization itself.