The process of dividing text into smaller units, such as words or sentences, to facilitate analysis and processing. In tokenization, text is segmented into individual tokens, which can be words, phrases, or punctuation marks, depending on the specific task or requirements. This technique is fundamental in natural language processing (NLP) tasks, enabling algorithms to parse and understand textual data more effectively. By breaking down text into manageable units, tokenization forms the basis for various NLP tasks, including part-of-speech tagging, named entity recognition, and syntactic analysis, allowing algorithms to extract meaningful information and derive insights from text data.