Tokenization is the process of breaking down a stream of textual data into smaller chunks called tokens, which can be words, terms, sentences, symbols, or other meaningful elements. Tokenization is the first step in any Natural Language Processing (NLP) pipeline, and it has an important effect on the rest of the pipeline. Tokenization enables machines to understand and organize human language, and it helps in interpreting the meaning of the text by analyzing the sequence of the words.

Tokenization is used to split paragraphs and sentences into smaller units that can be more easily assigned meaning. It is a simple process that takes raw data and converts it into a useful data string. Tokenization is also important in cybersecurity and in the creation of NFTs.

There are different ways to tokenize text, and each methods success relies heavily on the strength of the programming integrated in other parts of the NLP process. Some of the most common tokenization tools used in NLP include NLTK, Gensim, and Keras.

In summary, tokenization is a crucial step in NLP that breaks down a stream of textual data into smaller chunks called tokens, which can be words, terms, sentences, symbols, or other meaningful elements. It helps machines understand and organize human language, and it is the first step in any NLP pipeline.