What does the term "tokenization" mean in NLP?

The term "tokenization" in Natural Language Processing (NLP) refers to the process of dividing text into smaller components, typically words or phrases, that can be analyzed. This step is fundamental in NLP as it prepares the text data for further processing, such as parsing, understanding sentence structure, or training machine learning models. By breaking down a large chunk of text into manageable pieces, tokenization allows algorithms to recognize and interpret patterns, relationships, and meanings within the input data.

For example, in a sentence like "The cat sat on the mat," tokenization would separate this into the individual tokens: "The," "cat," "sat," "on," "the," "mat." Each of these tokens can then be utilized for various applications, such as sentiment analysis, language modeling, and more.

In understanding why this choice is accurate, it emphasizes how critical tokenization is to the initial stages of text analysis in NLP, as it enables other processes to function effectively, whether they are dealing with text classification, named entity recognition, or more complex linguistic tasks.