What is the purpose of tokenization in NLP?

Tokenization in Natural Language Processing (NLP) involves the process of breaking down text into smaller components, typically individual words, phrases, or symbols called tokens. This is a crucial step in text analysis because it allows for a more structured representation of unstructured text data.

By converting a sentence into tokens, NLP systems can more easily analyze the meaning and context of the text. This segmentation makes it simpler to perform further processing tasks such as parsing, sentiment analysis, and machine learning model training, as each token can be treated as a separate entity with its own significance.

Other choices don't accurately represent the primary function of tokenization. Visual representations of text pertain to techniques like word clouds or graphs but do not focus on breaking down the text into manageable parts. Classifying sentences by length does not involve tokenization, as this process is about number of words or characters rather than their individual components. Lastly, generating unique identifiers for texts refers more to the field of data management and indexing rather than the specific operation of tokenization in NLP.