What does tokenization refer to in Natural Language Processing?

Tokenization in Natural Language Processing (NLP) refers to the process of breaking down text or speech into smaller units called tokens, which can be words, phrases, or even characters. This is a crucial step in preprocessing data for various NLP tasks, as it helps in understanding the structure and meaning of the language being analyzed.

By dividing the text into tokens, NLP systems can more easily analyze and manipulate the data. For example, in text analysis, individual tokens allow for tasks like word counting, identifying keywords, or performing sentiment analysis. This forms the foundational step for further operations such as parsing, part-of-speech tagging, and other more complex processing.

The other options, while related to different aspects of language processing or audio analysis, do not specifically define tokenization. Breaking audio signals into separate frequencies pertains to audio signal processing rather than NLP. Grouping words into phrases relates to syntactic analysis but is a follow-up step after tokenization. Transforming sentences into numeric codes typically refers to techniques used after tokenization, such as vectorization or embedding, which represent the tokens in a form suitable for machine learning algorithms.