What does tokenization refer to in natural language processing?

What does tokenization refer to in natural language processing?

• A. Encrypting data for security.
• B. Breaking text into tokens.
• C. Compressing files for storage.
• D. Converting words into grammatical rules.

Answer: (B)

Tokenization is the process of breaking text into tokens, the basic units that NLP models work with. These tokens can be words, subword units, or individual characters, depending on the tokenizer. Tokenization makes it possible to convert raw text into a sequence of discrete pieces that can be mapped to numerical representations for processing. It also helps control vocabulary size and handles unknown words through subword units. For example, "Hello, world!" might be tokenized as ["Hello", ",", "world", "!"] in a simple word-level approach, or as ["Hello", "world"] if punctuation is stripped. In subword tokenization schemes, a word like "tokenization" might be split into pieces such as ["token", "ization"] or similar, enabling the model to handle unseen words. Some languages without clear spaces, like Chinese or Japanese, require dedicated segmentation to produce meaningful tokens. The other activities—encrypting data, compressing files, or turning words into grammatical rules—are different processes and not tokenization.

Tokenization is the process of breaking text into tokens, the basic units that NLP models work with. These tokens can be words, subword units, or individual characters, depending on the tokenizer. Tokenization makes it possible to convert raw text into a sequence of discrete pieces that can be mapped to numerical representations for processing. It also helps control vocabulary size and handles unknown words through subword units. For example, "Hello, world!" might be tokenized as ["Hello", ",", "world", "!"] in a simple word-level approach, or as ["Hello", "world"] if punctuation is stripped. In subword tokenization schemes, a word like "tokenization" might be split into pieces such as ["token", "ization"] or similar, enabling the model to handle unseen words. Some languages without clear spaces, like Chinese or Japanese, require dedicated segmentation to produce meaningful tokens. The other activities—encrypting data, compressing files, or turning words into grammatical rules—are different processes and not tokenization.