What is Tokenization?

What is Tokenization?

• A. Cleaning
• B. Tokenization
• C. Normalization
• D. Lemmatization

Answer: (B)

Tokenization is the process of breaking text into tokens—discrete units that a model can analyze, usually words or subword pieces. It’s the first step in text processing, turning a raw string into a sequence that downstream tasks like embeddings, frequency counts, or classification can work with. How this splitting happens can vary: some tokenizers treat punctuation as separate tokens, others keep punctuation attached or ignore it depending on the rules they follow. This is distinct from cleaning, which removes unwanted characters or noise; normalization, which standardizes text (such as lowercasing or removing accents); and lemmatization, which reduces words to their base Lemma form. Tokenization focuses on dividing the text into meaningful pieces so subsequent steps can operate consistently. For example, “Hello, world!” might tokenize to Hello, punctuation, world, punctuation, or into just words depending on the tokenizer, but the essential idea is that the raw text becomes a sequence of manageable units for processing.

Tokenization is the process of breaking text into tokens—discrete units that a model can analyze, usually words or subword pieces. It’s the first step in text processing, turning a raw string into a sequence that downstream tasks like embeddings, frequency counts, or classification can work with. How this splitting happens can vary: some tokenizers treat punctuation as separate tokens, others keep punctuation attached or ignore it depending on the rules they follow.

This is distinct from cleaning, which removes unwanted characters or noise; normalization, which standardizes text (such as lowercasing or removing accents); and lemmatization, which reduces words to their base Lemma form. Tokenization focuses on dividing the text into meaningful pieces so subsequent steps can operate consistently. For example, “Hello, world!” might tokenize to Hello, punctuation, world, punctuation, or into just words depending on the tokenizer, but the essential idea is that the raw text becomes a sequence of manageable units for processing.