tokenize: meaning, definition, pronunciation and examples

Medium in technical registers, low in everyday usage.
UK/ˈtəʊkənaɪz/US/ˈtoʊkənaɪz/

Technical, formal.

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Quick answer

What does “tokenize” mean?

To break down text or data into smaller units called tokens, such as words or symbols.

Audio

Pronunciation

Definition

Meaning and Definition

To break down text or data into smaller units called tokens, such as words or symbols.

In computing and linguistics, to process input by splitting it into tokens for analysis, parsing, or further processing, often in natural language processing or programming contexts.

Dialectal Variation

British vs American Usage

Differences

No significant differences in meaning or usage between British and American English.

Connotations

Neutral in both variants, primarily technical.

Frequency

Equally common in technical contexts such as computer science and linguistics in both regions.

Grammar

How to Use “tokenize” in a Sentence

transitive: tokenize + object (e.g., tokenize the corpus)passive: be tokenized (e.g., the data was tokenized)

Vocabulary

Collocations

strong
tokenize texttokenize datatokenize input
medium
tokenize the stringtokenize documentstokenize sentences
weak
tokenize efficientlytokenize manuallytokenize rapidly

Examples

Examples of “tokenize” in a Sentence

verb

British English

  • The algorithm will tokenise the entire corpus for linguistic analysis.
  • You must tokenise the input before feeding it to the model.

American English

  • The program needs to tokenize the dataset before training.
  • We tokenize the text to extract keywords.

adjective

British English

  • The tokenised text is stored in a separate file.
  • Use the tokenised version for faster processing.

American English

  • The tokenized data is ready for the next phase.
  • Access the tokenized output from the server.

Usage

Meaning in Context

Business

Rarely used outside tech-related business discussions, e.g., in data analytics projects.

Academic

Common in computer science, linguistics, and data science research papers.

Everyday

Almost never used in casual conversation; limited to technical enthusiasts or professionals.

Technical

Frequently used in programming, natural language processing, machine learning, and software development.

Vocabulary

Synonyms of “tokenize”

Vocabulary

Antonyms of “tokenize”

Watch out

Common Mistakes When Using “tokenize”

  • Confusing 'tokenize' with 'parse'—tokenization is a subset of parsing focusing on splitting, while parsing involves grammatical analysis.
  • Using 'tokenize' for non-text data without clarification, though it can apply to any sequential data.

FAQ

Frequently Asked Questions

Tokenization is the process of splitting text or data into smaller units called tokens, such as words or symbols, often used in computing and linguistics.

It is frequently used in natural language processing, programming, data science, machine learning, and computational linguistics.

Tokenization focuses on breaking input into tokens, while parsing involves analyzing the grammatical structure and relationships between those tokens.

Yes, in British English, it can be spelled 'tokenise', while American English typically uses 'tokenize'; however, both are acceptable and understood in technical contexts.

To break down text or data into smaller units called tokens, such as words or symbols.

Tokenize is usually technical, formal. in register.

Tokenize: in British English it is pronounced /ˈtəʊkənaɪz/, and in American English it is pronounced /ˈtoʊkənaɪz/. Tap the audio buttons above to hear it.

Learning

Memory Aids

Mnemonic

Think of 'token' as a small piece or chip; to tokenize is to turn something into tokens, like breaking a chocolate bar into pieces.

Conceptual Metaphor

Breaking a whole into identifiable, manageable parts for systematic processing, akin to chopping vegetables for cooking.

Practice

Quiz

Fill in the gap
To preprocess the text for analysis, we must it into tokens.
Multiple Choice

What is the primary purpose of tokenization in computing?