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Tokenisation in Natural Language Processing
How text is segmented into machine-readable units for NLP pipelines and large language models.
Definition
How text is segmented into machine-readable units for NLP pipelines and large language models.
How It Works
Tokenisation in Natural Language Processing helps teams build predictable AI and translation workflows by setting clear expectations for quality, consistency, and decision-making.
In production environments, this concept is applied with process controls such as human review, terminology alignment, and repeatable quality checks across multilingual content.
Key Concepts
- core principle of tokenisation in natural language processing
- workflow-level implementation
- terminology and quality consistency
- human validation before publication
Where It Is Used
- localisation workflows
- AI translation pipelines
- multilingual content production
- cross-referencing related concepts such as Terminology Extraction