Tokenization

Tokenization is the process of dividing text into smaller units, called tokens, such as words, subwords, or characters. It helps AI and natural language processing (NLP) systems analyze and understand text efficiently. Tokens are the building blocks for language models, enabling translation, sentiment analysis, and text generation.

Why it’s important

• Allows AI to process and analyze language in a structured way
• Improves accuracy in machine translation, speech recognition, and search engines
• Helps language models understand context by breaking text into meaningful units
• Enables multilingual AI by adapting to different writing systems and word structures

Real-world example

A machine translation system uses tokenization to:

• Segment sentences into words or subwords for accurate translation
• Handle languages differently, such as separating words in English while recognizing compound characters in Chinese or Japanese
• Improve efficiency by reducing redundancy in translation models
• Enhance contextual understanding, allowing AI to translate phrases more naturally

This article is about

• Definition:
  Tokenization divides text into smaller units (tokens) for AI to process
• Industry relevance:
  Used in NLP applications like machine translation, chatbots, and search engines
• Use case:
  AI translation systems tokenize text to improve accuracy and efficiency in multilingual processing

Tokenization is a fundamental step in AI-driven language understanding, making text processing more efficient and accurate across various applications.