What is Tokenization?

What Is Tokenization?

Tokenization is one of the most fundamental steps in Natural Language Processing. It is the process of splitting text into smaller, meaningful units called tokens. These tokens can be words, subwords, characters, or even sentences, depending on the tokenization strategy used. Every NLP application — from chatbots and search engines to sentiment analysis and machine translation — begins with tokenization.

Think of tokenization as the first step in teaching a computer to read. Before a machine can understand a sentence, it needs to break it down into manageable pieces, just as a child learning to read first identifies individual words before understanding sentences.

How Tokenization Works

There are several tokenization approaches, each with different strengths:

Word Tokenization The simplest approach splits text at spaces and punctuation. "The cat sat on the mat" becomes ["The", "cat", "sat", "on", "the", "mat"]. While intuitive, this approach struggles with compound words, contractions, and languages that do not use spaces between words (like Thai and Chinese).

Subword Tokenization Modern NLP models primarily use subword tokenization, which breaks words into smaller meaningful units. The word "unhappiness" might become ["un", "happi", "ness"]. This approach handles rare words and new vocabulary effectively because even unfamiliar words can be represented as combinations of known subword units. Byte-Pair Encoding (BPE) and WordPiece are popular subword methods used by models like GPT and BERT.

Character Tokenization Splits text into individual characters. This handles any word in any language but produces very long token sequences, making processing slower and more expensive.

Sentence Tokenization Splits text into sentences rather than words, useful as a preprocessing step for tasks like summarization or translation that operate on sentence-level units.

Why Tokenization Matters for Business

Tokenization may seem like a purely technical concept, but it has direct implications for business AI implementations:

Cost Implications Most AI language services — including OpenAI's GPT models, Google's language APIs, and other NLP services — charge by the token. Understanding tokenization helps businesses estimate and optimize their AI costs. A sentence in English might tokenize into 10-15 tokens, while the same sentence in Thai or Vietnamese might produce more tokens due to different linguistic structures, directly affecting costs for multilingual businesses.

Model Performance The choice of tokenizer affects how well an NLP model understands your content. Tokenizers trained primarily on English text may handle Southeast Asian languages less efficiently, using more tokens to represent the same meaning and potentially reducing model accuracy.

Context Window Limits AI models have a maximum number of tokens they can process at once (the context window). Understanding tokenization helps businesses plan how much text they can send to an AI model in a single request — critical for applications like document analysis and long-form content generation.

Search and Information Retrieval Search engines and document retrieval systems rely on tokenization to match queries with content. The tokenization approach determines what constitutes a searchable unit, affecting search accuracy and relevance.

Tokenization Challenges in Southeast Asian Languages

Southeast Asia's linguistic diversity creates specific tokenization challenges:

• Thai: Thai script does not use spaces between words, making word boundary detection a significant challenge. Specialized Thai tokenizers use dictionaries and statistical models to identify word boundaries
• Vietnamese: While Vietnamese uses spaces, it uses them between syllables rather than between words. Multi-syllable words require additional processing to identify
• Bahasa Indonesia and Malay: These languages use extensive prefixing and suffixing (affixation) that affects how words should be tokenized for optimal NLP performance
• Chinese characters: Widely used in Singapore and Malaysia, Chinese text requires character-based or word-based tokenization without the benefit of space separators
• Code-switching: Text that mixes languages (common in Southeast Asian communication) requires tokenizers that can handle multiple scripts and languages within the same text

Tokenization in Practice: What Business Leaders Should Know

Understanding AI Pricing When evaluating AI service costs, request token count estimates for your typical content. A customer support chatbot processing 10,000 conversations per month might use very different token volumes depending on the languages involved. Get cost projections based on actual content samples rather than generic estimates.

Optimizing AI Applications Content structure affects tokenization and therefore AI costs and performance:

• Clear, concise text produces fewer tokens and costs less to process
• Removing unnecessary formatting and boilerplate text reduces token count
• Structuring prompts efficiently for AI models saves tokens and improves response quality

Evaluating NLP Solutions When selecting NLP tools or AI platforms, ask about their tokenization approach for the languages your business uses. A platform that tokenizes Thai or Vietnamese efficiently will deliver better performance and lower costs for ASEAN-focused businesses.

Tokenization and Model Selection

Different AI models use different tokenizers, which affects their performance across languages:

• GPT models use BPE tokenization optimized for English and major European languages
• Multilingual BERT uses WordPiece tokenization with a vocabulary spanning 104 languages
• Language-specific models trained for particular Southeast Asian languages often use custom tokenizers optimized for those languages

When selecting AI models for multilingual business applications, understanding how each model tokenizes your languages helps predict both performance and cost.

Practical Takeaways

For business leaders, tokenization comes down to three practical considerations: it affects your AI costs (more tokens equals higher costs), your AI accuracy (better tokenization equals better understanding), and your AI capacity (token limits determine how much text you can process at once). When evaluating AI solutions, always test with content in the actual languages and formats your business uses.