NLP - Tokenization

 What is tokenization?

Tokenization is splitting raw text into smaller units called tokens—these might be words, subwords, or individual characters. Why bother? Imagine you’re handed a dense, unformatted sentence without spaces or punctuation: "GenerativeAIisfascinatingandisthefuture". Without tokenization, deciphering meaningful segments becomes nearly impossible. Humans can intuitively parse this into generative AI is fascinating and is the future, but machines require explicit instructions to recognize word boundaries. Tokenization bridges this gap, enabling machines to identify and separate individual words or meaningful subunits within the text. 

In reality, tokenization results may vary depending on the tokenizer used. The above image is for demonstration purposes only. Moreover, languages vary widely in their structure and word boundaries. For instance:

• English: Spaces separate words, making basic tokenization relatively straightforward.
  
• Chinese/Japanese: Words often aren’t separated by spaces, requiring statistical models or dictionary-based approaches to segment text.
  
• Social media and code: Hashtags (#MachineLearning), contractions (can't → can + not), and code snippets (int myVar = 5;) all require specialized tokenization strategies.
  

Effective tokenization must account for these linguistic nuances to accurately parse and process text across different languages, ensuring that NLP models remain versatile and applicable in diverse linguistic contexts.

Tokenization can be approached in various ways, each suited to different applications and linguistic complexities:

• Word tokenization splits text into individual words. For example, the sentence "Generative AI is fascinating." becomes["Generative", "AI", "is", "fascinating", "."]. This method is straightforward but may struggle with compound words, contractions (e.g., "don't" might be split into ["don", "'t"] or handled specially), or hyphenated words. Advanced tokenizers use regular expressions or statistical models to manage these cases.
  
• Subword tokenization breaks words into smaller units, which is particularly useful for handling unknown or rare words. For instance, "unhappiness" might be tokenized into ["un", "happiness"]. Take the word “tokenization.” A subword tokenizer might split it into ["token", "ization"], letting the model reuse “token” in “tokenizer” or “tokenized.” This is how models like GPT-4.5, Claude 3.7, and Grok 3 handle obscure terms like “supercalifragilisticexpialidocious” without breaking a sweat.
  

Note: Modern large language models often employ subword tokenization methods like byte pair encoding (BPE) or SentencePiece to handle massive vocabularies efficiently. Tools like “TikToken” are specifically designed for GPT-based models to keep track of token counts and ensure prompts fit within token limits. Context window sizes are evolving rapidly! We will take a closer look at these advanced tokenizers later as we proceed.

• Character tokenization splits text into individual characters, such as
  ["G", "e", "n", "e", "r", "a", "t", "i", "v", "e", ...]. While this method captures every detail, it often results in longer sequences that can be computationally intensive for models to process.
  

By breaking down text into tokens, these models can better understand and manipulate language, enabling them to produce human-like responses. Effective tokenization ensures that generative AI systems can handle a vast array of linguistic inputs—from simple sentences to complex technical jargon—maintaining accuracy and fluency in their outputs.