Tokenization

Tokenization is a core step in Natural Language Processing (NLP) that splits raw text into smaller, meaningful units called tokens. These can be words, subwords, characters, or symbols, depending on the method.

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Examples

• Sentence: “AI is transforming society.”
  • Word-level → ["AI", "is", "transforming", "society", "."]
  • Subword-level (e.g., BPE, WordPiece) → ["AI", "is", "trans", "form", "ing", "society", "."]

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Why it matters

• It converts unstructured text into a structured format that algorithms can process.
• It affects model efficiency, vocabulary size, and the ability to generalize to unseen words.

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Use cases

• Machine translation (mapping tokens between languages).
• Search engines (indexing and retrieving documents).
• Text generation (predicting the next token in a sequence).

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Tokenization is not a one-size-fits-all process. Different languages and scripts pose unique challenges: for instance, Chinese and Japanese do not use spaces to separate words, requiring specialized segmentation algorithms. In highly inflected languages such as Turkish or Finnish, tokenization must deal with long, morphologically complex word forms that may need to be split into meaningful subunits.

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Subword tokenization methods such as Byte Pair Encoding (BPE), WordPiece, and SentencePiece have become dominant in modern NLP because they strike a balance between efficiency and expressiveness. They reduce the out-of-vocabulary (OOV) problem by breaking rare words into smaller units while keeping frequent words intact. This ensures models can handle unseen terms (like new names or technical jargon) without excessively enlarging the vocabulary.

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In deep learning, tokenization is tightly coupled with the embedding layer, where tokens are mapped to numerical vectors. Poor tokenization can fragment semantic information, leading to less effective embeddings. Conversely, well-designed tokenization improves both training efficiency and model generalization, especially in multilingual or cross-domain settings.

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Another growing area of interest is character- and byte-level tokenization, which avoids predefined vocabularies entirely. While computationally more demanding, these approaches increase robustness to noisy input (e.g., typos, code-mixed text) and facilitate multilingual modeling without language-specific preprocessing.

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Ultimately, tokenization is more than preprocessing: it defines how language is represented in computational systems. The design choices directly influence model size, training cost, interpretability, and downstream performance, making it a cornerstone of NLP pipeline design.

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References

• Manning, C., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing.