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18 January 2025

Tokenization

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Understanding Tokenization in Natural Language Processing

Tokenization is a fundamental step in Natural Language Processing (NLP) that involves breaking down text into smaller units called tokens. These tokens can be words, subwords, or individual characters. This process enables machines to interpret and process human language effectively. In the context of transformer architectures, tokenization is crucial as it converts textual data into a format suitable for model input.

Why is Tokenization Needed?

Tokenization serves several essential purposes:

  • Simplifies Text Processing: By dividing text into manageable units, tokenization facilitates the analysis and manipulation of language data.

  • Enables Vocabulary Creation: Tokens form the basis of the model's vocabulary, allowing it to understand and generate language.

  • Handles Out-of-Vocabulary Words: Effective tokenization strategies can manage words not seen during training by breaking them into known sub-components.

Types of Tokenization

There are three primary types of tokenization in NLP:

  • Word-Based Tokenization

    This method treats each word as a separate token. For example, the sentence "Let's learn tokenization." would be tokenized into ["Let's", "learn", "tokenization."]. While straightforward, this approach struggles with:

    • Out-of-Vocabulary (OOV) Words: New or rare words not present in the training data can lead to issues, as the model has no prior knowledge of them.

    • Language Nuances: Languages with complex morphology or compound words can pose challenges for word-based tokenization.

  • Character-Based Tokenization

    Here, each character is considered a token. The same sentence would be tokenized into ['L', 'e', 't', "'", 's', ' ', 'l', 'e', 'a', 'r', 'n', ' ', 't', 'o', 'k', 'e', 'n', 'i', 'z', 'a', 't', 'i', 'o', 'n', '.']. This method effectively handles any vocabulary, including misspellings or rare words. However, it results in longer sequence lengths, increasing computational complexity and making it harder for models to capture meaningful patterns.

  • Subword-Based Tokenization

    This approach strikes a balance between word and character tokenization by breaking words into subword units. For instance, "unhappiness" might be tokenized into ["un", "happiness"]. Subword tokenization addresses the limitations of the previous methods by:

    • Reducing Vocabulary Size: By representing words through subword units, the overall vocabulary size is decreased, making the model more efficient.

    • Handling OOV Words: New words can be constructed from known subwords, allowing the model to infer meaning from unfamiliar terms.

Subword Tokenization Algorithms

Several algorithms implement subword tokenization:

  • Byte-Pair Encoding (BPE): This algorithm starts with individual characters and iteratively merges the most frequent pairs to form subwords. Over time, it builds a vocabulary of common subword units. BPE is utilized in models like GPT.

  • WordPiece: Initially developed for Google's BERT model, WordPiece begins with a base vocabulary and adds new subword units based on their frequency and contribution to the likelihood of the training data.

  • SentencePiece: This algorithm treats the input text as a sequence of characters and uses unsupervised learning to build the vocabulary, allowing it to handle languages without clear word boundaries effectively.

  • Whitespace Tokenization: This is the simplest form of tokenization, splitting text into tokens based on whitespace. While easy to implement, it may not handle punctuation or complex language structures effectively. 

Tokenization in Transformer Architecture

In transformer models, tokenization is a critical preprocessing step:

  • Text Input: Raw text data is provided to the model.

  • Tokenization: The text is tokenized into subword units using one of the aforementioned algorithms.

  • Vocabulary Mapping: Each token is mapped to a unique identifier based on the model's vocabulary.

  • Embedding: These token IDs are converted into dense vectors that the model can process.

  • Model Processing: The transformer processes these embeddings to perform tasks like translation, summarization, or text generation.

Effective tokenization ensures that the model can handle diverse and complex language inputs, making it a foundational component of transformer-based NLP systems.

Conclusion

Tokenization is an indispensable part of NLP, enabling models to process and understand human language. By breaking text into manageable units, tokenization facilitates the creation of efficient and robust language models. Understanding the different types of tokenization and their respective algorithms is crucial for developing effective NLP applications.

References:

1. https://neptune.ai/blog/tokenization-in-nlp

2.https://www.analyticsvidhya.com/blog/2020/05/what-is-tokenization-nlp/

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