Tokenization: Making Text Speak to Computers

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  • Have you ever wondered how computers understand human language? It might seem like magic, but there's a crucial first step called tokenization that breaks down complex sentences into manageable pieces. In this blog post, we'll break down everything you need to know about tokenization in Natural Language Processing (NLP).

What is Tokenization?

Imagine a delicious pizza. Before you can devour it entirely, you break it down into manageable slices. Tokenization does something similar for text. It's the process of breaking down a stream of text into smaller units called tokens.

These tokens can be words, sentences, characters, or even subwords (think prefixes and suffixes like "un-" or "-ing"). By splitting the text into bite-sized pieces, tokenization helps computers analyze and understand the meaning of language.

Why is Tokenization Important?

Raw text is a jumbled mess for computers. They can't grasp the meaning of a sentence like "The quick brown fox jumps over the lazy dog" unless it's broken down into individual words. Tokenization allows NLP applications to:

  • Understand word order and grammar: By identifying individual words, computers can start to understand the relationships between them and how they contribute to the overall meaning of a sentence.
  • Perform tasks like sentiment analysis: By analyzing the tokens, NLP models can determine if a piece of text expresses positive, negative, or neutral sentiment.
  • Prepare text for machine learning models: Tokenized data can be converted into numerical representations suitable for machine learning algorithms, enabling tasks like text classification and topic modeling.

Types of Tokenization

There are three main types of tokenization used in NLP:

  1. Word Tokenization: This is the most common type, where the text is split into individual words. For example the sentence “I love NLP” would be tokenized into three tokens: “I”, “love”, “NLP”. Punctuation marks are typically treated as separate tokens.
  2. Character Tokenization: Here, the text is broken down into its constituent characters, including letters, numbers, and special symbols. This approach is useful for analyzing languages that don't have clear word boundaries or for tasks like spelling correction.
  3. Subword Tokenization: This method is particularly helpful for dealing with rare words or languages with complex morphology (word structure). It involves splitting words into smaller meaningful units like prefixes, suffixes, or character n-grams (sequences of characters).

The Challenges of Tokenization

Tokenization might seem straightforward, but there are some complexities to consider. For example, handling contractions ("don't"), hyphenated words ("well-being"), and emojis can require specific rules. Additionally, some languages have different word boundaries compared to English, which necessitates adjustments in the tokenization process.

Conclusion

Tokenization is the invisible but vital first step in unlocking the meaning of human language for computers. By breaking down text into manageable tokens, NLP applications can perform a wide range of tasks, from sentiment analysis to machine translation. As NLP continues to evolve, tokenization will remain the foundation for building smarter and more interactive machines that can understand and respond to our natural language.

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  • References
    • Mastering Natural Language Processing. By Cybellium Ltd

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