What is tokenization?

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Tokenization is a fundamental step in Natural Language Processing (NLP) where text is broken down into smaller units called tokens. These tokens can be words, subwords, characters, or even sentences, depending on the application. By splitting text into manageable pieces, tokenization makes it easier for computers to analyze and process human language.

🔹 Why Tokenization is Needed

Computers cannot directly understand raw text. For example,

“Machine learning is powerful.”

Without tokenization, this is just one long string. Tokenization splits it into:

• Word-level tokens: [“Machine”, “learning”, “is”, “powerful”]

• Character-level tokens: [“M”, “a”, “c”, “h”, …, “l”]

• Subword tokens (used in BERT, GPT): [“Machine”, “learn”, “ing”, “is”, “powerful”]

This structured format is easier for models to process.

🔹 Types of Tokenization

1. Word Tokenization: Splitting text into words.

   • Example: “I’m happy” → [“I”, “’m”, “happy”]

2. Sentence Tokenization: Splitting text into sentences.

   • Example: “AI is growing fast. It’s everywhere.” → [“AI is growing fast.”, “It’s everywhere.”]

3. Subword Tokenization: Breaking words into meaningful smaller units to handle unknown words.

   • Example: “unhappiness” → [“un”, “happi”, “ness”]

4. Character Tokenization: Breaking text into individual characters.

🔹 Applications

• Search engines (breaking queries into words)

• Text preprocessing for machine learning models

• Spell checking and sentiment analysis

• Machine translation

• Chatbots and voice assistants

🔹 In short

Tokenization is like chopping a paragraph into building blocks, so that algorithms can understand, analyze, and generate language effectively.

Read More 

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What is RNN and how does it work?

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