Tokenization Explained with Examples
Tokenization Explained with Examples
Tokenization is the process of breaking down text into smaller units called tokens.
These tokens can be words, characters, or subwords, depending on the approach. Tokenization is one of the first steps in Natural Language Processing (NLP) because computers need structured input to understand human language.
Why is Tokenization Important?
Computers don’t understand sentences the way humans do.
Tokenization helps convert text into manageable pieces for tasks like translation, sentiment analysis, chatbots, and search engines.
Types of Tokenization
1. Word Tokenization
Splitting a sentence into words.
π Example:
Sentence: "I love quantum computing."
Tokens: ["I", "love", "quantum", "computing"]
This is useful for tasks where meaning is based on words.
2. Character Tokenization
Splitting text into individual characters.
π Example:
Sentence: "AI"
Tokens: ["A", "I"]
This is often used in languages like Chinese, or in tasks like spelling correction.
3. Subword Tokenization
Breaking text into smaller meaningful parts (subwords).
This helps handle rare or unknown words by splitting them into pieces.
π Example (using Byte-Pair Encoding or WordPiece):
Word: "unhappiness"
Tokens: ["un", "happi", "ness"]
This way, even if the model has never seen “unhappiness”, it can still understand it from known subwords.
4. Sentence Tokenization
Splitting a paragraph into individual sentences.
π Example:
Paragraph: "Quantum computing is fascinating. It has great potential."
Tokens:
["Quantum computing is fascinating.", "It has great potential."]
Real-Life Example of Tokenization in Action
In Google Translate, text is tokenized into smaller chunks so the system can translate word by word or phrase by phrase.
In chatbots, tokenization helps the model break down user queries into understandable parts.
✅ In short:
Tokenization = splitting text into smaller parts.
Types = word, character, subword, and sentence tokenization.
It’s a crucial first step in making human language understandable for machines.
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