What Tokenization Meaning, Applications & Example
The process of breaking down text into smaller, meaningful units.
What is Tokenization?
Tokenization is the process of breaking down text into smaller units, called tokens, which can be words, subwords, or characters. It is an essential step in natural language processing (NLP) and is used to convert raw text into a structured format that can be processed by machine learning models.
Types of Tokenization
- Word Tokenization: Splits the text into individual words. For example, the sentence “I love AI” would be tokenized as [“I”, “love”, “AI”].
- Subword Tokenization: Breaks words into smaller meaningful units, often used in languages with complex morphology or to handle rare words. For example, “unhappiness” could be tokenized as [“un”, “happiness”].
- Character Tokenization: Splits text into individual characters. For example, “AI” would be tokenized as [“A”, “I”].
Applications of Tokenization
- Text Preprocessing: Tokenization is the first step in preparing text for various NLP tasks like sentiment analysis , text classification , or named entity recognition.
- Machine Translation: Tokenization is essential for breaking down sentences into units that can be translated between languages.
- Language Modeling: Tokenization helps convert text into manageable pieces, enabling models to predict the next word or sequence of words.
Example of Tokenization
In machine translation, tokenization is used to split input sentences into tokens before they are passed to a model . For instance, the English sentence “I love programming” might be tokenized into the words [“I”, “love”, “programming”]. The tokens are then translated into the target language, and the translation model generates the translated sentence by predicting the appropriate tokens for the target language.