Types of Tokenization
Tokenization can be classified into several types based on how the text is segmented. Here are some types of tokenization:
Word Tokenization:
Word tokenization divides the text into individual words. Many NLP tasks use this approach, in which words are treated as the basic units of meaning.
Example:
Input: "Tokenization is an important NLP task."
Output: ["Tokenization", "is", "an", "important", "NLP", "task", "."]
Sentence Tokenization:
The text is segmented into sentences during sentence tokenization. This is useful for tasks requiring individual sentence analysis or processing.
Example:
Input: "Tokenization is an important NLP task. It helps break down text into smaller units."Output: ["Tokenization is an important NLP task.", "It helps break down text into smaller units."]
Subword Tokenization:
Subword tokenization entails breaking down words into smaller units, which can be especially useful when dealing with morphologically rich languages or rare words.
Example:
Input: "tokenization"Output: ["token", "ization"]
Character Tokenization:
This process divides the text into individual characters. This can be useful for modelling character-level language.
Example:
Input: "Tokenization"
Output: ["T", "o", "k", "e", "n", "i", "z", "a", "t", "i", "o", "n"]
NLP | How tokenizing text, sentence, words works
Tokenization in natural language processing (NLP) is a technique that involves dividing a sentence or phrase into smaller units known as tokens. These tokens can encompass words, dates, punctuation marks, or even fragments of words. The article aims to cover the fundamentals of tokenization, it’s types and use case.