Tokenizing

Why is tokenizing important in natural language processing?

In natural language processing (NLP), tokenizing is an important step in pre-processing textual data because it allows the computer to understand and analyze the meaning of text by treating each word as a separate entity.

How is tokenizing done?

There are several ways to tokenize a text, but the most common method is to split the text by whitespace or punctuation.

For example, the sentence “The quick brown fox jumps over the lazy dog” can be tokenized into individual words as follows:

[“The”, “quick”, “brown”, “fox”, “jumps”, “over”, “the”, “lazy”, “dog”]

Applications of tokenizing in NLP

Tokenizing is a fundamental step in many NLP tasks such as text classification, sentiment analysis, and machine translation, among others.