Tokenization in natural language processing (NLP)

Tokenization

Tokenization is the process of breaking down a text into its individual words or tokens. Tokens are the basic units of analysis in natural language processing (NLP).

There are many different ways to tokenize text. Some common methods include:

• Whitespace tokenization: This is the simplest method of tokenization. It simply breaks the text down at whitespace characters, such as spaces, tabs, and newlines.
• Regular expression tokenization: This method uses regular expressions to identify tokens. Regular expressions are a powerful tool for identifying patterns in text.
• NLP-specific tokenization: There are many NLP-specific tokenizers available. These tokenizers are designed to take into account the nuances of natural language, such as punctuation and compound words.

The choice of tokenizer will depend on the specific task that is being performed. For example, whitespace tokenization is often used for tasks such as text classification and sentiment analysis. NLP-specific tokenizers are often used for tasks such as named entity recognition and coreference resolution.

Tokenization is an important first step in many NLP tasks. By breaking down text into its individual tokens, it makes it easier to identify the important features of the text and to perform further analysis.