In natural language processing (NLP), tokenization is the process of breaking down a piece of text into smaller units called tokens. These tokens can be individual words, phrases, or symbols, and they are the building blocks of natural language processing tasks.
Tokenization is an important step in NLP because it allows algorithms to work with smaller, more manageable units of text, rather than trying to process the entire text at once. It also helps to normalize the text by separating it into smaller units, which can make it easier to analyze and interpret.
There are different approaches to tokenization, depending on the specific needs of the NLP task at hand. Some common techniques include word tokenization, which involves breaking down the text into individual words; phrase tokenization, which involves breaking down the text into phrases or groups of words; and symbol tokenization, which involves breaking down the text into individual symbols or characters.
Overall, tokenization is a fundamental step in natural language processing, and it is an important tool for breaking down and analyzing text data. It can be used as a preprocessing step for a wide range of NLP tasks, including text classification, sentiment analysis, and many others.