What is Tokenization in NLP?

Natural Language Processing (NLP) is a subfield of computer science, artificial intelligence, information engineering, and human-computer interaction. This field focuses on how to program computers to process and analyze large amounts of natural language data. It is difficult to perform as the process of reading and understanding languages is far more complex than it seems at first glance. Tokenization is a foundation step in NLP pipeline that shapes the entire workflow.

Tokenization is the process of dividing a text into smaller units known as tokens. Tokens are typically words or sub-words in the context of natural language processing. Tokenization is a critical step in many NLP tasks, including text processing, language modelling, and machine translation. The process involves splitting a string, or text into a list of tokens. One can think of tokens as parts like a word is a token in a sentence, and a sentence is a token in a paragraph.

Tokenization involves using a tokenizer to segment unstructured data and natural language text into distinct chunks of information, treating them as different elements. The tokens within a document can be used as vector, transforming an unstructured text document into a numerical data structure suitable for machine learning. This rapid conversion enables the immediate utilization of these tokenized elements by a computer to initiate practical actions and responses. Alternatively, they may serve as features within a machine learning pipeline, prompting more sophisticated decision-making processes or behaviors.

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.