NLP Tokenization

- Overview

Natural language processing (NLP) enables machine learning algorithms to organize and understand human language. NLP enables machines to not only collect text and speech, but also recognize the core meaning to which they should respond. Human language is complex and constantly evolving, which means that natural language processing presents considerable challenges. Tokenization is one of the many challenges of how NLP works.

In NLP, "tokenization" is the process of breaking down a piece of text into smaller units called "tokens", which are typically words, but can also be characters or subwords, allowing machines to easily analyze and understand the text by dividing it into manageable parts; essentially, it's the first step in processing raw text data for further NLP tasks like sentiment analysis or text classification. 

- Purpose

The purpose of NLP tokenization is to simplify complex human language by splitting it into smaller, meaningful units that can be processed by computers.

Tokenization is the beginning of the NLP process, converting sentences into understandable bits of data that the program can process. Without a solid foundation established through tokenization, the NLP process can quickly devolve into a messy game of telephone.

- Example

A token can be a word, a punctuation mark, a number, or even a character depending on the chosen tokenization method. Tokenization is a fundamental step in most NLP tasks as it enables further analysis like part-of-speech tagging, named entity recognition, and sentiment analysis.

For example,

• Sentence: "The quick brown fox jumps over the lazy dog."

• Tokenized: ["The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"] 

[More to come ...]