what is tokenization in nlp

Tokenization is the process of breaking down raw text into smaller chunks called tokens, which can be words, sentences, or other meaningful elements. Tokenization is the first step in any natural language processing (NLP) pipeline and is essential for organizing and understanding human language. Tokenization is used to split paragraphs and sentences into smaller units that can be more easily assigned meaning. Tokenization is a simple process that takes raw data and converts it into a useful data string. There are different ways to tokenize text, and each methods success relies heavily on the strength of the programming integrated into other parts of the NLP process. Some of the challenges of tokenization include handling contractions, apostrophes, and hyphens, which can vary depending on the language of the document. Tokenization can be done using various open-source tools such as NLTK, Gensim, and Keras. Proper tokenization is crucial for the success of an NLP project, as failing to tokenize every part of the sentence can lead to misunderstandings later in the NLP process.