TOKENIZATION TECHNIQUES IN NLP:A COMPREHENSIVE REVIEW

Computer Science

Tokenization is a natural language processing (NLP) preprocessing technique that involves breakingdown a text into individual tokens, often words or subwords. It has been the subject of extensive research in recent years, leading to the creation of numerous innovative methods and tools. The state of the art in NLP tokenization approaches is thoroughly reviewed in this study. We start by outlining the various tokenization techniques, including word, subword, and character-level tokenization. The benefits and drawbacks of various tokenization strategies, including rule-based, statistical, and neural network-based techniques, are then covered. The performance of various tokenization techniques and libraries is then compared.We also look at current developments in tokenization research,such as the use of unsupervised techniques and contextual data. Finally, we list many challenges in tokenization. We wrap up by examining prospective possibilities for tokenization research in the future and their effects on the larger discipline of NLP.

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