Tokenization

Tokenization is a fundamental process in natural language processing (NLP) that involves breaking down text into smaller units called tokens. These tokens can be words, phrases, or even characters, depending on the granularity required for analysis. The main characteristics of tokenization include its ability to simplify text processing, facilitate the extraction of meaningful information, and enable various NLP tasks such as sentiment analysis, text classification, and machine translation. Common use cases for tokenization include preparing text data for machine learning models, enabling search functionalities, and improving the accuracy of language models by providing structured input. Overall, tokenization serves as a crucial step in transforming raw text into a format suitable for computational analysis.