Tokenization
Tokenization is the process of breaking down text or data into smaller units called tokens, which are typically words, subwords, or characters, for analysis or processing in natural language processing (NLP) and computational linguistics. It serves as a foundational step in text preprocessing, enabling machines to interpret and manipulate human language by converting raw text into structured, manageable pieces. This technique is crucial for tasks like machine translation, sentiment analysis, and information retrieval.
Developers should learn tokenization when working on NLP projects, such as building chatbots, search engines, or text classification systems, as it transforms unstructured text into a format that algorithms can process efficiently. It is essential for handling diverse languages, dealing with punctuation and special characters, and improving model accuracy by standardizing input data. Use cases include preprocessing data for machine learning models, tokenizing code in compilers, and securing sensitive information in payment systems.