Tokenization refers to the process of breaking down a piece of text into smaller units, known as tokens, to analyze and process them individually.
In AI, tokenization is a technique used to divide a text into smaller units, such as words or subwords, by separating them based on specific criteria, such as spaces or punctuation marks. By tokenizing text, each unit becomes a distinct entity that can be analyzed, processed, and understood by AI models. Tokenization is a fundamental step in natural language processing tasks, such as language translation, sentiment analysis, or text classification. It helps in extracting meaningful information from text and enables AI systems to understand the structure and context of the language. Tokens can be further processed, such as converting them to numerical representations, to be used as input for machine learning algorithms.
In summary, tokenization in the context of AI involves breaking down a piece of text into smaller units, known as tokens, to enable analysis, processing, and understanding by AI models, playing a crucial role in various natural language processing tasks.