Tokens are individual units or elements that make up a piece of text, such as a sentence or document. These units are typically words, but they can also be subwords, characters, or other linguistic components. Tokenization is the process of breaking down text into these discrete tokens, which serves as a crucial preprocessing step for various natural language processing (NLP) tasks.
Tokenization is fundamental because it enables machines to process and understand human language, which is otherwise continuous and complex. By segmenting text into tokens, machines can analyze the structure, grammar, and meaning of language. For example, in the sentence “I love dogs,” the tokens are “I,” “love,” and “dogs.” Tokenization also plays a key role in building vocabulary lists and numerical representations that AI models can work with. Tokens are the building blocks that facilitate many NLP tasks, including sentiment analysis, text classification, machine translation, and more.
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