Token classification refers to the process of assigning a label or category to each token or element in a text stream. Tokens can be individual words, numbers, symbols and other elements in a text. Token classification is commonly used in natural language processing and machine learning for tasks such as sentiment analysis, information extraction and document classification.
Token classification involves labelling each token with a specific category based on its meaning or function in the text. For example, in a sentence, verbs can be labelled as "VERB", nouns as "NOUN", adjectives as "ADJECTIVE", and so on.
To perform token classification, machine learning algorithms, such as neural network-based classification models, are used, which can learn to assign categories to tokens based on text features and labels already existing in a training dataset.
Token classification is a fundamental technique in natural language processing and is essential for many applications, such as text generation, machine translation, natural language understanding and sentiment analysis in social networks.
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