What does "Token Classification" mean?
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Token classification is a method used in natural language processing (NLP) to identify and label individual parts of text. This method treats words or pieces of words as "tokens." The goal is to assign specific labels to these tokens based on their roles in a sentence or text.
How It Works
In token classification, a computer model is trained on example texts where tokens are already labeled. During training, the model learns patterns in the data to predict labels for new, unseen text. This process helps the model to understand the context and meaning of words.
Applications
Token classification can be useful in many areas, such as adding marks to Arabic text to enhance readability or grading short answer responses automatically. By accurately identifying and labeling tokens, models can provide better support in tasks like these.