In NLP, text annotation is used to markup textual data with linguistic information, such as part-of-speech tags, named entities, syntactic dependencies, and semantic roles. Annotators analyze the text and assign relevant labels or tags to identify and extract linguistic features. This annotated data is crucial for training machine learning models for tasks like named entity recognition, sentiment analysis, and machine translation.

Intent annotation involves labeling text data with user intents or purposes expressed in the text. Annotators identify the underlying intentions or goals conveyed by the text, such as booking a flight, ordering food, or seeking information. Intent annotation is commonly used in applications like chatbots, virtual assistants, and customer service systems to understand user queries and provide appropriate responses.

Text classification entails annotating text documents with predefined categories or labels based on their content or topic. Annotators assign appropriate labels to text data to classify them into different classes or categories. This annotated data is used to train classification models for tasks like sentiment analysis, topic modeling, and spam detection. Text classification enables automated organization and analysis of large text corpora, facilitating information retrieval and decision-making.