Text Annotation is the process of labeling and categorizing text to help AI understand language, context, intent, and meaning. It transforms raw text into structured data for NLP, machine learning, and Generative AI models.

Text can be annotated for entities, sentiment, intent, keywords, relationships, topics, and semantic meaning. The annotation type depends on the AI model, industry, and specific project requirements.

Annotated text provides models with structured examples that help them identify patterns and understand human language. High-quality annotation can improve the accuracy, consistency, and performance of AI and NLP systems.

Quality is maintained through defined annotation guidelines, trained annotators, consistency checks, and multiple levels of review. This helps reduce errors and ensures the final dataset is accurate and reliable for AI training.