Full Annotation

Full annotation, the process of comprehensively labeling data for machine learning, is crucial for training accurate and reliable models but faces challenges in scalability and cost. Current research focuses on developing efficient annotation strategies, including techniques like partial annotation and leveraging existing knowledge bases (e.g., codebooks) to reduce annotation burden, as well as exploring novel annotation schemas to capture complex relationships within data. These advancements are improving model performance across diverse fields, from biomedical literature analysis and political event classification to audio processing and medical image segmentation, ultimately enhancing the reliability and applicability of AI systems.