Annotated Chapter Information
Annotated chapter information research focuses on creating and utilizing high-quality datasets with detailed annotations for various tasks, ranging from Named Entity Recognition in novels to medical image segmentation and sentiment analysis in news articles. Current research emphasizes developing efficient annotation methods, including leveraging AI for automated annotation and active learning strategies to minimize annotation costs, and exploring the use of diverse model architectures like transformers and U-Nets for processing and analyzing annotated data. This work is crucial for advancing numerous fields, including healthcare, natural language processing, and computer vision, by providing the labeled data necessary to train and evaluate robust machine learning models.
Papers
Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Cost
Oana Ignat, Longju Bai, Joan Nwatu, Rada Mihalcea
Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations
Chenyu You, Yifei Min, Weicheng Dai, Jasjeet S. Sekhon, Lawrence Staib, James S. Duncan