Few-shot biomedical relation extraction using data augmentation and domain information

Abstract

Relation extraction (RE) plays a pivotal role in biomedical information extraction. However, traditional approaches are often limited by high data annotation costs and extensive time investments. To address this challenge, this study proposes an innovative few-shot learning approach for biomedical RE tasks that utilizes data augmentation and domain information. Specifically, this method enhances the diversity of training data through a synthesized data augmentation strategy. At the same time, it combines domain information with prompt learning techniques. By incorporating domain information, the model’s generalization capability for rare data instances is improved. Furthermore, the prompt learning approach, by integrating prompt templates into the model input, guides the pre-trained language model to more accurately adapt to the RE task. Our model demonstrated exceptional performance in three different biomedical RE tasks, particularly on the I2B2-2010 RE dataset. In the 1-shot, 10-shot, and 50-shot settings, the F1 scores increased by 14.38%, 9.50%, and 6.05%, respectively.