The IMNTPU team engaged in the NTCIR-17 RealMedNLP task, specifically focusing on Subtask1: Adverse Drug Event detection (ADE) and the challenge of identifying related radiology reports. This task is centered on harnessing methodologies that offer significant aid in real-world medical services, especially when training resources are limited. In our approach, we harnessed the power of pre-trained language models (PLMs), particularly leveraging models like the BERT transformer, to understand both sentence and document structures. Our experimentation with diverse network designs based on PLMs paved the way for an enlightening comparative analysis. Notably, BioBERT-Base emerged as a superior contender, showcasing commendable accuracy relative to its peers. Furthermore, our investigation made strides in the realm of one-shot learning for multiclass labeling, specifically with the GPT framework. The insights gathered emphasized the necessity for more specialized strategies, suggesting avenues for future research in multiclass labeling tasks.
Liu et al. (Tue,) studied this question.