SRCB reports approaches for named entity recognition and adverse drug event detection in a few-resource context, highlighting implications for medical NLP applications.
The SRCB participated in subtask1: Few-resource Named Entity Recognition (NER) and subtask3: Adverse Drug Event detection (ADE) in NTCIR-16 Real-MedNLP. This paper reports our approach to solve the problem and discusses the official results. For the Few-resource NER subtask, we developed NER systems based on pretraining model, span-based classification and prompt learning. In addition, data augmentation and model ensemble are used to further improve performance. For ADE subtask, we mainly adopted two methods: multi-class classification and prompt learning. We employed a two-stage training strategy to solve the long tail distribution problem and applied transfer learning to improve performance of model.
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ZHANG et al. (2022) studied this question.
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