A Multi-span-Based Conditional Information Extraction Model

Conditional information extraction plays an important role in medical information extraction applications, such as medical information retrieval, medical knowledge graph construction, intelligent diagnosis and medical question-answering. Based on the evaluation task of China Conference on Health Information Processing 2022 (CHIP 2022), we propose a Multi-span-based Conditional Information Extraction model (MSCIE), which can well solve the conditional information extraction by extracting multiple span and the relations between each span. Moreover, the model provide a solution to conditional information extraction in complex scenes such as discontinuous entities, entity overlap, and entity nesting. Finally, our model, with the fusion of two pretrained models, has obtained the performance of the 1st in list A and the 2nd in list B, which also proves the effectiveness of the model.