基于EALMDA的医疗命名实体识别数据增强方法OA

Named entity recognition in the medical field involved identifying named entities from unstruc-tured medical texts.This played a crucial role in various downstream tasks.Due to the complexity of medical named entities leveraging domain-specific knowledge was required in expert annotations,which led to a severe scarcity of annotated data in the medical field.To address this issue,an entity-aware mask local mixup data augmentation method(EALMDA)was proposed.This method firstly extracted key ele-ments using an entity-aware masking channel and masked non-entity parts to retain core semantics.Then,masked sentences were fused through a linear combination of two sampling strategies:contextual entity similarity and k-nearest neighbors,which preserved the core semantics while increasing sample di-versity.Finally,after sequence linearization,the sentences were input into a generative model to obtain augmented samples.Comparative experiments were conducted on five mainstream medical named entity recognition datasets,such as NCBI-disease,simulating low-resource scenarios against mainstream data augmentation baselines,and significant improvements were observed compared to baseline methods.