Chinese medical named entity recognition model based on local enhancement - CHEN Jing; XING Kexuan; MENG Weilun; GUO Jingfeng; FENG Jianzhou (Tongxin xuebao)
In the medical field, the recognition of medical entities is often influenced by their adjacent context, the current named entity recognition methods typically rely on BiLSTM to capture the global dependency relationships within text, lacking modeling of local dependencies between characters. To resolve this problem, a Chinese medical named entity recognition model LENER based on local enhancement was proposed. Firstly, the representation of characters was enriched by LENER utilizing multi-source information, including phonetic, graphic and semantic features. Secondly, relative position encoding was combined to perform local attention calculations on sequence segments divided by sliding windows, and local information was fused with global information obtained from BiLSTM through nonlinear computation. Finally, the recognized entity heads and tails were combined by LENER to extract the entities. The experimental results show that the LENER model has excellent entity recognition capabilities, and the F1 value is improved by 0.5% to 2% compared with other models.