As the first key link in medical information extraction,the medical named entity recognition task aims to extract medical-related entities from unstructured texts such as electronic medical records and Chinese medical instructions. Most current Chinese medical named entity recognition works obtain text representation vectors by fine-tuning pre-trained models,and then use feature engineering to improve the performance of the models in the medical field. Most of these mod-els are derived from models that perform well on general-purpose datasets,without considering the language characteristics of Chinese medical datasets. Through statistical analysis on multiple medical data sets,it is found that some types of medi-cal entities have similarities in glyphs. For example,in Chinese characters,most of the characters representing diseases con-tain "疒",and most of the characters representing body organs contain "月". In response to these problems,this paper pro-poses a Chinese medical named entity recognition method based on glyph features. This method improves the accuracy and generalization ability of the model by fusing the glyph vector on the text representation vector and further utilizing the nega-tive samples in the dataset. Experimental results on multiple public Chinese medical datasets show that this method achieves better results than other models,and ablation experiments prove that fusing glyph features and learning from negative sam-ples is effective for this task.
维普
万方数据