Research on Named Entity Recognition of Shen Nong's Materia Medica Based on BiLSTM-CRF
Objective:To mine and demonstrate the drug theories embedded in Shen Nong's Materia Medica based on BiLSTM-CRF named entity recognition technology.Methods:Build a custom vocabulary of traditional Chinese medicine terminology,annotated by computer automated sequences,according to different mainstream named entity recognition methods and the text characteristics of ancient Chinese medical texts,a BiLSTM-CRF model is constructed for named entity recognition of Shen Nong's Materia Medica with word vectors as the initial input.Results:The test results show that the precision of BiLSTM-CRF model is 89.00％,the recall rate is 88.83％,and the F1 value is 88.91％,which is better than other models.Conclusion:BiLSTM-CRF model can effectively identify the entity types of Shen Nong's Materia Medica,which is suitable for knowledge mining of ancient Chinese medical texts and helps to practice the theory of Chinese medicine and bring into play the value of clinical application.
Named entity recognitionShennong's materia medicaTraditional Chinese medicineBiLSTM-CRF
国家科技期刊平台
NSTL
万方数据
