Recognition of Named Entities in Acupuncture Literature Based on Dictionary and Deep Learning Model
Objective Based on the acupuncture literature data set,a named entity recognition method of acupuncture literature based on dictionary and deep learning model is proposed to improve the effect of acupuncture literature entity recognition.Methods In this paper,the entity recognition methods of acupuncture literature were explored,and the vector representation effects based on word2vec and ALBERTmodels and ALBERT+domain dictionary were compared.On this basis,a named entity extraction method combining domain dictionary and ALBERT-BiLSTM-CRF deep learning model was proposed.Results According to the extraction effect of three model entities,the P value of word2vec-BiLSTM-CRF is 81.82%,the R value is 70.76%,and the F1 value is 75.48%;ALBERT-BiLSTM-CRF has an P value of 83.10%,a R value of 81.14%and a F1 value of 81.98%."ALBERT-BiLSTM-CRF+dictionary"is 92.57%,91.42%and 91.85%.In terms of entity categories,the top three entities with the highest accuracy rate are acupuncture,needling and needling site,which are 98%,ninety-seven percent and ninety-seven percent respectively,while the three entities with the lowest accuracy rate are acupoint matching corresponding symptoms,disease names and sample size,which are 50.00%,50.68%and 52.43%respectively.Conclusion Compared with the original ALBERT-BiLSTM-CRF model,the precision rate,recall rate and F1 value increased after adding the dictionary,and the convergence speed of the model after adding the dictionary was twice that without adding the dictionary.It is effective to use"ALBERT-BiLSTM-CRF+dictionary"model to identify named entities in acupuncture literature.
NETL
NSTL
万方数据
