融合雙通道特征信息的醫(yī)療短文本分類模型

打開文本圖片集

Medical short text classification model with fusion of dual channel feature information

LI Chen2，LIU Na1，2 ，ZHENGGuofeng1，2，YANGJie1，2，DAOLu1，2 （1.CollegeofComputerScienceandEngineering，NorthMinzuUniversity，Yinchuan75o021，China; 2.TheKey

Abstract：Inviewofthesparsefeatures，semanticambiguitiesandinsuficientextractionofshorttextfeaturesinthe medicalshorttexts，amedicalshorttextclassificationmodelEBDF（ERNIE-BiLSTM-DPECNN-FGM）fusingdual-chanel featuresisproposed.Thepre-trainedmodelisusedtoobtaindynamicwordvectors，whichmadethemodelcontainricher semantic information.Thenthe BiLSTMisusedtoobtainglobaltextfeature informationandthe DPECNNisusedtoobtain deep localtextfeatureinformation.TheFGMadversarialtrainingalgorithmisusedtodisturbancethedatatoimprovetherobustne andgeneralizationabilityofthemodel.Finally，thefeatureinformationofthetwochannelsisfusedtoobtainthefinaltext representation.TheEBDFmodelwascompared with the model withthe better efectontheshorttextdatasetsof three medical fieldsand two general fields.It can be seen that itsaccuracyis improved by about 0.57%＼～6.16%，and its F1 value is improved by about 0.65% 3 5.80%

Keywords：medical text mining;short text clasification;feature fusion;BiLSTM; DPECNN;two-channel

0 引言

醫(yī)療行業(yè)是一個數(shù)據(jù)密集型和知識密集型的行業(yè)。（剩余16371字）