This study proposes a Chinese disease text classification model that integrates knowledge graph.Firstly,by introducing structured knowledge from external medical knowledge graph,a knowledge enhanced disease text vector representation is obtained;Secondly,the global semantic features and local semantic features of the disease text are extracted by using bidirectional long short-term memory network and convolutional neural network respectively.At the same time,the joint attention mechanism improves the efficiency of the model in extracting effective features information;Finally,the extracted features are concatenated and fused,and a classifier is used to output the classification result.The experimental results on the Chinese disease text dataset show that the pro-posed model has a classification accuracy,recall,and the harmonic mean value F1 of 95.21%,95.64%,and 95.42%,respectively,which shows better classification performance compared to other models.
disease text classificationknowledge graphCNNBiLSTMattention mechanism
NETL
NSTL
万方数据
