Hybrid Chinese text classification model based on pretraining model

Abstract In order to solve the problems of fuzzy boundary of Chinese words, polysemy of one word and the inability of traditional models to reflect the importance of each word in the text, a hybrid text classification model based on Ernie CNN and bilstm attention (mecba) is proposed. Firstly, Ernie model is used to generate the corresponding word vector, which can retain rich semantic information and enhance the semantic representation of words. Then, the word vector is input into CNN to extract local information, and the context feature is extracted by bilstm attention, and the output of the two is spliced; Finally, softmax classifier is used for classification. The experimental results show that the model is better than CNN, bilstm and other classification models, and can effectively improve the performance of Chinese text classification.