Affiliations: [a]
Zhejiang Economic Information Center, Hangzhou, China
| [b]
Institute of Computing Innovation, Zhejiang University, Hangzhou, China
| [c]
School of Public Administration of Zhejiang University of Technology, Hangzhou, China
Correspondence:
[*]
Corresponding author. Lie’en Weng, School of Public Administration of Zhejiang University of Technology, Hangzhou 310023, China. Email: [email protected].
Abstract: The text information processing technology of public health service is one of the hot research topics at present. To improve the defects of public health service texts, such as inaccurate word segmentation, spelling errors and professional vocabulary understanding, this study designed a character-level deep neural network model on the characteristics of public health service texts. In this model, the bidirectional short and short time memory and the attention pooling operation layer are introduced to make the model better classify the text according to the context. In addition, counter perturbation is introduced in this study to improve the robustness and generalization ability of the model, thus improving its classification effect. The performance verification results show that the proposed model has better classification performance on the public health service text data set. The anti-disturbance samples generated by the model are all in the range of 0–0.2 when WMD deviation degree is measured, while most of the other methods are in the range of 0.4–0.6. The experimental object of this study is ultrasonic examination data. The experimental results show that the automatic analysis model of public health service text based on character level convolutional neural network constructed in this study has excellent accuracy and convergence speed, and has excellent performance in the classification of public health service text in different subject areas.
Keywords: Public health service text, character level convolutional neural network, automatic analysis, counter sample, text classification
