A Granular Computing-Based Deep Neural Network Approach for Automatic Evaluation of Writing Quality

As a subjective behaviour relying on expert experience, automatic evaluation of writing quality always remains a technical issue. It requires both effective semantic understanding and structure analysis towards writing contents. To deal with this challenge, this paper combines speed superiority of granular computing and the effective approximation ability of deep neural network towards nonlinear mapping relationships. On this basis, a granular computing-based deep neural network approach for automatic evaluation of writing quality, is developed in this paper. Specifically, the granular computing is used as the front-end processor of deep neural network, so as to reduce the following information density. Then, the deep neural network serves as the main backbone structure to extract semantic features of writing contents. Such combination of two modules can improve processing speed in large-scale textual analysis scenes, under insurance of evaluation performance. The simulation experiments are also conducted to test performance of the proposed technical framework, and the results show that both high accuracy and proper running speed are endowed with the proposal.