关键词: 政策工具, 深度学习, 自动识别, 卷积神经网络

Abstract: [Purpose/significance] The identification and analysis of policy tools is one of the important methods of policy research. However, the identification of policy tools is mostly manual. In this article, we attempt to use deep learning methods to automatically identify policy tools, aiming at improving the efficiency of policy tool identification. [Method/process] We designed and implemented the policy tool automatic identification experimental process of "Policy data collection and cleaning-policy tool manual indexing-model training-result interpretation". We take the open government data policies of Beijing, Shanghai, Guangzhou, and Guiyang as an example to compare the performance of traditional machine learning methods and deep learning methods on the task of identifying policy tools. In addition, we have proposed to integrate policy global information to identify policy tools in each paragraph, and our experiments have proved the effectiveness of the idea. [Result/conclusion] The deep learning model CNN achieves an accuracy of 76.51% on the full test data, and the CNN model that integrates global information achieves an accuracy of 77.13%. When evaluating the high-confident results of the model, we find that the model achieves an accuracy of 95.44% on 55.63% of the test data, which has reached the practical requirements. This shows that more than half of the data can be indexed with the model’s high-confidence results without manual review. Deep learning methods have been applied to the automatic identification of policy tools and has achieved good results. It could help to improve the efficiency of policy tool labeling and provide positive experience for the automatic identification of policy tools with big data. And it provides a positive experience for automatic identification of policy tools with large data volumes.

Key words: policy tools, deep learning, automatic identification, CNN