An unsupervised policy relevance scoring method: Taking Chinese social security policies as the application case. Issue 2 (21st September 2022)

• Record Type: Journal Article
• Authors: Sun, Jingyun; Huang, Shaobin; Li, Rongsheng
• Other Names: Yu Hui guestEditor.
• Is Part Of: Expert systems. Volume 40:Issue 2(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-21
• Subjects: BERT -- digital governments -- natural language processing -- policy documents; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.13149
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Organizing and managing policy documents (PDs) issued to the public in a good way can improve the efficiency of government employees and make it easier for the public to find the needed policy information. However, existing PDs are organized only by dates and manually defined categories; besides, PDs issued by different government branches are isolated from each other. These problems make it challenging and time‐consuming for the public to find the needed policy information. We argue that implicit links should be established between PDs based on their relevance, thus helping the public find the needed policy information efficiently. To this end, we propose an unsupervised relevance scoring method for PDs consist six modules, taking Chinese social security policies as the application case. The method combines the TextRank algorithm, TF‐IDF representation, mutual information and left–right information entropy algorithm, and BERT. The method can decrease the interference of noisy words in PDs to relevance scoring. In addition, the method can consider multiple features of PDs simultaneously so that the measure of relevance can be more comprehensive. The method is not driven by domain‐specific labelled data, hence can be easily generalized to PDs in various domains. We construct a dataset containing 5000 Chinese social security policies and then conduct experiments on it to evaluate our method. Experimental results show that our method is feasible and can bring convenience to government agencies and the public to a certain extent. Furthermore, our method achieves more than a 3% improvement in evaluation results on test tasks than the methods with a similar purpose in the legal AI community.