Method for Automatic Tagging of Social Issues and Extraction of Related Cases on Web Articles for Public Collaboration

Abstract

Recently, social issues that threaten sustainable development are increasing in Japanese local societies. To address these issues, public collaboration between citizens, government, and experts is important. To discuss how to address these issues, surveying related activities are important. We aim to develop systems for collecting local social issues and actual solutions from Web articles to support the survey and discussion.In this paper, we tried two approaches: (1) developing an automatic tagging system for social issues on Web articles and (2) developing an information extraction system for related cases from Web articles to address them. For the approach (1), we propose a method for classifying each sentence in Web articles into social issue tags using a BERT pre-trained on Japanese Wikipedia. For the approach (2), we propose a method for extracting cases related to social problems from the Web articles. First, we design and develop a corpus of social issues using the Web Annotation Data Model. Second, we describe a method of extracting related cases on social issues from Web articles using a corpus as training data.