A System for Classifying Proposals and Estimating Start Pages Stated in Notice of Annual Meeting of Shareholders

Abstract

In this paper, we describe research on applied systems for realizing efficiency of work to store information of notice of annual meeting of shareholders in the database by using text mining technology. We aim to estimate start pages of proposals stated in notice of the meeting of shareholders and classify which proposal the page is. And we developed a system that automatically performs these tasks using text information of the notice of convocation of shareholders, and actually operates it. As a result of comparative experiment between our implemented system and conventional manual work, the working time was shortened to about 1/10. We propose three methods for classifying proposals. The first method classifies proposals by specialized terms extracted from training data. The second method classifies proposals by using deep learning. The final method classifies proposals by extracted proposal title. We evaluated our methods, and the effectiveness of each method was verified.