Similarity Retrieval of JSIP Paper Based on Predefined Categories

Abstract

Japan Society for Intellectual Production (JSIP) has evolved over more than 20 years and has made extensive contributions to the field. While existing work has relied mainly on quantitative statistical methods for context analysis, recent advances in large language models (LLMs) have made more possible methods such as context learning and analysis. In this study, a retrieval system is constructed, which aims to perform similarity retrieval analysis on JSIP papers according to predefined categories. It uses LLMs for text vectorization and improves the retrieval results through similarity filtering, while effectively analyzing the topic trends. The experimental results verify the effectiveness of the proposed approach, in which "Industry-Academia-Government Collaboration Policy" appears with the highest frequency and " collaboration between different fields" with the lowest frequency, which will help to expand the content analysis of industry-academia collaboration activities in the future.