Research on Domain Information Mining and Theme Evolution of Scientific Papers
It aims to help researchers navigate and utilize large-scale scientific literature, but it appears incremental as it reviews existing approaches rather than introducing new solutions.
This paper addresses the challenge of analyzing the growing volume of scientific papers, particularly cross-disciplinary research, by reviewing methods for semantic feature representation, domain information mining, and topic evolution prediction to aid researchers.
In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Cross-disciplinary research results have gradually become an emerging frontier research direction. There is a certain dependence between a large number of research results. It is difficult to effectively analyze today's scientific research results when looking at a single research field in isolation. How to effectively use the huge number of scientific papers to help researchers becomes a challenge. This paper introduces the research status at home and abroad in terms of domain information mining and topic evolution law of scientific and technological papers from three aspects: the semantic feature representation learning of scientific and technological papers, the field information mining of scientific and technological papers, and the mining and prediction of research topic evolution rules of scientific and technological papers.