Research on Domain Information Mining and Theme Evolution of Scientific Papers

TL;DR

This paper reviews methods for extracting domain information and understanding how research topics evolve in scientific papers, emphasizing semantic features, field information, and evolution prediction to aid researchers.

Contribution

It provides a comprehensive overview of current techniques in domain information mining and topic evolution analysis for scientific literature.

Findings

Summarizes semantic feature representation learning methods.

Analyzes field information mining approaches.

Discusses research topic evolution prediction techniques.

Abstract

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.