Research on Domain Information Mining and Theme Evolution of Scientific Papers

Changwei Zheng; Zhe Xue; Meiyu Liang; Feifei Kou; Zeli Guan

cs.DLApr 18, 2022

Research on Domain Information Mining and Theme Evolution of Scientific Papers

Authors: Changwei Zheng, Zhe Xue, Meiyu Liang, Feifei Kou, Zeli Guan

Organizations: School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications Beijing, China

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.

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University of South Carolina Columbia, South Carolina, USA

The rapid growth of scientific publishing has made it increasingly difficult to track how fast-moving areas evolve. Search engines and LLM-based assistants retrieve or summarize papers, but often hide how the corpus was selected, organized, or connected to temporal patterns. We present

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Abstract

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