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Research on Domain Information Mining and Theme Evolution of Scientific Papers

arXiv 2204.08476 ↗First posted 18 Apr 2022cs.DL cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 29 of its 91 references that are in the map. First posted on arXiv 18 Apr 2022.

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29 of 91 references.

References · 29

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