arXiv:2204.08476v2 Announce Type: replace-cross 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 be
The proliferation of scientific papers and cross-disciplinary research necessitates new tools for analysis, driven by advancements in AI and natural language processing.
This research outlines methods to extract valuable insights and track thematic evolution within the vast and interconnected landscape of scientific literature, which is crucial for identifying emerging trends and dependencies.
The ability to efficiently analyze and map the evolution of scientific domains will improve research funding allocation, collaboration strategies, and the identification of pivotal discoveries.
- · AI researchers
- · Scientific research institutions
- · Research funding bodies
- · Data scientists
- · Researchers relying on manual literature reviews
- · Inefficient scientific silos
Improved understanding of research dependencies and interconnections across scientific fields.
More targeted and efficient allocation of research funding towards promising and impactful areas.
Accelerated scientific discovery and innovation through enhanced synthesis of interdisciplinary knowledge.
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Read at arXiv cs.LG