Measuring Research Difficulty of Academic Papers: A Case Study in Natural Language Processing
With the rapid growth of the number of academic papers, systematically evaluating the difficulty of research and its relationship to academic impact offers important significance for research topic selection and resource allocation. However, current studies lack quantitative assessments of research difficulty and its correlation with academic impact. This paper proposes a comprehensive evaluation system for research difficulty, incorporating factors such as academic collaboration, content, and references. Taking the field of Natural Language Processing (NLP) as a case study, we extract both internal and external features from academic papers, compute multiple research difficulty indicator...
| Authors | Haochuan Li, Jingyuan Li, Yi Zhao et al. |
|---|---|
| Primary category | cs.DL |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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