科技文献智能分析：AI助力的科研新范式
Intelligent Analysis of Scientific Literature: A Novel AI Driven Research Framework
摘要: 本文提出了一种新的科研新范式，科技文献智能分析。通过对比传统文献分析方法，强调了科技文献智能分析在提升研究效率和深度方面的显著优势。文章详细阐述了科技文献智能分析的概念及其在科研方面的巨大作用，并展望了自然语言处理、机器学习等技术在实现科技文献智能分析方面的理论依据，设计了概念验证系统，并对部分核心功能，利用一些随机论文数据进行了测试与分析。科技文献智能分析有望成为科研人员们不可或缺的研究工具，推动科学研究迈向新的高度。
Abstract: This paper introduces a novel research framework, “Intelligent Analysis of Scientific Literature”, for bibliometric analysis. By comparing it with traditional bibliometric analysis methods, the paper highlights the significant advantages of Intelligent Analysis of Scientific Literature in improving research efficiency and depth. The concept of Intelligent Analysis of Scientific Literature and its profound impact on scientific research are elaborated. The theoretical foundation, including natural language processing and machine learning, for realizing Intelligent Analysis of Scientific Literature is outlined. A proof-of-concept system is designed, and some core functions are tested with some random journal paper data, with results demonstrated and analyzed. “Intelligent Analysis of Scientific Literature” is expected to become an indispensable research tool for researchers, propelling scientific research to new heights.
文章引用：黄晓晔, 吴琰. 科技文献智能分析：AI助力的科研新范式[J]. 计算机科学与应用, 2024, 14(12): 222-235. https://doi.org/10.12677/csa.2024.1412257

参考文献

[1] Kratochvíl, J. (2017) Comparison of the Accuracy of Bibliographical References Generated for Medical Citation Styles by Endnote, Mendeley, Refworks and Zotero. The Journal of Academic Librarianship, 43, 57-66. [Google Scholar] [CrossRef] 
[2] Moral-Muñoz, J.A., Herrera-Viedma, E., Santisteban-Espejo, A. and Cobo, M.J. (2020) Software Tools for Conducting Bibliometric Analysis in Science: An Up-to-Date Review. El Profesional de la Información, 29, e290103. [Google Scholar] [CrossRef] 
[3] Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z. and Zhang, Y. (2024) A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly. High-Confidence Computing, 4, Article 100211. [Google Scholar] [CrossRef] 
[4] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., et al. (2017) Attention Is All You Need. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, 4-9 December 2017, 6000-6010.
[5] Collins, M. and Singer, Y. (1999) Unsupervised Models for Named Entity Classification. Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, College Park, 21-22 June 1999, 100-110.
[6] Binjumah, W.M. (2024) The Role of Machine Learning and Deep Learning Approaches to Improve Optical Communication Systems. Journal of Intelligent Learning Systems and Applications, 16, 418-429. [Google Scholar] [CrossRef] 
[7] Pantel, P. and Lin, D. (2002) Discovering Word Senses from Text. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, 23-26 July 2002, 613-619. [Google Scholar] [CrossRef] 
[8] Abunemeh, S. (2024) Investigating the Impact of Conflict Management Approaches on Organizational Productivity in Healthcare Settings: A Qualitative Exploration. Open Journal of Social Sciences, 12, 322-335. [Google Scholar] [CrossRef] 
[9] Brown, P.F., de Souza, P.V., Mercer, R.L., Della Pietra, V.J. and Lai, J.C. (1992) Class-Based n-Gram Models of Natural Language. Computational Linguistics, 18, 467-479.
[10] Keith, B., Fuentes, E. and Meneses, C. (2017) A Hybrid Approach for Sentiment Analysis Applied to Paper. Proceedings of ACM SIGKDD Conference, Halifax, 13-17 August 2017, 1-10.
[11] Knight, K. and Marcu, D. (2002) Summarization Beyond Sentence Extraction: A Probabilistic Approach to Sentence Compression. Artificial Intelligence, 139, 91-107. [Google Scholar] [CrossRef] 
[12] 黄晓晔. 移动互联网时代商业交易中的信任建构——基于对J社区微信团购群的考察[J]. 中国矿业大学学报(社会科学版), 2023, 25(3): 91-104.
[13] Rajpurkar, P., Zhang, J., Lopyrev, K. and Liang, P. (2016) SQuAD: 100,000+ Questions for Machine Comprehension of Text. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, 1-5 November 2016, 2383-2392.
[14] 黄晓晔. 技术逻辑指引下的国外互联网信任研究发展态势——基于CiteSpace的识别和可视化分析[J]. 江南大学学报(人文社会科学版), 2023, 22(2): 52-65.