基于语义和元数据特征的科学论文引用潜力的即时预测:以人工智能领域为例
Hou Zhu1,2, Li Shuhuai1,3
1School of Information Management, Sun Yat-sen University, Guangzhou, China.
PloS one
|December 2, 2024
概括
这项研究引入了一种新的方法,用于使用元数据和语义分析即时预测学术论文在出版后的影响. 该模型达到88%的准确性,可以更快地识别有影响力的研究.
科学领域:
- 图书统计学 图书统计学
- 信息科学 信息科学 信息科学
- 机器学习 机器学习
背景情况:
- 科学出版物的快速增长需要有效的方法来识别高影响力的论文.
- 引用频率是学术影响力的关键指标,但传统的预测方法需要历史数据,导致延迟.
- 及时发现有影响力的论文至关重要,但受到延迟引用预测的阻碍.
研究的目的:
- 开发一种新的方法,可以在发表后立即预测一篇论文的引用潜力.
- 通过结合元数据和丰富的语义信息来提高预测准确性.
- 为了快速识别潜在的高影响力的学术论文.
主要方法:
- 使用机器学习模型来预测引用潜力.
- 提取语义信息,包括抽象内容,语义跨度和语义屈曲.
- 利用纸质元数据与语义特征一起,以提高预测能力.
主要成果:
- 拟议的模型实现了88%的预测准确度,即时引用的潜力.
- 经验实验验证了模型的有效性,并确定了最佳的输入特征组合.
- 该方法允许立即评估论文的潜在学术影响.
结论:
- 这种新的方法提供了学术论文引用潜力的准确和即时预测.
- 将元数据与语义信息整合起来,可以显著提高预测性能.
- 这种方法有助于及时发现快速扩张的学术环境中的有影响力的研究.
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