使用自然语言处理和机器学习进行半自动化标题摘要选.
Maximilian Pilz1,2, Samuel Zimmermann3, Juliane Friedrichs4
1University of Heidelberg - Institute of Medical Biometry, Heidelberg, Germany. maximilian.pilz@itwm.fraunhofer.de.
Systematic reviews
|November 2, 2024
概括
通过自然语言处理 (NLP) 和机器学习 (ML) 实现系统审查的标题摘要选自动化. 这种方法半自动化了这个过程,节省了时间,提高了研究效率.
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
- 计算语言学 计算语言学
背景情况:
- 系统审查需要广泛的标题摘要选,这是一个艰苦的过程.
- 自然语言处理 (NLP) 和机器学习 (ML) 提供了自动化这项任务的潜力.
- 对于在查中实施NLP和ML的实际指导是非常需要的.
研究的目的:
- 为在标题摘要选中使用NLP和ML提供一个全面的管道.
- 为了在系统性审查中应用这些计算技术,提供实际指导.
主要方法:
- 开发一个NLP管道,为ML算法准备标题和摘要.
- 应用ML算法来预测出版物对于全文选的相关性.
- 用两个真实世界的系统审查来展示方法论.
主要成果:
- 拟议的NLP和ML管道有效地预测了用于全文选的出版物.
- 该方法在现实世界系统审查场景中显示出有希望的表现.
- 该方法为半自动化标题摘要选提供了可行的解决方案.
结论:
- 在系统性审查中,NLP和ML可以显著帮助半自动化标题摘要选.
- 成功实施需要仔细考虑项目特定因素.
- 这种方法可以提高系统审查准备的效率.
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