积极学习模型和先前知识对难以捉摸的相关论文发现时间的影响:一个模拟研究
Fionn Byrne1, Laura Hofstee2, Jelle Teijema2
1Department of Information and Computing Science, Faculty of Science, Utrecht University, Utrecht, The Netherlands.
Systematic reviews
|July 8, 2024
概括
积极学习 (AL) 软件可以加快系统审查. 模型选择,特别是特征提取器,对找到难以捉摸的论文产生影响,而先前的知识对发现时间 (TD) 的影响较小.
科学领域:
- 信息科学 信息科学 信息科学
- 图书统计学 图书统计学
- 系统审查方法论 系统审查方法论
背景情况:
- 积极学习 (AL) 软件通过预测相关性来加速记录选.
- 识别难以捉摸的相关论文对于防止有偏见的系统审查结果至关重要.
- 时间到发现 (TD) 量化了在找到相关论文之前选的记录,有助于发现难以捉摸的论文.
研究的目的:
- 调查模型选择和先前知识对难以找到相关论文的发现时间 (TD) 的影响.
- 分析这些因素如何影响选过程中难以捉摸的论文的排名顺序.
主要方法:
- 进行了模拟研究,复制了选过程.
- 该研究使用了一组数据集,其中包含已发表的系统综述的标题,摘要和标签.
- 评估了不同的积极学习模型和特征提取器.
主要成果:
- 选择主动学习 (AL) 模式显著影响了TD值和难以捉摸的论文的排名顺序.
- 特征提取器的选择是这些变化的主要驱动因素.
- 之前的知识并没有显著影响TD值或难以捉摸的相关论文的排名顺序.
结论:
- 模型和特征提取器的选择对于优化系统审查中难以捉摸的论文的识别至关重要.
- 未来的研究应该专注于了解难以捉摸的论文的特征,以改善其检测.
- 积极学习策略可以通过考虑特征提取对找到难以定位的相关文献的影响来改进.
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