使用新型数据和整体模型来改进可持续发展目标的自动标签
Dirk U Wulff1,2, Dominik S Meier1, Rui Mata1
1Max Planck Institute for Human Development, Berlin, Germany.
概括
联合国可持续发展目标 (SDGs) 标签的不同基于文本的系统在准确性和偏见上有所不同. 结合多个系统的整体模型为监测可持续发展目标进展提供了更好的性能.
科学领域:
- 计算社会科学 计算社会科学
- 数据科学数据科学数据科学
- 政策分析 政策分析
背景情况:
- 基于文本的标签系统对于监测联合国可持续发展目标 (SDGs) 的进展至关重要.
- 现有的系统表现出性能和潜在偏差的变化,影响了可持续发展目标进展评估的可靠性.
研究的目的:
- 系统地比较著名的SDG标签系统的表现.
- 评估文本源对系统准确性的影响.
- 评估集合模型在改进SDG标签方面的有效性.
主要方法:
- 在各种文本数据集中对多个SDG标签系统进行比较分析.
- 系统灵敏度 (真正正比率) 和特异性 (真负比率) 的评估.
- 组合模型的开发和测试,结合单个标签系统.
主要成果:
- 在各个SDG标签系统中观察到敏感性和特异性的显著差异.
- 系统表现出系统性的偏见,在不同的SDG和文本类型中表现不同.
- 整体模型的性能优于单个系统,显示出更高的准确性和稳定性.
结论:
- 选择SDG标签系统显著影响SDG工作的自动评估.
- 整体方法提供了一个更可靠的方法来分析对可持续发展目标工作的普遍性.
- 研究人员和政策制定者应该考虑整体方法来准确监测SDG.
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