Collaborative Research: Scaling Insight into Science: Assessing the value and effectiveness of machine assisted classification within a statistical system

协作研究：扩展对科学的洞察力：评估统计系统内机器辅助分类的价值和有效性

• 批准号: 1422492
• 负责人: Jordan Boyd-Graber
• 金额: $ 19.5万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422492&HistoricalAwards=false
• 关键词: Collaborative; Research; Scaling; Insight; into

The project develops and compares cutting edge methods for the classification and analysis of American scientific research and compares them to existing manually generated approaches. The project examines the strengths and weaknesses of four computational approaches: topic models, network-based partitioning methods, Wikipedia-based labeling, and an active-learning approach. In particular, the research examines whether or not the different approaches can correctly classify established research areas and discover emerging fields across a broad range of disciplines. Each approach is evaluated based on a set of metrics measuring effectiveness, computational costs, human oversight costs, and the need to retain consistency with existing classification frameworks. The work directly responds to a number of National Academies recommendations and National Center for Science and Engineering Statistics (NCSES) reports that suggest using computational approaches to classify scientific research fields. A longer-term impact is improvement in data collection, processing, and reporting of key national statistics on science and engineering.

该项目开发并比较了用于美国科学研究分类和分析的最先进方法，并将其与现有的手动生成方法进行了比较。 该项目研究了四种计算方法的优点和缺点：主题模型，基于网络的分区方法，基于维基百科的标签和主动学习方法。特别是，该研究考察了不同的方法是否可以正确地对已建立的研究领域进行分类，并在广泛的学科中发现新兴领域。 每种方法都是基于一组衡量有效性、计算成本、人力监督成本以及与现有分类框架保持一致性的指标进行评估的。这项工作直接响应了一些国家科学院的建议和国家科学与工程统计中心（NCSES）的报告，这些报告建议使用计算方法对科学研究领域进行分类。一个长期的影响是改善数据收集、处理和报告科学和工程方面的关键国家统计数据。