使用机器学习确定研究优先事项
Determining Research Priorities Using Machine Learning
AI总结：
本文提出用LDA分析天文学论文文本以识别高优先级研究方向，并通过与十年调查和白皮书的显著相关性验证，还构建了预测未来高被引内容的指标。
摘要：
我们总结了一项探索性研究，考察应用于公开专业文本的机器学习（ML）技术能否显著增强天文学战略规划。我们发现，基于隐含狄利克雷分配（LDA）并利用天文学期刊论文内容的方法，可用于推断高优先级研究领域。尽管LDA模型难以解释，但我们发现它们可能与有意义的关键词和科学论文密切相关，从而使主题模型能够获得人工解读。将这些模型应用于过去十年的天文学研究（“1998-2010”语料库）所得结果，与科学前沿小组报告的内容之间存在显著相关；该报告包含2010年美国国家科学院《天文学与天体物理学十年调查》（“DS2010”语料库）确定的高优先级研究领域。1998-2010语料库的模型结果与提交给十年调查的白皮书（“whitepapers”语料库）之间也存在显著相关。重要的是，我们基于这些结果得出了预测性指标，这些指标可作为先行指标，判断主题模型所建模的哪些内容未来将获得高引用。利用这些识别出的指标以及论文与主题模型之间的关联，可以识别出规划者应考虑的重要论文。我们工作的初步版本由Thronson等人2021年和Thomas等人2022年发表。
英文摘要：
We summarize our exploratory investigation into whether Machine Learning (ML) techniques applied to publicly available professional text can substantially augment strategic planning for astronomy. We find that an approach based on Latent Dirichlet Allocation (LDA) using content drawn from astronomy journal papers can be used to infer high-priority research areas. While the LDA models are challenging to interpret, we find that they may be strongly associated with meaningful keywords and scientific papers which allow for human interpretation of the topic models. Significant correlation is found between the results of applying these models to the previous decade of astronomical research ("1998-2010" corpus) and the contents of the science frontier panel report which contains high-priority research areas identified by the 2010 National Academies' Astronomy and Astrophysics Decadal Survey ("DS2010" corpus). Significant correlations also exist between model results of the 1998-2010 corpus and the submitted whitepapers to the Decadal Survey ("whitepapers" corpus). Importantly, we derive predictive metrics based on these results which can provide leading indicators of which content modeled by the topic models will become highly cited in the future. Using these identified metrics and the associations between papers and topic models it is possible to identify important papers for planners to consider. A preliminary version of our work was presented by Thronson etal. 2021 and Thomas etal. 2022.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
