The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work.
翻译:科学场所,例如会议和期刊的年度出版物数量正在迅速增加,因此,即使研究人员也越来越难跟踪研究课题及其进展。在这项任务中,研究人员可以得到自动化出版分析的支持。然而,许多这类方法导致无法解释、纯粹数字的表述方式。为了支持人类分析家,我们提出了空间轨迹专题,这个结构可以使人们对研究课题进行可理解的跟踪。我们展示了如何根据八种不同的分析方法来解释这些轨迹。为了获得可理解的结果,我们采用了非负矩阵化和适当的可视化技术。我们展示了我们对于从32个出版场所进行长达50年的机器学习研究的系列出版物的实用性。我们的新式分析方法可用于纸张分类、预测未来的研究课题,以及建议为提交未出版的工作安排适当的会议和期刊。