论文标题
主题太空轨迹：关于机器学习文献的案例研究
Topic Space Trajectories: A case study on machine learning literature
论文作者
论文摘要
科学场所的年度出版物数量，例如会议和期刊，正在迅速增长。因此，即使对于研究人员来说，跟踪研究主题及其进步越来越困难。在此任务中，可以通过自动出版物分析来支持研究人员。然而，许多这样的方法导致无法解释，纯粹的数值表示。为了支持人类分析师，我们提出了主题空间轨迹，该结构允许对研究主题进行可理解的跟踪。我们证明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，我们采用非负基质分解以及合适的可视化技术。我们在32个出版物场所进行了50年的机器学习研究，我们的方法在出版物语料库中的适用性。我们的新颖分析方法可以用于纸质分类，预测未来的研究主题，并建议安装会议和期刊以提交未发表的工作。
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the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work.
