Literature Explorer: effective retrieval of scientific documents through nonparametric thematic topic detection
Literature Explorer: effective retrieval of scientific documents through nonparametric thematic topic detection
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DOI：
10.1007/s00371-019-01721-7
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Shaopeng Wu;Youbing Zhao;Farzad Parvinzamir;Nikolaos Ersotelos;Hui Wei;Feng Dong
中科院分区：
文献类型：
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作者：
Shaopeng Wu;Youbing Zhao;Farzad Parvinzamir;Nikolaos Ersotelos;Hui Wei;Feng Dong
如今，科学研究人员面临着大量文献的快速增长。虽然这些出版物提供了丰富而有价值的信息，但数据集的规模使研究人员难以有效地管理和搜索所需的信息。文献探索者是一个新的交互式可视化分析套件，通过挖掘和交互式可视化，方便访问所需的科学文献。我们提出了一种新的主题挖掘方法，能够从科学语料库中发现“主题主题”。这些主题与人类研究人员在科学领域中常用的研究主题具有明确的语义关联，因此是人类可解释的。它们还有助于有效的文档检索。可视化分析套件由一组可视化组件组成，这些组件与底层主题主题检测紧密结合，以支持交互式文档检索。视觉组件在设计原理和目标下充分集成。通过专家评估，给出了客观测量和主观评价结果。并与传统主题建模方法的结果进行了比较。
Scientific researchers are facing a rapidly growing volume of literatures nowadays. While these publications offer rich and valuable information, the scale of the datasets makes it difficult for the researchers to manage and search for desired information efficiently. Literature Explorer is a new interactive visual analytics suite that facilitates the access to desired scientific literatures through mining and interactive visualisation. We propose a novel topic mining method that is able to uncover “thematic topics” from a scientific corpus. These thematic topics have an explicit semantic association to the research themes that are commonly used by human researchers in scientific fields, and hence are human interpretable. They also contribute to effective document retrieval. The visual analytics suite consists of a set of visual components that are closely coupled with the underlying thematic topic detection to support interactive document retrieval. The visual components are adequately integrated under the design rationale and goals. Evaluation results are given in both objective measurements and subjective terms through expert assessments. Comparisons are also made against the outcomes from the traditional topic modelling methods.