论文标题
Glassviz：可视化自动提取的入口点，用于探索问题驱动的可视化研究中的科学语料库
GlassViz: Visualizing Automatically-Extracted Entry Points for Exploring Scientific Corpora in Problem-Driven Visualization Research
论文作者
论文摘要
在本文中，我们报告了模型和概念验证视觉文本分析（VTA）工具的开发，以增强问题驱动的可视化研究（PDVR）连接中的文档发现。所提出的模型通过分析研究论文的Twodisjoint集合中所示的跨学科通信渠道来捕获认知模型和可视化专家之后的认知模型。采用高分销间的相似性来构建内容丰富的关键字群体，以作为推动Alarge文档语料库探索的切入点。在数字人文可视化研究的背景下，我们的方法得到了证明。
In this paper, we report the development of a model and a proof-of-concept visual text analytics (VTA) tool to enhance documentdiscovery in a problem-driven visualization research (PDVR) con-text. The proposed model captures the cognitive model followed bydomain and visualization experts by analyzing the interdisciplinarycommunication channel as represented by keywords found in twodisjoint collections of research papers. High distributional inter-collection similarities are employed to build informative keywordassociations that serve as entry points to drive the exploration of alarge document corpus. Our approach is demonstrated in the contextof research on visualization for the digital humanities.
