GlassViz: Visualizing Automatically-Extracted Entry Points for Exploring Scientific Corpora in Problem-Driven Visualization Research

In this paper, we report the development of a model and a proof-of-concept visual text analytics (VTA) tool to enhance document discovery in a problem-driven visualization research (PDVR) context. The proposed model captures the cognitive model followed by domain and visualization experts by analyzing the interdisciplinary communication channel as represented by keywords found in two disjoint collections of research papers. High distributional intercollection similarities are employed to build informative keyword associations that serve as entry points to drive the exploration of a large document corpus. Our approach is demonstrated in the context of research on visualization for the digital humanities.