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In this paper, we report the development of a model and a proof-of-conceptvisual text analytics (VTA) tool to enhance documentdiscovery in aproblem-driven visualization research (PDVR) con-text. The proposed modelcaptures the cognitive model followed bydomain and visualization experts byanalyzing the interdisciplinarycommunication channel as represented by keywordsfound in twodisjoint collections of research papers. High distributionalinter-collection similarities are employed to build informativekeywordassociations that serve as entry points to drive the exploration ofalarge document corpus. Our approach is demonstrated in the contextof researchon visualization for the digital humanities.

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