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.