  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.
