Characterizing Uncertainty in the Visual Text Analysis Pipeline
This addresses the problem of uncertainty communication in visual text analysis for researchers and practitioners, but it is incremental as it focuses on characterization rather than new solutions.
The paper characterizes sources of uncertainty in visual text analysis pipelines, identifying six sources across labeling, modeling, and analysis phases and discussing their propagation.
Current visual text analysis approaches rely on sophisticated processing pipelines. Each step of such a pipeline potentially amplifies any uncertainties from the previous step. To ensure the comprehensibility and interoperability of the results, it is of paramount importance to clearly communicate the uncertainty not only of the output but also within the pipeline. In this paper, we characterize the sources of uncertainty along the visual text analysis pipeline. Within its three phases of labeling, modeling, and analysis, we identify six sources, discuss the type of uncertainty they create, and how they propagate.