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.
@article{arxiv.2209.13498,
title = {Characterizing Uncertainty in the Visual Text Analysis Pipeline},
author = {Pantea Haghighatkhah and Mennatallah El-Assady and Jean-Daniel Fekete and Narges Mahyar and Carita Paradis and Vasiliki Simaki and Bettina Speckmann},
journal= {arXiv preprint arXiv:2209.13498},
year = {2022}
}