Characterizing Uncertainty in the Visual Text Analysis Pipeline

TL;DR

This paper examines the sources and propagation of uncertainty in the visual text analysis pipeline, emphasizing the importance of communicating uncertainty throughout the process for better interpretability.

Contribution

It identifies six sources of uncertainty across labeling, modeling, and analysis phases, and discusses their impact and propagation within the pipeline.

Findings

Six sources of uncertainty identified

Uncertainty propagation mechanisms analyzed

Guidelines for communicating uncertainty proposed

Abstract

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.