论文标题
在视觉文本分析管道中表征不确定性
Characterizing Uncertainty in the Visual Text Analysis Pipeline
论文作者
论文摘要
当前的视觉文本分析方法取决于复杂的处理管道。此类管道的每个步骤都可能会放大上一步的任何不确定性。为了确保结果的可理解性和互操作性，重要的是要清楚地传达输出的不确定性，而且在管道中传达不确定性。在本文中，我们表征了沿视觉文本分析管道的不确定性来源。在标签，建模和分析的三个阶段中，我们确定了六个来源，讨论了它们创造的不确定性类型以及它们如何传播。
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.
