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Characterizing Uncertainty in the Visual Text Analysis Pipeline

Haghighatkhah, Pantea ; El-Assady, Mennatallah ; Fekete, Jean Daniel ; Mahyar, Narges ; Paradis, Carita LU orcid ; Simaki, Vasiliki LU and Speckmann, Bettina (2022) 7th IEEE Workshop on Visualization for the Digital Humanities, VIS4DH 2022 p.25-30
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. The goal of this paper is to bring the attention of the visualization community to additional types and sources of... (More)

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. The goal of this paper is to bring the attention of the visualization community to additional types and sources of uncertainty in visual text analysis and to call for careful consideration, highlighting opportunities for future research.

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author
; ; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Human-centered computing, Treemaps, Visualization, Visualization techniques
host publication
Proceedings - 2022 IEEE 7th Workshop on Visualization for the Digital Humanities, VIS4DH 2022
pages
6 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
7th IEEE Workshop on Visualization for the Digital Humanities, VIS4DH 2022
conference location
Virtual, Online, United States
conference dates
2022-10-16
external identifiers
  • scopus:85145661877
ISBN
9781665476683
DOI
10.1109/VIS4DH57440.2022.00010
language
English
LU publication?
yes
id
3d64d4e5-a21a-4020-838b-c621c69b518e
date added to LUP
2023-01-16 16:57:44
date last changed
2025-03-07 04:27:39
@inproceedings{3d64d4e5-a21a-4020-838b-c621c69b518e,
  abstract     = {{<p>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. The goal of this paper is to bring the attention of the visualization community to additional types and sources of uncertainty in visual text analysis and to call for careful consideration, highlighting opportunities for future research.</p>}},
  author       = {{Haghighatkhah, Pantea and El-Assady, Mennatallah and Fekete, Jean Daniel and Mahyar, Narges and Paradis, Carita and Simaki, Vasiliki and Speckmann, Bettina}},
  booktitle    = {{Proceedings - 2022 IEEE 7th Workshop on Visualization for the Digital Humanities, VIS4DH 2022}},
  isbn         = {{9781665476683}},
  keywords     = {{Human-centered computing; Treemaps; Visualization; Visualization techniques}},
  language     = {{eng}},
  pages        = {{25--30}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Characterizing Uncertainty in the Visual Text Analysis Pipeline}},
  url          = {{http://dx.doi.org/10.1109/VIS4DH57440.2022.00010}},
  doi          = {{10.1109/VIS4DH57440.2022.00010}},
  year         = {{2022}},
}