Characterizing Uncertainty in the Visual Text Analysis Pipeline
(2022)- 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 comprehensibil- ity 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 comprehensibil- ity 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 uncer- tainty in visual text analysis and to call for careful consideration, highlighting opportunities for future research. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/64e43eee-daea-4db7-a1f0-7c14349a0028
- author
- Haghighatkhah, Pantea ; El-Assady, Mennatallah ; Fekete, Jean-Daniel ; Mahyar, Narges ; Paradis, Carita LU ; Simaki, Vasiliki LU and Speckmann, Bettina
- organization
- publishing date
- 2022
- type
- Working paper/Preprint
- publication status
- published
- subject
- keywords
- Human-centered computing, Visualization, Visu- alization techniques, Treemaps, Visualization design and evaluation methods
- pages
- 6 pages
- publisher
- arXiv.org
- DOI
- 10.48550/arXiv.2209.13498
- language
- English
- LU publication?
- yes
- additional info
- To appear in Proceedings of the 2022 IEEE Workshop on Evaluation and Beyond - Methodological Approaches to Visualization (BELIV '22)
- id
- 64e43eee-daea-4db7-a1f0-7c14349a0028
- date added to LUP
- 2022-11-29 08:41:53
- date last changed
- 2023-02-01 11:47:51
@misc{64e43eee-daea-4db7-a1f0-7c14349a0028, 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 comprehensibil- ity 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 uncer- tainty in visual text analysis and to call for careful consideration, highlighting opportunities for future research.}}, author = {{Haghighatkhah, Pantea and El-Assady, Mennatallah and Fekete, Jean-Daniel and Mahyar, Narges and Paradis, Carita and Simaki, Vasiliki and Speckmann, Bettina}}, keywords = {{Human-centered computing; Visualization; Visu- alization techniques; Treemaps; Visualization design and evaluation methods}}, language = {{eng}}, note = {{Preprint}}, publisher = {{arXiv.org}}, title = {{Characterizing Uncertainty in the Visual Text Analysis Pipeline}}, url = {{http://dx.doi.org/10.48550/arXiv.2209.13498}}, doi = {{10.48550/arXiv.2209.13498}}, year = {{2022}}, }