Characterizing Uncertainty in the Visual Text Analysis Pipeline - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Characterizing Uncertainty in the Visual Text Analysis Pipeline

Résumé

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.
Fichier principal
Vignette du fichier
TextVis_Uncertainty.pdf (235.25 Ko) Télécharger le fichier
Vignette du fichier
UncertaintyModel.jpg (101.77 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Format : Figure, Image
Licence : CC BY NC - Paternité - Pas d'utilisation commerciale

Dates et versions

hal-03784519 , version 1 (23-09-2022)

Identifiants

  • HAL Id : hal-03784519 , version 1

Citer

Pantea Haghighatkhah, Mennatallah El-Assady, Jean-Daniel Fekete, Narges Mahyar, Carita Paradis, et al.. Characterizing Uncertainty in the Visual Text Analysis Pipeline. VIS4DH 2022 - 7th Workshop on Visualization for the Digital Humanities, Oct 2022, Oklahoma City, United States. ⟨hal-03784519⟩
67 Consultations
123 Téléchargements

Partager

Gmail Facebook X LinkedIn More