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Citation link: https://doi.org/10.26092/elib/2329

Publisher DOI: https://doi.org/10.1145/3313831.3376815
Putze_Platform for studying Self-repairing auto-correction_2020_accepted-version_PDF-A.pdf
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Platform for Studying Self-Repairing Auto-Corrections in Mobile Text Entry based on Brain Activity, Gaze, and Context


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Authors: Putze, Felix  
Ihrig, Tilman 
Schultz, Tanja  
Stuerzlinger, Wolfgang  
Abstract: 
Auto-correction is a standard feature of mobile text entry. While the performance of state-of-the-art auto-correct methods is usually relatively high, any errors that occur are cumbersome to repair, interrupt the flow of text entry, and challenge the user's agency over the process. In this paper, we describe a system that aims to automatically identify and repair auto-correction errors. This system comprises a multi-modal classifier for detecting auto-correction errors from brain activity, eye gaze, and context information, as well as a strategy to repair such errors by replacing the erroneous correction or suggesting alternatives. We integrated both parts in a generic Android component and thus present a research platform for studying self-repairing end-to-end systems. To demonstrate its feasibility, we performed a user study to evaluate the classification performance and usability of our approach.
Keywords: Text entry; Auto-correction; Self-repair; Eye gaze; EEG
Issue Date: 2020
Publisher: ACM
Journal/Edited collection: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems 
Start page: 1
End page: 13
Type: Artikel/Aufsatz
ISBN: 9781450367080
Secondary publication: yes
Document version: Postprint
DOI: 10.26092/elib/2329
URN: urn:nbn:de:gbv:46-elib70084
Faculty: Fachbereich 03: Mathematik/Informatik (FB 03) 
Appears in Collections:Forschungsdokumente

  

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