Article

A character-level error analysis technique for evaluating text entry methods

Authors:

I. Scott MacKenzie,

R. William SoukoreffAuthors Info & Claims

NordiCHI '02: Proceedings of the second Nordic conference on Human-computer interaction

Pages 243 - 246

https://doi.org/10.1145/572020.572056

Published: 19 October 2002 Publication History

Get Access

Abstract

We describe a technique to analyse character-level errors in evaluations of text entry methods. Using an algorithm for sequence comparisons, we generate the set of optimal alignments between the presented and transcribed text. Percharacter errors, categorized as insertions, substitutions, or deletions, are obtained by analysing the alignments and applying a weighting factor. A detailed example using a real data set is given.

References

[1]

MacKenzie, I. S. Mobile text entry using three keys, Proceedings of NordiCHI 2002 (to appear).

Digital Library

Google Scholar

[2]

Soukoreff, R. W., and MacKenzie, I. S. Measuring errors in text entry tasks: An application of the Levenshtein string distance statistic, Extended Abstracts of CHI 2001. New York: ACM, 2001, 319-320.

Digital Library

Google Scholar

Cited By

View all

Kawamura AUchida A(2024)Extraction of Sound Sources in Specified Area Using Binaural Microphones両耳マイクロホンを用いた指定領域内音源の抽出IEEJ Transactions on Electronics, Information and Systems10.1541/ieejeiss.144.267144:3(267-275)Online publication date: 1-Mar-2024
https://doi.org/10.1541/ieejeiss.144.267
Richardson MBotros FShi YSnow BGuo PZhang LDong JVertanen KMa SWang R(2024)StegoType: Surface Typing from Egocentric CamerasAdjunct Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3672539.3686762(1-14)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3672539.3686762
Mitchell CWobbrock J(2024)Characterizing "Motor Ability" for Ability-Based DesignProceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3663548.3675646(1-15)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3663548.3675646
Show More Cited By

Index Terms

A character-level error analysis technique for evaluating text entry methods
1. Applied computing
  1. Document management and text processing
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

Recommendations

Measuring errors in text entry tasks: an application of the Levenshtein string distance statistic
CHI EA '01: CHI '01 Extended Abstracts on Human Factors in Computing Systems

We propose a new technique based on the Levenshtein minimum string distance statistic for measuring error rates in text entry research. The technique obviates the need to artificially constrain subjects to maintain synchronization with the presented ...
Recent developments in text-entry error rate measurement
CHI EA '04: CHI '04 Extended Abstracts on Human Factors in Computing Systems

Previously, we defined robust and easy-to-calculate error metrics for text entry research. Herein, we announce a software implementation of this error analysis technique. We build on previous work, by introducing two new metrics, and we extend error ...
Analyzing the input stream for character- level errors in unconstrained text entry evaluations

Recent improvements in text entry error rate measurement have enabled the running of text entry experiments in which subjects are free to correct errors (or not) as they transcribe a presented string. In these “unconstrained” experiments, it is no longer ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

NordiCHI '02: Proceedings of the second Nordic conference on Human-computer interaction

October 2002

309 pages

ISBN:1581136161

DOI:10.1145/572020

Conference Chair:
Olav W. Bertelsen
University of Aarhus, Denmark
,
Program Chairs:
Susanne Bødker
University of Aarhus, Denmark
,
Kari Kuutti
University of Oulu, Finland

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 October 2002

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

NORDICHI02

Sponsor:

UAARHUS
AIAS
ACM

NORDICHI02: NORDICHI 2002, The second Nordic conference on Human-Computer Interaction

October 19 - 23, 2002

Aarhus, Denmark

Acceptance Rates

Overall Acceptance Rate 379 of 1,572 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

67
Total Citations
View Citations
739
Total Downloads

Downloads (Last 12 months)33
Downloads (Last 6 weeks)13

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Kawamura AUchida A(2024)Extraction of Sound Sources in Specified Area Using Binaural Microphones両耳マイクロホンを用いた指定領域内音源の抽出IEEJ Transactions on Electronics, Information and Systems10.1541/ieejeiss.144.267144:3(267-275)Online publication date: 1-Mar-2024
Richardson MBotros FShi YSnow BGuo PZhang LDong JVertanen KMa SWang R(2024)StegoType: Surface Typing from Egocentric CamerasAdjunct Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3672539.3686762(1-14)Online publication date: 13-Oct-2024
Mitchell CWobbrock J(2024)Characterizing "Motor Ability" for Ability-Based DesignProceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3663548.3675646(1-15)Online publication date: 27-Oct-2024
Richardson MBotros FShi YGuo PSnow BZhang LDong JVertanen KMa SWang R(2024)StegoType: Surface Typing from Egocentric CamerasProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676343(1-14)Online publication date: 13-Oct-2024
Streli PRichardson MBotros FMa SWang RHolz C(2024)TouchInsight: Uncertainty-aware Rapid Touch and Text Input for Mixed Reality from Egocentric VisionProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676330(1-16)Online publication date: 13-Oct-2024
Chen ZLiu YLiu G(2024)Expressive Voice Conversion Using Perturbation and Autoencoder Loss2024 IEEE 12th International Conference on Information, Communication and Networks (ICICN)10.1109/ICICN62625.2024.10761574(70-75)Online publication date: 21-Aug-2024
Boehme RCoors SOster PMunser-Kiefer MHilbert S(2024)Machine Learning for Spelling Acquisition: How Accurate is the Prediction of Specific Spelling Errors in German Primary School Students?Computers and Education: Artificial Intelligence10.1016/j.caeai.2024.100233(100233)Online publication date: May-2024
Myers B(2024)Pick, Click, Flick!undefinedOnline publication date: 14-Mar-2024
Mussakhojayeva SDauletbek KYeshpanov RVarol H(2023)Multilingual Speech Recognition for Turkic LanguagesInformation10.3390/info1402007414:2(74)Online publication date: 28-Jan-2023
Gil HOakley I(2023)ThumbAirProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694746:4(1-30)Online publication date: 11-Jan-2023
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Measuring errors in text entry tasks: an application of the Levenshtein string distance statistic

Recent developments in text-entry error rate measurement

Analyzing the input stream for character- level errors in unconstrained text entry evaluations