Text compression
Communications of the ACM
A communication tool for people with disabilities: lexical semantics for filling in the pieces
Assets '94 Proceedings of the first annual ACM conference on Assistive technologies
Semiautomatic disabbreviation of technical text
Information Processing and Management: an International Journal
A theory of stimulus-response compatibility applied to human-computer interaction
CHI '85 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Reactive Keyboard
Japanese text input system with digits
HLT '01 Proceedings of the first international conference on Human language technology research
Relaxing stylus typing precision by geometric pattern matching
Proceedings of the 10th international conference on Intelligent user interfaces
Mobile devices: soft keyboard text-entry enhanced by Visual Cues
UbiMob '04 Proceedings of the 1st French-speaking conference on Mobility and ubiquity computing
Using visual cues to help text entry on PDAs
IHM 2003 Proceedings of the 15th French-speaking conference on human-computer interaction on 15eme Conference Francophone sur l'Interaction Homme-Machine
Analysing performance in a word prediction system with multiple prediction methods
Computer Speech and Language
Text Entry Systems: Mobility, Accessibility, Universality
Text Entry Systems: Mobility, Accessibility, Universality
Sibylle, An Assistive Communication System Adapting to the Context and Its User
ACM Transactions on Accessible Computing (TACCESS)
Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility
KICSS'10 Proceedings of the 5th international conference on Knowledge, information, and creativity support systems
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We address the problem of improving the efficiency of natural language text input under degraded conditions (for instance, on PDAs or cell phones or by disabled users) by taking advantage of the informational redundacy in natural language. Previous approaches to this problem have been based on the idea of prediction of the text, but these require the user to take overt action to verify or select the system's predictions. We propose taking advantage of the duality between prediction and compression. We allow the user to enter text in compressed form, in particular, using a simple stipulated abbreviation method that reduces characters by about 30% yet is simple enough that it can be learned easily and generated relatively fluently. Using statistical language processing techniques, we can decode the abbreviated text with a residual word error rate of about 3%, and we expect that simple adaptive methods can improve this to about 1.5%. Because the system's operation is completely independent from the user's, the overhead from cognitive task switching and attending to the system's actions online is eliminated, opening up the possibility that the compression-based method can achieve text input efficiency improvements where the prediction-based methods have not