Abbreviated text input

Abstract

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.