Improving word prediction using Markov models and Heuristic methods

Abstract

The goal of this project was to design and implement a new word predictor for Swedish that would suggest words that are more grammatically appropriate, thus presenting a lower cognitive load for users and saving significantly more keystrokes than the previous predictor. The new predictor that was designed and developed uses a probabilistic language model based on the well-established ideas of the trigram predictor for speech recognition, developed by IBM. In tests, this program has been shown to result in keystroke savings of 46% given five predictions—a substantial saving compared with the 35% savings achieved with the previous predictor.