Basic word completion and prediction for hebrew

Abstract

This research aims to improve keystroke savings for completion and prediction of Hebrew words. This task is very important to augmentative and alternative communication systems as well as to search engines, short messages services, and mobile phones. The proposed model is composed of Hebrew corpora containing 177M words, a morphological analyzer, various n-gram Hebrew language models and other tools. The achieved keystroke savings rate is higher than those reported in a previous Hebrew word prediction system and previous word prediction systems in other languages. Two main findings have been found: the larger the corpus that the language model is trained on, the better predictions that are achieved and a morphological analyzer helps only when the language model is based on only one corpus. © 2012 Springer-Verlag Berlin Heidelberg.