Improved Language Models for Word Prediction and Completion with Application to Hebrew

Language models (LMs) are important components of many applications that work with natural language, such as word prediction and completion programs, automatic speech recognition, and machine translation. In this paper, we introduce various types of improvements for LMs dealing with word prediction and completion in Hebrew. Whereas previous systems for the Hebrew language apply known variants of existing LMs without any alteration, this study presents two types of improvements concerning the LMs: one is general and the other is special for the Hebrew language. These improvements enable all tested LMs to improve their keystroke saving abilities.