Word prediction using language models

The thesis utilizes ngram language models to improve text entry with QWERTY keyboard by the means of word prediction. Related solutions are briedly introduced. Then follows theoretical background for the work. The analysis in the next part divides problems into four tasks: language model training, incorporating model for word prediction, GUI component and evaluation framework. The realization combines Python and C++. The used corpora come from Czech (19\,M words) and (84\,M words) English Wikipedia articles. A small corpus of Czech educative texts was used to test domain adaptation. The quality metrics are defined and various configuration are measured. The best solutions reduced keystrokes per character to 0.44, resp. 0.55 for English, resp. Czech on testing data