Text prediction is the task of suggesting text while the user is typing. Its main aim is to reduce the number of keystrokes that are needed to type a text. In this paper, we address the influence of text type and domain differences on text prediction quality. By training and testing our text prediction algorithm on four different text types (Wikipedia, Twitter, transcriptions of conversational speech and FAQ) with equal corpus sizes, we found that there is a clear effect of text type on text prediction quality: training and testing on the same text type gave percentages of saved.
