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Due to its performance, the system is suitable for languages with high inflection. The system was tested on Czech, however it can easily be adapted an arbitrary language.
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We have shown that the system performs well in general (letter KSPC on average was 0.64, real KSPC on average was 0.77) but performs even better on specific domains with the appropriate custom-built model (letter KSPC and real KSPC were on average 0.63 and 0.73 respectively). We measured the key stroke per character (KSPC) rate in two different modes: one – called letter KSPC – excludes the control keys since they are input method specific, the other – called real KSPC – includes all key strokes. The system was then extended by custom-built model that can consist of domain or user specific n-grams. , 12 from a Czech corpus and evaluated the writing system on Czech texts. We calculated 150 millions most frequent n-grams for n = 1. We also designed the client-side script that is suitable for desktop computers without touchscreens. We designed the high-performance server-side script that returns either the most probable endings of a word or the most probable following words. The paper presents a new predictive writing system based on n-grams calculated from a large corpus. However, the same corpus size is significantly insufficient for languages with many word forms. Systems designed for English are easier than those for flective languages since even smaller models allow reasonable coverage. Most predictive writing systems are based on n-gram model with different size.
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