Improving SMS Usability Using Bayesian Networks

During the last years, the significant increase of mobile communications has resulted in the wide acceptance of a plethora of new services, like communication via written short messages (SMS). The limitations of the dimensions and the number of keys of the mobile phone keypad are probably the main obstacles of this service. Numerous intelligent techniques have been developed aiming at supporting users of SMS services. Special emphasis has been provided to the efficient and effective editing of words. In the presented research, we introduce a predictive algorithm that forecasts Greek letters occurrence during the process of compiling an SMS. The algorithm is based on Bayesian networks that have been trained with sufficient Greek corpus. The extracted network infers the probability of a specific letter in a word given one, two or three previous letter that have been keyed by the user with precision that reaches 95%. An important advantage, compared to other predictive algorithms is that the use of a vocabulary is not required, so the limited memory resources of mobile phones can easily accommodate the presented algorithm. The proposed method1 achieves improvement in the word editing time compared to the traditional editing method by a factor of 34.72%, as this has been proven by using Keystroke Level Modeling technique described in the paper.