Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Predicting text entry speed on mobile phones
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
The keystroke-level model for user performance time with interactive systems
Communications of the ACM
Improving mobile internet usability
Proceedings of the 10th international conference on World Wide Web
Machine Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
The growth of cognitive modeling in human-computer interaction since GOMS
Human-Computer Interaction
Métamodèle de règles d'adaptation pour la plasticité des interfaces homme-machine
IHM '07 Proceedings of the 19th International Conference of the Association Francophone d'Interaction Homme-Machine
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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 method 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.