• Korean Journal of Cognitive Science
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• v.22 no.1
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• pp.1-18
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• 2011

With rapid spread of handheld devices like cellular or smart phones, a short message service (SMS) comes on the public as a communication means. SMS is very cheap and can be easily written down on the storage in order not to forget it, hence it is widely used to inform schedules (time and place). In this paper, we develop a system for automatically registering schedules extracted from SMS text messages. SMS text messages are very short and concise, but include a lot of Internet words like slangs and abbreviations. These have made it difficult to extract information on schedules from them. Also handheld devices have some limitations on computing power and storage and then applying general natural language processing modules like morphological analysis to the devices are somewhat hard. To relax these burdens, we extract schedule informations from SMS messages using machine learning methods like condition random field (CRF) without using any language processing modules and register the informations on the schedule management system of handheld devices. Our proposed automatic schedule registration system has implemented on Samsung Omnia phone for experiments.