Combining rule-based learning and memory-based learning for automatic word spacing in simple message service

Abstract

Short message service (SMS) is a widely used service in modern mobile phones that allows users to send or receive short text messages. Current SMS, however, has two problems of inconvenient input and short message length. These problems can be resolved if a phone has an ability of automatic word spacing. This is because users need not put spaces in sending messages and longer messages are possible as they contain no space. Thus, automatic word spacing will be a very useful tool for SMS, if it can be commercially served. The practical issues of implementing it on the devices such as mobile phones are small memory and low computing power of the devices. To tackle these problems, this paper proposes a combined model of rule-based learning and memory-based learning. According to the experimental results, the model shows higher accuracy than rule-based learning or memory-based learning alone. In addition, the generated rules are so small and simple that the proposed model is appropriate for small memory devices.