A Commonsense Approach to Predictive Text Entry
Tom Stocky, Alexander Faaborg, Henry Lieberman
People cannot type as fast as they think, especially when faced with the constraints of mobile devices. There have been numerous approaches to solving this problem, including research in augmented input devices and predictive typing aids. We propose an alternative approach to predictive text entry based on commonsense reasoning. Using OMCSNet, a large-scale semantic network that aggregates and normalizes the contributions made to Open Mind Common Sense (OMCS), our system is able to show significant success in predicting words based on their first few letters. We evaluate this commonsense approach against traditional statistical methods, demonstrating comparable performance, and suggest that combining commonsense and statistical approaches could achieve superior performance. Mobile device implementations of the commonsense predictive typing aid demonstrate that such a system could be applied to just about any computing environment.
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