Evolutionary Ranking on Multiple Word Correction Algorithms Using Neural Network Approach

Abstract

Multiple algorithms have been developed to correct user’s typing mistakes. However, an optimum solution is hardly identified among them. Moreover, these solutions rarely produce a single answer or share common results, and the answers may change with time and context. These have led this research to combine some distinct word correction algorithms to produce an optimal prediction based on database updates and neural network learning. In this paper, three distinct typing correction algorithms are integrated as a pilot research. Key factors including Time Change, Context Change and User Feedback are considered. Experimental results show that 57.50% Ranking First Hitting Rate (HR) with the samples of category one and a best Ranking First Hitting Rate of 74.69% within category four are achieved.