Improving the Template Generation for Chinese Character Error Detection with Confusion Sets

Abstract

In this paper, we propose a system that automatically generates templates for detecting Chinese character errors. We first collect the confusion sets for each high-frequency Chinese character. Error types include pronunciation-related errors and radical-related errors. With the help of the confusion sets, our system generates possible error patterns in context, which will be used as detection templates. Combined with a word segmentation module, our system generates more accurate templates. The experimental results show the precision of performance approaches 95%. Such a system should not only help teachers grade and check student essays, but also effectively help students learn how to write.