Personalized unknown word detection in non-native language reading using eye gaze

Abstract

This paper proposes a method to detect unknown words during natural reading of non-native language text by using eye-tracking features. A previous approach utilizes gaze duration and word rarity features to perform this detection. However, while this system can be used by trained users, its performance is not sufficient during natural reading by untrained users. In this paper, we 1) apply support vector machines (SVM) with novel eye movement features that were not considered in the previous work and 2) examine the effect of personalization. The experimental results demonstrate that learning using SVMs and proposed eye movement features improves detection performance as measured by F-measure and that personalization further improves results.