An Automatic Text Annotation System to Improve Reading Comprehension of Chinese Ancient Texts

Since many scholars begin to deal with large amounts of text by means of digital text, using computer technology and online resource to improve the interpretation of ancient texts has become more and more important. Thus, an automatic text annotation system (ATAS) that can collect resources from diverse databases through Linked Data (LD) for automatically annotating ancient texts was developed in this study to promote the reading performance of learners. Based on the quasi-experimental design, the developed ATAS and MARKUS semi-automatic text annotation system were compared whether the significant differences in the reading effectiveness and technology acceptance existed or not. The experimental results reveal that the developed ATAS has higher reading effectiveness in digital learning than MARKUS semi-automatic text annotation system, but not reaching the statistically significant difference. The technology acceptance of the ATAS is higher than that of MARKUS semi-automatic text annotation system. Furthermore, among all the considered LD sources, Moedict that is an online Chinese dictionary was confirmed as the most helpful one. To sum up, ATAS provided a more friendly digital reading environment to support learners on interpreting ancient texts, also helping learners obtain a deeper and broader understanding in the ancient text.