RUVA – A Radical Universal Visual Annotation for Web-Based Language Learning
摘要
In this paper, we introduce a novel language representation, which we have built on five cornerstones: radical construction grammar, uniquely identifiable concepts, visualization, Uniform Meaning Representation, and Interlinear Morphemic Glossing. The resulting annotation is perfectly suited for multilingual applications, in particular second language acquisition. We have integrated the representation into our Web-Based Language Learning environment, which allows to design engaging annotation tasks by using augmented browsing technology. As first use case, we focus on Japanese language learning because of the growing popularity at our university and the particular challenges posed by this difficult language.