A Graph-Based Approach for Learner-Tailored Teaching of Korean Grammar Constructions.

Foreign language learning on an intermediate level is often a complicated task, as it requires acquisition not only of vocabulary and language rules but of context-dependent meanings of words. This is especially relevant for Category IV languages like Korean, in which the same tokens could be both words and grammar tags. The textbook adapted versions of words and contexts often fail to capture the existing complexity, while the real world examples may be too hard for a novice and even an intermediate level learner. In addition, the particular learner may be familiar with some functions and contexts for a particular word, but not with the other ones. To alleviate this complexity problem, we propose a semantic graph based personalized tutoring system. The learning corpus is constructed using real-world sentences from a newspaper, which are translated using an automated service and processed with NLP techniques to extract token functions. A graph is used to track word and grammar construct context and thus find similar and dissimilar word use cases, as well as for the estimation of sentence complexity. The system then shows words and grammar constructs from real-world sentences to learners and records their understanding in each context. The collected context dependent understanding data together with the sentence complexity estimation are then used to estimate the learner's level and tailor the sentence set accordingly. The resultant approach could be extended to the tutoring of context-dependent meanings in other languages.