Modifying Corpus Annotation to Support the Analysis of Learner Language

A crucial question for automatically analyzing learner language is to determine which grammatical information is relevant and useful for learner feedback. Based on knowledge about how learner language varies in its grammatical properties, we propose a framework for reusing analyses found in corpus annotation and illustrate its applicability to Korean postpositional particles. Simple transformations of the corpus annotation allow one to quickly use state-of-the-art parsing methods.

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