Detecting and Correcting Learner Korean Particle Omission Errors

We detect errors in Korean post-positional particle usage, focusing on optimizing omission detection, as omissions are the single-biggest factor in particle errors for learners of Korean. We also develop a system for predicting the correct choice of a particle. For omission detection, we model the task largely on English grammatical error detection, but employ Korean-specific features and filters; likewise, output analysis and the omission correction system illustrate how unique properties of Korean, such as the distinct types of particles used, need to be accounted for in adapting the system, thereby moving the field one step closer to robust multi-lingual methods.