Building a Korean particle error detection system from the ground u

This dissertation describes an approach to automatically detecting and correcting grammatical errors in text produced by Korean language learners. Specifically, we focus on Korean particles, which have a range of functions including case marking and indicate properties similar to English prepositions. There are two main goals for this research: to establish reliable data sources that can serve as a foundation for Korean language learning research endeavors, and to develop an accurate error detection system. The machine learning-based system is built to detect errors of particle omission and substitution, then to select the best particle to produce grammatical output. The resources and results outlined in this work should prove useful in aiding other researchers working on Korean error detection and in moving the field one step closer to robust multi-lingual methods.