This paper describes a method of detecting grammatical and lexical errors made by Japanese learners of English and other techniques that improve the accuracy of error detection with a limited amount of training data. In this paper, we demonstrate to what extent the proposed methods hold promise by conducting experiments using our learner corpus, which contains information on learners' errors.
Izumi et al. REF use empirical techniques to detect omission-and replacement-type grammatical and lexical errors in Japanese learners of English.
Automatic Error Detection In The Japanese Learners' English Spoken Data
Annual Meeting Of The Association For Computational Linguistics - Interactive Posters And Demonstrations
Computer Science