Automatic Error Detection Method for Japanese Particles

In this article, I propose an approach for detecting appropriate usage models of case particles in the writings of Japanese Second Language learners (JSL) in order to create a Japanese automatic error detection system. As learner corpora are receiving special attention as an invaluable source for the educational feedback to improve teaching material and methodology, automatic methods of error analysis have become necessary to facilitate the development of learner corpora. Particle errors account for a substantial proportion of all grammatical errors by JSL learners and discourage the readers from understanding the meaning of a sentence. To address this issue, I trained Support Vector Machines (SVMs) to learn correct patterns of case particle usages from a Japanese newspaper text corpus. The result differs according to the kind of the particle. The object marker “wo (を)” has the best score of 81.4%. Applying the “wo (を)” model to detect wrong use of the particle, the result shows 92.6% for precision and 34.3% for recall with the 100 instance test set. The result shows 95.2% for precision and 37.6% for recall with the 200 instance test set. Although this is a pilot study, this experiment shows a promising result for Japanese particle error detection. Key terms: Automatic Error Detection, Learner Corpora, Support Vector Machines, N-gram, Case Particle Detection