Recognizing article errors using prepositional information

In this paper, we propose a prepositional model that uses prepositional information to detect article errors often seen in English sentences written by Japanese learners of English. The conventional methods for detecting article errors include a statistical model that is based on statistics obtained from an electronic corpus created from documents such as English newspapers. However, the usage of the articles has many exceptions, and thus the performance of the statistical model is not yet sufficient. Hence, in the prepositional model, the performance of the statistical model is improved by using prepositional information, and errors are detected while also taking into account exceptional usages of the articles. In an experiment, it was verified that the performance of the prepositional model (F-measure = 0.72) is a huge improvement over the performance of the statistical model (F-measure = 0.53) in dealing with article errors in prepositional phrases. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(12): 17–26, 2006; Published online in Wiley InterScience (). DOI 10.1002sscj.20527