An unsupervised approach to preposition error correction
Aminul Islam, Diana Zaiu Inkpen · 2010
In this work, an unsupervised statistical method for automatic correction of preposition errors using the Google n-gram data set is presented and compared to the state-of-the-art. We use the Google n-gram data set in a back-off fashion that increases the performance of the method. The method works automatically, does not require any human-annotated knowledge resources (e.g., ontologies) and can be applied to English language texts, including non-native (L2) ones in which preposition errors are known to be numerous. The method can be applied to other languages for which Google n-grams are available.