Using Error-Annotated ESL Data to Develop an ESL Error Correction System

This paper presents research on building a model of grammatical error correction, for preposition errors in particular, in English text produced by language learners. Unlike most previous work which trains a statistical classifier exclusively on well-formed text written by native speakers, we train a classifier on a large-scale, error-tagged corpus of English essays, relying on contextual and grammatical features surrounding preposition usage. First, we show that such a model can achieve high performance values: 93.3% precision and 14.8% recall for error detection and 81.7% precision and 13.2% recall for error detection and correction when tested on preposition replacement errors. Second, we show that this model outperforms models trained on well-edited text produced by native speakers of English. We discuss the implications of our approach in the area of language error modeling and the issues stemming from working with a noisy data set whose error annotations are not exhaustive.