Using an Error-Annotated Learner Corpus to Develop an ESL/EFL 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 written by EFL learners, relying on contextual and grammatical features surrounding preposition usage. First, we show that such a model can achieve high performance values: 93.3%

• Type: Conference
• Year: 2010
• Where: LREC
• Authors: Na-Rae Han, Joel R. Tetreault, Soo-Hwa Lee, Jin-Young Ha
• Keywords: Education, Features Surrounding Preposition, Grammatical Error Correction, LREC 2010, Preposition Errors