Using an Error-Annotated Learner Corpus to Develop an ESL/EFL Error Correction System

Na-Rae Han, Joel Tetreault, Soo-Hwa Lee, Jin-Young Ha

Abstract
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 ESL learners, 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.
Anthology ID:
L10-1566
Volume:
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
Month:
May
Year:
2010
Address:
Valletta, Malta
Editors:
Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Mike Rosner, Daniel Tapias
Venue:
LREC
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Publisher:
European Language Resources Association (ELRA)
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URL:
http://www.lrec-conf.org/proceedings/lrec2010/pdf/821_Paper.pdf
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Cite (ACL):
Na-Rae Han, Joel Tetreault, Soo-Hwa Lee, and Jin-Young Ha. 2010. Using an Error-Annotated Learner Corpus to Develop an ESL/EFL Error Correction System. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
Cite (Informal):
Using an Error-Annotated Learner Corpus to Develop an ESL/EFL Error Correction System (Han et al., LREC 2010)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2010/pdf/821_Paper.pdf
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