Using Mostly Native Data to Correct Errors in Learners' Writing: A Meta-Classifier Approach

We present results from a range of experiments on article and preposition error correction for non-native speakers of English. We first compare a language model and error-specific classifiers (all trained on large English corpora) with respect to their performance in error detection and correction. We then combine the language model and the classifiers in a meta-classification approach by combining evidence from the classifiers and the language model as input features to the meta-classifier. The meta-classifier in turn is trained on error-annotated learner data, optimizing the error detection and correction performance on this domain. The meta-classification approach results in substantial gains over the classifier-only and language-model-only scenario. Since the meta-classifier requires error-annotated data for training, we investigate how much training data is needed to improve results over the baseline of not using a meta-classifier. All evaluations are conducted on a large error-annotated corpus of learner English.

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In  Proceedings of NAACL 2010

Publisher  Association for Computational Linguistics
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TypeInproceedings
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