Automatic Extraction of Learner Errors in ESL Sentences Using Linguistically Enhanced Alignments
26th International Conference on Computational Linguistics (COLING 2016)
Association for Computational Linguistics
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Felice, M., Bryant, C., & Briscoe, T. (2016). Automatic Extraction of Learner Errors in ESL Sentences Using Linguistically Enhanced Alignments. 26th International Conference on Computational Linguistics (COLING 2016) https://doi.org/10.17863/CAM.6388
This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the Association for Computational Linguistics.
We propose a new method of automatically extracting learner errors from parallel English as a Second Language (ESL) sentences in an effort to regularise annotation formats and reduce inconsistencies. Specifically, given an original and corrected sentence, our method first uses a linguistically enhanced alignment algorithm to determine the most likely mappings between tokens, and secondly employs a rule-based function to decide which alignments should be merged. Our method beats all previous approaches on the tested datasets, achieving state-of-the-art results for automatic error extraction.
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This record's DOI: https://doi.org/10.17863/CAM.6388
This record's URL: https://www.repository.cam.ac.uk/handle/1810/261217
Attribution 4.0 International
Licence URL: http://creativecommons.org/licenses/by/4.0/
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