Abstract
Mastering proper article usage, especially in the English language, has been known to pose an extreme challenge to non-native speakers whose L1 languages have no concept of articles. Although the development of correction methods for article usage has posed a challenge for researchers, current methods do not perfectly correct the articles. In addition, proper article usage is not taught by these methods. Therefore, they are not useful for those wishing to learn a language with article usage. In this paper, we discuss the necessity of presenting evidence for corrections of English article usage. We demonstrate the effectiveness of this approach to improve the writing skills of English learners.
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References
Leacock, C., Chodorow, M., Gamon, M., Tetreault, J.R.: Automated Grammatical Error Detection for Language Learners. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers (2010)
Nicholls, D.: The cambridge learner corpus - error coding and analysis for lexicography and elt. In: Corpus Linguistics 2003, pp. 572–581 (2003)
Dahlmeier, D., Ng, H.T., Ng, E.J.F.: NUS at the HOO 2012 Shared Task. In: Proceedings of the Seventh Workshop on Building Educational Applications Using NLP, pp. 216–224 (2012)
Dale, R., Anisimoff, I., Narroway, G.: HOO 2012: A report on the preposition and determiner error correction shared task. In: Proceedings of the Seventh Workshop on Building Educational Applications Using NLP, pp. 54–62 (June 2012)
Dale, R., Kilgarriff, A.: Helping our own: Text massaging for computational linguistics as a new shared task. In: Proceedings of the 6th International Natural Language Generation Conference, pp. 261–265 (2010)
Chodorow, M., Gamon, M., Tetreault, J.: The utility of article and preposition error correction systems for English language learners: Feedback and assessment. Language Testing 27(3), 419–436 (2010)
Han, N.R., Chodorow, M., Leacock, C.: Detecting errors in English article usage by non-native speakers. Natural Language Engineering 12, 115–129 (2006)
Minnen, G., Bond, F., Copestake, A.: Memory-based learning for article generation. In: Proceedings of the 2nd Workshop on Learning Language in Logic and the 4th Conference on Computational Natural Language Learning, CoNLL 2000, pp. 43–48 (2000)
Gamon, M., Gao, J., Brockett, C., Klementiev, A., Dolan, W.B., Belenko, D., Vanderwende, L.: Using contextual speller techniques and language modeling for ESL error correction. In: Proceedings of the Third International Joint Conference on Natural Language Processing (IJCNLP 2008), pp. 449–456 (2008)
Gamon, M.: Using mostly native data to correct errors in learners’ writing: a meta-classifier approach. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT 2010), pp. 163–171 (2010)
Nagata, R., Nakatani, K.: Evaluating performance of grammatical error detection to maximize learning effect. In: Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010): Posters, pp. 894–900 (2010)
Izumi, E., Uchimoto, K., Saiga, T., Supnithi, T., Isahara, H.: Automatic error detection in the japanese learners’ English spoken data. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, vol. 2, pp. 145–148 (2003)
Lee, J.: Automatic article restoration. In: HLT-NAACL 2004: Student Research Workshop, pp. 31–36 (2004)
Chodorow, M., Tetreault, J., Han, N.R.: Detection of grammatical errors involving prepositions. In: Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions, Prague, Czech Republic. Association for Computational Linguistics, pp. 25–30 (June 2007)
De Felice, R., Pulman, S.G.: A classifier-based approach to preposition and determiner error correction in L2 English. In: Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pp. 169–176 (2008)
Tetreault, J.R., Chodorow, M.: The ups and downs of preposition error detection in ESL writing. In: Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pp. 865–872 (2008)
Han, N.R., Tetreault, J.R., Lee, S.H., Ha, J.Y.: 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 2010), pp. 763–770 (2010)
Gamon, M.: High-order sequence modeling for language learner error detection. In: Proceedings of the Sixth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 180–189 (2011)
Dahlmeier, D., Ng, H.T.: Grammatical error correction with alternating structure optimization. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 915–923 (2011)
Brockett, C., Dolan, W.B., Gamon, M.: Correcting ESL errors using phrasal SMT techniques. In: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pp. 249–256 (2006)
Hermet, M., Désilets, A.: Using first and second language models to correct preposition errors in second language authoring. In: Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 64–72 (2009)
Yi, X., Gao, J., Dolan, W.B.: A web-based English proofing system for English as a second language users. In: Proceedings of the Third International Joint Conference on Natural Language Processing (IJCNLP 2008), pp. 619–624 (2008)
Rozovskaya, A., Roth, D.: Algorithm selection and model adaptation for ESL correction tasks. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 924–933 (2011)
Liu, T., Zhou, M., Gao, J., Xun, E., Huang, C.: PENS: a machine-aided English writing system for Chinese users. In: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, pp. 529–536 (2000)
Loper, E., Bird, S.: NLTK: the Natural Language Toolkit. In: Proceedings of the ACL 2002 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, vol. 1, pp. 63–70 (2002)
Tsuruoka, Y., Tateishi, Y., Kim, J.-D., Ohta, T., McNaught, J., Ananiadou, S., Tsujii, J.: Developing a robust Part-of-Speech tagger for biomedical text (chapter 36). In: Bozanis, P., Houstis, E.N. (eds.) PCI 2005. LNCS, vol. 3746, pp. 382–392. Springer, Heidelberg (2005)
Bird, S., Dale, R., Dorr, B., Gibson, B., Joseph, M., Kan, M.Y., Lee, D., Powley, B., Radev, D., Tan, Y.F.: The ACL Anthology Reference Corpus: A reference dataset for bibliographic research in computational linguistics. In: Proceedings of the Sixth International Conference on Language Resources and Evaluation, LREC 2008 (2008)
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Umezawa, J., Mizuno, J., Okazaki, N., Inui, K. (2013). Evidence in Automatic Error Correction Improves Learners’ English Skill. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2013. Lecture Notes in Computer Science, vol 7817. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37256-8_46
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DOI: https://doi.org/10.1007/978-3-642-37256-8_46
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