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A meta learning approach to grammatical error correction

Published:08 July 2012Publication History

ABSTRACT

We introduce a novel method for grammatical error correction with a number of small corpora. To make the best use of several corpora with different characteristics, we employ a meta-learning with several base classifiers trained on different corpora. This research focuses on a grammatical error correction task for article errors. A series of experiments is presented to show the effectiveness of the proposed approach on two different grammatical error tagged corpora.

References

  1. R. K. Ando and T. Zhang. 2005. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6, pp. 1817--1853. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. U. Aydin, S. Murat, Olcay T Yildiz, A. Ethem, 2009, Incremental construction of classifier and discriminant ensembles, Information Science, 179 (9), pp. 144--152. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. L. Breiman, 1996, Bagging predictors, Machine Learning, pp. 123--140. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. Cohen, L. Rokach, O. Maimon, 2007, Decision tree instance space decomposition with grouped gain-ratio, Information Science, 177 (17), pp. 3592--3612. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. D. Dahlmeier, H. T. Ng, 2011, Grammatical error correction with alternating structure optimization, In Proceedings of the 49th Annual Meeting of the ACL-HLT 2011, pp. 915--923. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. R. De Felice. 2008. Automatic Error Detection in Non-native English. Ph.D. thesis, University of Oxford.Google ScholarGoogle Scholar
  7. S. Dzeroski, B. Zenko, 2004, Is combining classifiers with stacking better than selecting the best one?, Machine Learning, 54 (3), pp. 255--273. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. J. R. Finkel, T. Grenager, and C. Manning. 2005. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. In Proceedings of the 43nd Annual Meeting of the ACL, pp. 363--370. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. N. R. Han, M. Chodorow, and C. Leacock. 2006. Detecting errors in English article usage by non-native speakers. Natural Language Engineering, 12(02), pp. 115--129. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. N. R. Han, J. Tetreault, S. H. Lee, and J. Y. Ha. 2010. Using an error-annotated learner corpus to develop an ESL/EFL error correction system. In Proceedings of LREC.Google ScholarGoogle Scholar
  11. D. Klein and C. D. Manning. 2003a. Accurate unlexicalized parsing. In Proceedings of ACL, pp. 423--430. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. D. Klein and C. D. Manning. 2003b. Fast exact inference with a factored model for natural language processing. Advances in Neural Information Processing Systems (NIPS 2002), 15, pp. 3--10.Google ScholarGoogle Scholar
  13. K. Knight and I. Chander. 1994. Automated postediting of documents. In Proceedings of AAAI, pp. 779--784. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. J. Lee. 2004. Automatic article restoration. In Proceedings of HLT-NAACL, pp. 31--36. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. R. Nagata, A. Kawai, K. Morihiro, and N. Isu. 2006. A feedback-augmented method for detecting errors in the writing of learners of English. In Proceedings of COLING-ACL, pp. 241--248. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. A. Mariko, 2007, Grammatical errors across proficiency levels in L2 spoken and written English, The Economic Journal of Takasaki City University of Economics, 49 (3, 4), pp. 117--129.Google ScholarGoogle Scholar
  17. E. Menahem, L. Rokach, Y. Elovici, 2009, Troika-An imporoved stacking schema for classification tasks, Information Science, 179 (24), pp. 4097--4122. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. G. Minnen, F. Bond, and A. Copestake. 2000. Memory-based learning for article generation. In Proceedings of CoNLL, pp. 43--48. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. E. Izumi, K. Uchimoto, H. Isahara, 2005, Error annotation for corpus of Japanese learner English, In Proceedings of the 6th International Workshop on Linguistically Interpreted Corpora, pp. 71--80.Google ScholarGoogle Scholar
  20. A. Rozovskaya and D. Roth. 2010. Training paradigms for correcting errors in grammar and usage. In Proceedings of HLT-NAACL, pp. 154--162. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. K. Toutanova and C. D. Manning. 2000. Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger. In Proceedings of the Joint SIGDAT Conference on EMNLP/VLC-2000, pp. 63--70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. H. Yannakoudakis, T. Briscoe, B. Medlock, 2011, A new dataset and method for automatically grading ESOL texts, In Proceedings of ACL, pp. 180--189. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. G. P. Zhang, 2007, A neural network ensemble method with jittered training data for time series forecasting, Information Sciences: An International Journal, 177 (23), pp. 5329--5346. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image DL Hosted proceedings
    ACL '12: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
    July 2012
    420 pages

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    Association for Computational Linguistics

    United States

    Publication History

    • Published: 8 July 2012

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