Grammatical error detection in automatic essay scoring and feedback

Handbook of Automated Essay Evaluation: Current Applications and New Directions

Volumn , Issue , 2013, Pages 251-266

  Gamon, Michael ^a   Chodorow, Martin ^b   Leacock, Claudia ^c   Tetreault, Joel ^d  

^a UNIVERSITY OF WASHINGTON   (United States)

^b CITY UNIVERSITY OF NEW YORK   (United States)

^c CTB MCGRAW HILL   (United States)

^d EDUCATIONAL TESTING SERVICE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GRAMMATICAL ERROR DETECTIONS; GRAMMATICAL ERRORS; NEGATIVE CORRELATION; TOTAL ERRORS;

ERRORS;

EID: 84899583443     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9780203122761     Document Type: Chapter

References (31)

1. Allan, J. (1980). Nouns and countability. Language, 56(3), 541–567
2. Bergsma, S., Lin, D., & Goebel, R. (2009). Web-scale n-gram models for lexical disambiguation. Paper presented at the 21st International Joint Conference on Artificial Intelligence
3. Chodorow, M., Gamon, M., & Tetreault, J. (2010). The utility of article and preposition error correction systems for English language learners: Feedback and assessment. Language Testing, 27(3), 419–436
4. Choi, J. (2010). The impact of automated essay scoring (AES) for improving English language learners’ essay writing. Unpublished Ph.D dissertation. University of Virginia
5. Conners, R. J., & Lunsford, A. A. (1988). Frequency of formal errors in current college writing or Ma and Pa Kettle do research. College Composition and Communication, 39(4), 395–409
6. CTB/McGraw-Hill. (2010). WVDE WESTEST 2 technical report. Monterey, CA
7. Dahlmeier, D., & Ng, T. H. (2011). Grammatical error correction with alternating structure optimization. Paper presented at the Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT-2011)
8. Dale, R., & Kilgarriff, A. (2010). Helping our own: Text massaging for computational linguistics as a new shared task. Paper presented at the INLG
9. Dalgish, G. (1985). Computer-assisted ESL research and courseware development. Computers and Composition, 2(4), 45–61
10. Elliot, S., & Mikulas, C. (2004). The impact of MY Access! use on student writing performance: A technology overview and four studies. Paper presented at the the Annual Meeting of the American Educational Research Association, San Diego, CA, April
11. Enright, M. K., & Quinlan, T. (2010). Complementing human judgment of essays written by English language learners with e-rater® scoring. Language Testing, 27(3), 317–334
12. Gamon, M. (2010). Using mostly native data to correct errors in learners’ writing: A meta-classifier approach. Paper presented at the NAACL 2010, Association for Computational Linguistics, June
13. Gamon, M. (2011). High-order sequence modeling for language learner error detection. Paper presented at the Sixth Workshop on Innovative Use of NLP for Building Educational Applications, June
14. Gamon, M., & Leacock, C. (2010). Search right and thou shalt find … Using web queries for learner error detection. Paper presented at the NAACL 2010, Association for Computational Linguistics, June
15. Guo, Y., & Beckett, G. H. (2007). The hegemony of English as a global language. Paper presented at the International Symposium on Social Policy and Social Engineering
16. Heift, T., & Schulze, M. (2007). Errors and intelligence in computer-assisted language learning: Parsers and pedagogues. New York, NY and London: Routledge
17. Jensen, K., Heidorn, G. E. and Richardson, S. D. (1993). Natural language processing: The PLNLP approach. Dordrecht: Kluwer
18. Leacock, C., & Chodorow, M. (2003). Automated grammatical error detection. In M. D. Shermis and J. C. Burstein (Eds.), Automated essay scoring: A cross-disciplinary perspective (pp. 195–207). Mahwah, NJ: Lawrence Erlbaum Associates
19. Leacock, C., Chodorow, M., Gamon, M., & Tetreault, J. (2010). Automated grammatical error detection for language learners. Paper presented at the Synthesis Lectures on Human Language Technologies
20. Lipnevich, A., and Smith, J. (2009). Effects of differential feedback on students’ examination performance. Journal of Experimental Psychology: Applied, 15, 319–333
21. Madnani, N., Tetreault, J., Chodorow, M., & Rozovskaya, A. (2011). They can help: Using crowdsourcing to improve the evaluation of grammatical error detection systems. Paper presented at the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, OR, June 19–24
22. Marcus, M. P., Santorini, B., & Marcinkiewicz, M. A. (1993). Building a large annotated corpus of English: The Penn Tree Bank. Computational Linguistics, 19, 313–330
23. Rozovskaya, A., & Roth, D. (2011). Algorithm selection and model adaptation for ESL correction tasks. Paper presented at the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT-2011)
24. Shermis, M. D., Burstein, J., & Bliss, L. (2004). The impact of automated essay scoring on high stakes writing assessments. Paper presented at the annual meetings of the National Council on Measurement in Education, San Diego, CA, April
25. Shermis, M. D., Wilson, C., Diao, G., & Diao, Y. (2008). The impact of automated essay scoring on writing outcomes online submission. Paper presented at the Annual Meetings of the National Council on Measurement in Education, New York, NY, March 25–27
26. Tetreault, J., & Chodorow, M. (2008a). Native judgments of non-native usage: Experiments in preposition error detection. Paper presented at the COLING Workshop on Human Judgments in Computational Linguistics, Manchester, UK, August 23
27. Tetreault, J., & Chodorow, M. (2008b). The ups and downs of preposition error detection. Paper presented at the COLING, Manchester, UK
28. Tetreault, J., & Chodorow, M. (2009). Examining the use of region web counts for ESL error detection. Paper presented at the Web as Corpus Workshop (WAC-5), San Sebastian, Spain
29. Tetreault, J., Filatova, E., & Chodorow, M. (2010). Rethinking grammatical error annotation and evaluation with the amazon mechanical Turk. Paper presented at the 5th Workshop on Innovative Use of NLP for Building Educational Applications, Los Angeles, CA, June 5
30. Wagner, J. (2012). Detecting grammatical errors with treebank-induced, probabilistic parsers. Dublin City University, Dublin, Ireland
31. Warschauer, M., & Ware, P. (2006). Automated writing evaluation: Defining the classroom research agenda. Language Teaching Research, 10(2), 1–24