Abstract
This is a report on the methods used and results obtained by the UW-Stanford team for the Automated Evaluation of Scientific Writing (AESW) Shared Task 2016 on grammatical error detection. This team developed a symbolic grammar-based system augmented with manually defined mal-rules to accommodate and identify instances of highfrequency grammatical errors. System results were entered both for the probabilistic estimation track, where we ranked second, and for the Boolean decision track, where we ranked fourth.
Cite
CITATION STYLE
Flickinger, D., Goodman, M., & Packard, W. (2016). Uw-stanford system description for aesw 2016 shared task on grammatical error detection. In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2016 at the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2016 (pp. 105–111). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-0511
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