Abstract
We applied two standard, open source tools for detecting spelling and grammar errors to the AESW2016 shared task: After the Deadline and LanguageTool. The tools' output was combined with a Maximum Entropy machine learning model to classify each input sentence as requiring or not requiring any edits. This approach yielded the second-highest precision of 64.41% in the binary estimation task at AESW2016, but also the lowest recall of 36.85%, resulting in an F-Measure of 46.34%.
Cite
CITATION STYLE
Witte, R., & Sateli, B. (2016). Combining off-the-shelf grammar and spelling tools for the automatic evaluation of scientific writing (aesw) shared task 2016. 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. 252–255). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-0529
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