Abstract
In this paper, we use supervised machine learning to automatically identify the problem localization of peer-review feedback. Using five features extracted via Natural Language Processing techniques, the learned model significantly outperforms a standard baseline. Our work suggests that it is feasible for future tutoring systems to generate assessments regarding the use of localization in student peer reviews. © 2010 Springer-Verlag.
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CITATION STYLE
Xiong, W., & Litman, D. (2010). Identifying problem localization in peer-review feedback. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6095 LNCS, pp. 429–431). https://doi.org/10.1007/978-3-642-13437-1_93
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