Modeling Thesis Clarity in Student Essays
Isaac Persing and Vincent Ng
The 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013)
Sofia, Bulgaria, August 4-9, 2013
Recently, researchers have begun exploring methods of scoring student essays with respect to particular dimensions of quality such as coherence, technical errors, and relevance to prompt, but there is relatively little work on modeling thesis clarity. We present a new annotated corpus and propose a learning-based approach to scoring essays along the thesis clarity dimension. Additionally, in order to provide more valuable feedback on why an essay is scored as it is, we propose a second learning-based approach to identifying what kinds of errors an essay has that may lower its thesis clarity score.
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