Abstract
In this paper, we address the problem of quantifying the overall extent to which a testtaker's essay deals with the topic it is assigned (prompt). We experiment with a number of models for word topicality, and a number of approaches for aggregating word-level indices into text-level ones. All models are evaluated for their ability to predict the holistic quality of essays. We show that the best texttopicality model provides a significant improvement in a state-of-art essay scoring system. We also show that the findings of the relative merits of different models generalize well across three different datasets.
Cite
CITATION STYLE
Klebanov, B. B., Flor, M., & Gyawali, B. (2016). Topicality-based indices for essay scoring. 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. 63–72). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-0507
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