Using entity-based features to model coherence in student essays

Authors:
Jill Burstein;Joel Tetreault;Slava Andreyev
Affiliations:
Educational Testing Service, Princeton, NJ;Educational Testing Service, Princeton, NJ;Educational Testing Service, Princeton, NJ
Venue:
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Year:
2010

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Abstract

We show how the Barzilay and Lapata entity-based coherence algorithm (2008) can be applied to a new, noisy data domain --- student essays. We demonstrate that by combining Barzilay and Lapata's entity-based features with novel features related to grammar errors and word usage, one can greatly improve the performance of automated coherence prediction for student essays for different populations.