Text-to-text Semantic Similarity for Automatic Short Answer Grading

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In this paper, the authors explore unsupervised techniques for the task of automatic short answer grading.

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9 p.

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Mohler, Michael & Mihalcea, Rada, 1974- March 2009.

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This paper is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Engineering to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 455 times , with 4 in the last month . More information about this paper can be viewed below.

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In this paper, the authors explore unsupervised techniques for the task of automatic short answer grading.

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9 p.

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Abstract: In this paper, the authors explore unsupervised techniques for the task of automatic short answer grading. The authors compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating automatic feedback from the student answers. Overall, our system significantly and consistently outperforms other unsupervised methods for short answer grading that have been proposed in the past.

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  • European Chapter of the Association for Computational Linguistics (EACL), 2009, Athens, Greece

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UNT Scholarly Works

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  • March 2009

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  • Jan. 31, 2011, 2:01 p.m.

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  • April 28, 2014, 2:20 p.m.

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Mohler, Michael & Mihalcea, Rada, 1974-. Text-to-text Semantic Similarity for Automatic Short Answer Grading, paper, March 2009; [Stroudsburg, Pennsylvania]. (digital.library.unt.edu/ark:/67531/metadc31017/: accessed July 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.