Sentence Similarity Analysis with Applications in Automatic Short Answer Grading

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In this dissertation, I explore unsupervised techniques for the task of automatic short answer grading. I 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. I continue to combine graph alignment features with lexical semantic similarity measures and employ machine learning techniques to show that grade assignment error can be reduced compared to a system that considers only lexical semantic measures of similarity. I also detail ... continued below

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Mohler, Michael A.G. August 2012.

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  • Mohler, Michael A.G.

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Description

In this dissertation, I explore unsupervised techniques for the task of automatic short answer grading. I 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. I continue to combine graph alignment features with lexical semantic similarity measures and employ machine learning techniques to show that grade assignment error can be reduced compared to a system that considers only lexical semantic measures of similarity. I also detail a preliminary attempt to align the dependency graphs of student and instructor answers in order to utilize a structural component that is necessary to simulate human-level grading of student answers. I further explore the utility of these techniques to several related tasks in natural language processing including the detection of text similarity, paraphrase, and textual entailment.

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  • August 2012

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  • March 4, 2013, 2:02 p.m.

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  • Nov. 16, 2016, 12:05 p.m.

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Mohler, Michael A.G. Sentence Similarity Analysis with Applications in Automatic Short Answer Grading, dissertation, August 2012; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc149640/: accessed November 18, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .