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.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc149640 |
| Date | 08 1900 |
| Creators | Mohler, Michael A. G. |
| Contributors | Mihalcea, Rada, 1974-, Bunescu, Răzvan, Tarau, Paul, Ruiz, Miguel E. |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Mohler, Michael A.G., Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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