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Sentence Similarity Analysis with Applications in Automatic Short Answer Grading

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.

Identiferoai:union.ndltd.org:unt.edu/info:ark/67531/metadc149640
Date08 1900
CreatorsMohler, Michael A. G.
ContributorsMihalcea, Rada, 1974-, Bunescu, Răzvan, Tarau, Paul, Ruiz, Miguel E.
PublisherUniversity of North Texas
Source SetsUniversity of North Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
FormatText
RightsPublic, Mohler, Michael A.G., Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved.

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