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

Description:

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

Creator(s):
Creation Date: March 2009
Partner(s):
UNT College of Engineering
Collection(s):
UNT Scholarly Works
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Total Uses: 76
Past 30 days: 10
Yesterday: 1
Creator (Author):
Mohler, Michael

University of North Texas

Creator (Author):
Mihalcea, Rada, 1974-

University of North Texas

Publisher Info:
Place of Publication: [Stroudsburg, Pennsylvania]
Date(s):
  • Creation: March 2009
Description:

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

Degree:
Note:

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.

Physical Description:

9 p.

Language(s):
Subject(s):
Keyword(s): short answer grading | text similarities | computer-assisted assessment | annotated corpus
Source: European Chapter of the Association for Computational Linguistics (EACL), 2009, Athens, Greece
Contributor(s):
Partner:
UNT College of Engineering
Collection:
UNT Scholarly Works
Identifier:
  • ARK: ark:/67531/metadc31017
Resource Type: Paper
Format: Text
Rights:
Access: Public