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

Thumbnail image of item number 1 in: 'Text-to-text Semantic Similarity for Automatic Short Answer Grading'.
Thumbnail image of item number 2 in: 'Text-to-text Semantic Similarity for Automatic Short Answer Grading'.
Thumbnail image of item number 3 in: 'Text-to-text Semantic Similarity for Automatic Short Answer Grading'.
Thumbnail image of item number 4 in: 'Text-to-text Semantic Similarity for Automatic Short Answer Grading'.
Showing 1-4 of 9 pages in this paper.

PDF Version Also Available for Download.

Description

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

Physical Description

9 p.

Creation Information

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

Context

This paper is part of the collection entitled: UNT Scholarly Works and was provided by the UNT College of Engineering to the UNT Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 778 times. More information about this paper can be viewed below.

Who

People and organizations associated with either the creation of this paper or its content.

Authors

Organizer of meeting

Publisher

Provided By

UNT College of Engineering

The UNT College of Engineering strives to educate and train engineers and technologists who have the vision to recognize and solve the problems of society. The college comprises six degree-granting departments of instruction and research.

About | Browse this Partner

Contact Us

Corrections Questions

What

Descriptive information to help identify this paper. Follow the links below to find similar items on the Digital Library.

Degree Information

Description

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

Physical Description

9 p.

Notes

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.

Source

  • European Chapter of the Association for Computational Linguistics (EACL), March 30-April 3, 2009. Athens, Greece

Language

Item Type

Identifier

Unique identifying numbers for this paper in the Digital Library or other systems.

Collections

This paper is part of the following collection of related materials.

UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

About | Browse this Collection

What responsibilities do I have when using this paper?

Digital Files

When

Dates and time periods associated with this paper.

Creation Date

  • March 2009

Added to The UNT Digital Library

  • Jan. 31, 2011, 2:01 p.m.

Description Last Updated

  • Nov. 30, 2023, 11:28 a.m.

Usage Statistics

When was this paper last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 778
More Statistics

Interact With This Paper

Here are some suggestions for what to do next.

Start Reading

Thumbnail image of item number 1 in: 'Text-to-text Semantic Similarity for Automatic Short Answer Grading'.
Thumbnail image of item number 2 in: 'Text-to-text Semantic Similarity for Automatic Short Answer Grading'.
Thumbnail image of item number 3 in: 'Text-to-text Semantic Similarity for Automatic Short Answer Grading'.
Thumbnail image of item number 4 in: 'Text-to-text Semantic Similarity for Automatic Short Answer Grading'.

PDF Version Also Available for Download.

Citations, Rights, Re-Use

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

Links for Robots

Helpful links in machine-readable formats.

Archival Resource Key (ARK)

International Image Interoperability Framework (IIIF)

Metadata Formats

Images

URLs

Stats

Mohler, Michael & Mihalcea, Rada, 1974-. Text-to-text Semantic Similarity for Automatic Short Answer Grading, paper, March 2009; [Stroudsburg, Pennsylvania]. (https://digital.library.unt.edu/ark:/67531/metadc31017/: accessed September 11, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Engineering.

Back to Top of Screen