Automated essay scoring using set of literary sememes

Tao Hsing Chang*, Chia Hoang Lee, Pei Yen Tsai, Hak Ping Tam

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Automatic essay scoring system is a very important research tool for many educational studies. Many researches indicate that AES systems should be able to analyze semantic characteristics of an essay and include more such features to score essays. This paper makes an assumption: some concepts that can be regarded as literary concepts would only be utilized by skillful writers. However, it is a difficult task to extract literal concepts due to small size of training corpora. This work uses a semantic network tool to overcome the problem. The concepts in essays can be transformed into sememes using the tool and literary concepts are also transformed into literary sememes. This work introduces a method which makes use of the literary sememes in an essay to score the essay. Experimental results show that the accuracy of the proposed method for Chinese essays is comparable to those as achieved by several current English AES systems.

Original languageEnglish
Title of host publication2008 International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2008
DOIs
Publication statusPublished - 2008
Event2008 International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2008 - Beijing, China
Duration: 2008 Oct 192008 Oct 22

Publication series

Name2008 International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2008

Conference

Conference2008 International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2008
Country/TerritoryChina
CityBeijing
Period2008/10/192008/10/22

Keywords

  • Chinese writings
  • Essay scoring
  • Literary sememes
  • Semantic analysis
  • Writing grading

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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