Automated chinese essay scoring from topic perspective using regularized latent semantic indexing

Shudong Hao, Yanyan Xu, Hengli Peng, Kaile Su, Dengfeng Ke

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Finding out an effective way to score Chinese written essays automatically remains challenging for researchers. Several methods have been proposed and developed but limited in the character and word usage levels. As one of the scoring standards, however, content or topic perspective is also an important and necessary indicator to assess an essay. Therefore, in this paper, we propose a novel perspective - topic, and a new method integrating topic modeling strategy called Regularized Latent Semantic Indexing to recognize the latent topics and Support Vector Machines to train the scoring model. Experimental results show that automated Chinese essay scoring from topic perspective is effective which can improve the rating agreement to 89%.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
Pages3092-3097
Number of pages6
ISBN (Electronic)9781479952083
DOIs
StatePublished - 4 Dec 2014
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference22nd International Conference on Pattern Recognition, ICPR 2014
Country/TerritorySweden
CityStockholm
Period24/08/1428/08/14

Keywords

  • Automated Chinese essay scoring
  • Classification application
  • Document understanding
  • Topic modeling application

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