Automated Chinese Essay Scoring from Topic Perspective Using Regularized Latent Semantic Indexing
File version
Author(s)
Xu, Yanyan
Peng, Hengli
Su, Kaile
Ke, Dengfeng
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Anders Heyden, Denis Laurendeau
Date
Size
File type(s)
Location
Stockholm, Sweden
License
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%.
Journal Title
Conference Title
Pattern Recognition (ICPR), 2014 22nd International Conference on
Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject
Natural language processing