Title :
An Effective Automated Essay Scoring System Using Support Vector Regression
Author :
Li, Yali ; Yan, Yonghong
Author_Institution :
Key Lab. of Speech Acoust. & Content Understanding, Beijing, China
Abstract :
In this paper, we introduce an effective automated essay scoring system. To implement the system, we extract several features, including the surface features such as the number of words in the essay, number of words longer than 5, and complex features such as grammar checking, sentences, whether the essay is off-topic, the similarity to full-score essays. We get the result of 86% precision given the two scores deviation and average deviation of 0.88 compared to human score on real CET4 data.
Keywords :
educational administrative data processing; regression analysis; support vector machines; complex features; effective automated essay scoring system; feature extraction; grammar checking; sentences; support vector regression; surface features; Feature extraction; Grammar; Humans; Mutual information; Speech; Vectors; CET4; automated essay scoring;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2012 Fifth International Conference on
Conference_Location :
Zhangjiajie, Hunan
Print_ISBN :
978-1-4673-0470-2
DOI :
10.1109/ICICTA.2012.23
