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SCESS: a WFSA-based automated simplified chinese essay scoring system with incremental latent semantic analysis

Published online by Cambridge University Press:  30 October 2014

SHUDONG HAO ,
YANYAN XU ,
DENGFENG KE ,
KAILE SU  and
HENGLI PENG
SHUDONG HAO
Affiliation:
School of Information Science and Technology, Beijing Forestry University, Beijing, China email: [email protected]
YANYAN XU*
Affiliation:
School of Information Science and Technology, Beijing Forestry University, Beijing, China email: [email protected]
DENGFENG KE
Affiliation:
Institute of Automation, Chinese Academy of Sciences, Beijing, China email: [email protected]
KAILE SU
Affiliation:
Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia email: [email protected]
HENGLI PENG
Affiliation:
Institute of Educational Measurement, Beijing Language and Culture University, Beijing, China email: [email protected]
*
†Corresponding author: [email protected]
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Abstract

Writing in language tests is regarded as an important indicator for assessing language skills of test takers. As Chinese language tests become popular, scoring a large number of essays becomes a heavy and expensive task for the organizers of these tests. In the past several years, some efforts have been made to develop automated simplified Chinese essay scoring systems, reducing both costs and evaluation time. In this paper, we introduce a system called SCESS (automated Simplified Chinese Essay Scoring System) based on Weighted Finite State Automata (WFSA) and using Incremental Latent Semantic Analysis (ILSA) to deal with a large number of essays. First, SCESS uses an n-gram language model to construct a WFSA to perform text pre-processing. At this stage, the system integrates a Confusing-Character Table, a Part-Of-Speech Table, beam search and heuristic search to perform automated word segmentation and correction of essays. Experimental results show that this pre-processing procedure is effective, with a Recall Rate of 88.50%, a Detection Precision of 92.31% and a Correction Precision of 88.46%. After text pre-processing, SCESS uses ILSA to perform automated essay scoring. We have carried out experiments to compare the ILSA method with the traditional LSA method on the corpora of essays from the MHK test (the Chinese proficiency test for minorities). Experimental results indicate that ILSA has a significant advantage over LSA, in terms of both running time and memory usage. Furthermore, experimental results also show that SCESS is quite effective with a scoring performance of 89.50%.

Type
Articles
Information
Natural Language Engineering , Volume 22 , Issue 2 , March 2016 , pp. 291 - 319
Copyright
Copyright © Cambridge University Press 2014 

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