SCESS: a WFSA-based automated simplified chinese essay scoring system with incremental latent semantic analysis
Hao, Shudong1; Xu, Yanyan1; Ke, Dengfeng2; Su, Kaile3; Peng, Hengli4
2016-03-01
发表期刊NATURAL LANGUAGE ENGINEERING
卷号22期号:2页码:291-319
文章类型Article
摘要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%.
关键词Automatic Essay Scoring Latent Semantic Analysis
WOS标题词Science & Technology ; Social Sciences ; Technology
DOI10.1017/S1351324914000138
关键词[WOS]SEGMENTATION ; ALGORITHM ; ASTERISK
收录类别SCI ; SSCi
语种英语
项目资助者Beijing Higher Education Young Elite Teacher Project(YETP0768) ; Fundamental Research Funds for the Central Universities(YX2014-18) ; National Natural Science Foundation of China(61103152 ; 61472369)
WOS研究方向Computer Science ; Linguistics
WOS类目Computer Science, Artificial Intelligence ; Linguistics ; Language & Linguistics
WOS记录号WOS:000370862900005
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/11351
专题数字内容技术与服务研究中心_听觉模型与认知计算
作者单位1.Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
3.Griffith Univ, Inst Integrated & Intelligent Syst, Brisbane, Qld 4111, Australia
4.Beijing Language & Culture Univ, Inst Educ Measurement, Beijing, Peoples R China
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GB/T 7714
Hao, Shudong,Xu, Yanyan,Ke, Dengfeng,et al. SCESS: a WFSA-based automated simplified chinese essay scoring system with incremental latent semantic analysis[J]. NATURAL LANGUAGE ENGINEERING,2016,22(2):291-319.
APA Hao, Shudong,Xu, Yanyan,Ke, Dengfeng,Su, Kaile,&Peng, Hengli.(2016).SCESS: a WFSA-based automated simplified chinese essay scoring system with incremental latent semantic analysis.NATURAL LANGUAGE ENGINEERING,22(2),291-319.
MLA Hao, Shudong,et al."SCESS: a WFSA-based automated simplified chinese essay scoring system with incremental latent semantic analysis".NATURAL LANGUAGE ENGINEERING 22.2(2016):291-319.
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