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Contextualized Latent Semantic Indexing: A New Approach to Automated Chinese Essay Scoring
Yanyan Xu; Dengfeng Ke; Kaile Su
Source PublicationJournal of Intelligent Systems
2017
Issue26Pages:263-285
Abstract
The writing part in Chinese language tests is badly in need of a mature automated essay scoring system. In this paper, we propose a new approach applied to automated Chinese essay scoring (ACES), called contextualized latent semantic indexing (CLSI), of which Genuine CLSI and Modified CLSI are two versions. Then-gram language model and the weighted finite-state transducer (WFST), two critical components, are used to extract context information in our ACES system. Not only does CLSI improve conventional latent semantic indexing (LSI), but bridges the gap between latent semantics and their context information, which is absent in LSI. Moreover, CLSI can score essays from the perspectives of language fluency and contents, and address the local overrating and underrating problems caused by LSI. Experimental results show that CLSI outperforms LSI, Regularized LSI, and latent Dirichlet allocation in many aspects, and thus, proves to be an effective approach.
KeywordAutomated Chinese Essay Scoring Latent Semantic Indexing N-gram Language Model Weighted Finite-state Transducer Natural Language Processing
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20202
Collection模式识别国家重点实验室_语音交互
Recommended Citation
GB/T 7714
Yanyan Xu,Dengfeng Ke,Kaile Su. Contextualized Latent Semantic Indexing: A New Approach to Automated Chinese Essay Scoring[J]. Journal of Intelligent Systems,2017(26):263-285.
APA Yanyan Xu,Dengfeng Ke,&Kaile Su.(2017).Contextualized Latent Semantic Indexing: A New Approach to Automated Chinese Essay Scoring.Journal of Intelligent Systems(26),263-285.
MLA Yanyan Xu,et al."Contextualized Latent Semantic Indexing: A New Approach to Automated Chinese Essay Scoring".Journal of Intelligent Systems .26(2017):263-285.
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