Contextualized Latent Semantic Indexing: A New Approach to Automated Chinese Essay Scoring

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Author(s)
Xu, Yanyan
Ke, Dengfeng
Su, Kaile
Griffith University Author(s)
Year published
2017
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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. The n-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 ...
View more >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. The n-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.
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View more >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. The n-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.
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Journal Title
Journal of Intelligent Systems
Volume
26
Issue
2
Copyright Statement
© 2017 Walter de Gruyter & Co. KG Publishers. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
Subject
Artificial intelligence
Cognitive and computational psychology