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dc.contributor.authorXu, Yanyan
dc.contributor.authorKe, Dengfeng
dc.contributor.authorSu, Kaile
dc.date.accessioned2017-08-15T02:30:34Z
dc.date.available2017-08-15T02:30:34Z
dc.date.issued2017
dc.identifier.issn0334-1860
dc.identifier.doi10.1515/jisys-2015-0048
dc.identifier.urihttp://hdl.handle.net/10072/344157
dc.description.abstractThe 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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherWalter de Gruyter
dc.relation.ispartofpagefrom263
dc.relation.ispartofpageto285
dc.relation.ispartofissue2
dc.relation.ispartofjournalJournal of Intelligent Systems
dc.relation.ispartofvolume26
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchInformation Systems
dc.subject.fieldofresearchCognitive Sciences
dc.subject.fieldofresearchcode080199
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0806
dc.subject.fieldofresearchcode1702
dc.titleContextualized Latent Semantic Indexing: A New Approach to Automated Chinese Essay Scoring
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 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.
gro.hasfulltextFull Text
gro.griffith.authorSu, Kaile


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