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Automatic classification of teaching and learning materials based on standard education classification schemes

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dc.contributor.author Joorabchi, Arash
dc.contributor.author Mahdi, Abdulhussain E.
dc.date.accessioned 2016-07-25T14:35:50Z
dc.date.available 2016-07-25T14:35:50Z
dc.date.issued 2008
dc.identifier.uri http://hdl.handle.net/10344/5136
dc.description peer-reviewed en_US
dc.description.abstract With the significant growth in available electronic education materials such as syllabus documents and lecture notes on the Internet and intranets there is a need for developing efficient indexing/categorizing mechanisms to organize such E-documents in institutional repositories. In this paper we describe our approach for automatic classification of syllabus documents in the national Irish syllabus repository. The classifier software component is based on the well-known naïve Bayes classification algorithm. We have also studied the application of a web corpus in training an unsupervised classifier which eliminates the need for manual classification of a training set required in standard classifications systems en_US
dc.language.iso eng en_US
dc.relation.ispartof Cranfield Multi-Strnad Conference ¿ CMC 2008 en
dc.relation.ispartofseries Cranfield Multi-Strand Conference (CMC);
dc.subject electronic education materials en_US
dc.subject E-documents en_US
dc.subject organizing en_US
dc.title Automatic classification of teaching and learning materials based on standard education classification schemes en_US
dc.type info:eu-repo/semantics/conferenceObject en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.date.updated 2016-07-25T14:31:38Z
dc.description.version PUBLISHED
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1452474
dc.internal.copyrightchecked Yes
dc.description.status non-peer-reviewed


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