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dc.contributor.authorSymeonidis P
dc.contributor.authorKehayov I
dc.contributor.authorManolopoulos Y
dc.contributor.editor
dc.date.accessioned2019-03-08T08:24:26Z
dc.date.available2019-03-08T08:24:26Z
dc.date.issued2012
dc.identifier.isbn978-3-642-33073-5
dc.identifier.issn0302-9743
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-642-33074-2_29
dc.identifier.urihttp://link.springer.com/chapter/10.1007/978-3-642-33074-2_29
dc.identifier.urihttp://hdl.handle.net/10863/9055
dc.description.abstractText classification is a process where documents are categorized usually by topic, place, readability easiness, etc. For text classification by topic, a well-known method is Singular Value Decomposition. For text classification by readability, "Flesch Reading Ease index" calculates the readability easiness level of a document (e.g. easy, medium, advanced). In this paper, we propose Singular Value Decomposition combined either with Cosine Similarity or with Aggregated Similarity Matrices to categorize documents by readability easiness and by topic. We experimentally compare both methods with Flesch Reading Ease index, and the vector-based cosine similarity method on a synthetic and a real data set (Reuters-21578). Both methods clearly outperform all other comparison partners. © 2012 Springer-Verlag.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation16th East European Conference on Advances in Databases and Information Systems, ADBIS 2012 ; Poznan : 18.9.2012 - 21.9.2012
dc.relation.ispartofseriesLecture Notes in Computer Science;
dc.rights
dc.titleText classification by aggregation of SVD eigenvectorsen_US
dc.typeBook chapteren_US
dc.date.updated2019-03-08T03:00:48Z
dc.publication.titleAdvances in Databases and Information Systems: 16th East European Conference, ADBIS 2012, Poznań, Poland, September 18-21, 2012. Proceedings
dc.language.isiEN-GB
dc.description.fulltextopenen_US


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