Using IR techniques to improve Automated Text Classification

dc.contributor.authorGonçalves, Teresa
dc.contributor.authorQuaresma, Paulo
dc.date.accessioned2011-02-15T10:54:06Z
dc.date.available2011-02-15T10:54:06Z
dc.date.issued2004
dc.description.abstractThis paper performs a study on the pre-processing phase of the automated text classification problem. We use the linear Support Vector Machine paradigm applied to datasets written in the English and the European Portuguese languages – the Reuters and the Portuguese Attorney General’s Office datasets, respectively. The study can be seen as a search, for the best document representa- tion, in three different axes: the feature reduction (using linguistic in- formation), the feature selection (using word frequencies) and the term weighting (using information retrieval measures).en
dc.format.extent129335 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.accesstypelivreen
dc.identifier.authoremailtcg@uevora.pt
dc.identifier.authoremailpq@uevora.pt
dc.identifier.editorpersonMeziane, F.
dc.identifier.editorpersonMetais, E.
dc.identifier.numrev3136en
dc.identifier.pagina374-379en
dc.identifier.principalpublicationtitleNLDB-04, Natural Language Processing and Information Systemsen
dc.identifier.revistaLecture Notes in Computer Scienceen
dc.identifier.scientificarea498en
dc.identifier.urihttp://hdl.handle.net/10174/2557
dc.language.isoeng
dc.peerreviewednoen
dc.publisherSpringer-Verlagen
dc.rightsopenAccessen
dc.subjectmachine learningen
dc.subjectText classificationen
dc.titleUsing IR techniques to improve Automated Text Classificationen
dc.typearticleen

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