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dc.contributor.authorSauban, Maximilien
dc.contributor.authorPfahringer, Bernhard
dc.coverage.spatialConference held at Cavtat-Dubrovnik, Croatiaen_NZ
dc.identifier.citationSauban, M. & Pfahringer, B. (2003). Text categorisation using document profiling. In N. Lavrac et al. (Eds), Proceedings of 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Cavtat-Dubrovnik, Croatia, September 22-26, 2003(pp.411-422). Berlin: Springer.
dc.description.abstractThis paper presents an extension of prior work by Michael D. Lee on psychologically plausible text categorisation. Our approach utilises Lee s model as a pre-processing filter to generate a dense representation for a given text document (a document profile) and passes that on to an arbitrary standard propositional learning algorithm. Similarly to standard feature selection for text classification, the dimensionality of instances is drastically reduced this way, which in turn greatly lowers the computational load for the subsequent learning algorithm. The filter itself is very fast as well, as it basically is just an interesting variant of Naive Bayes. We present different variations of the filter and conduct an evaluation against the Reuters-21578 collection that shows performance comparable to previously published results on that collection, but at a lower computational cost.en_US
dc.publisherSpringer, Berlinen_US
dc.sourcePKDD 2003en_NZ
dc.subjectcomputer scienceen_US
dc.subjectdocument profilingen_US
dc.subjecttext classificationen_US
dc.subjectMachine learning
dc.titleText categorisation using document profilingen_US
dc.typeConference Contributionen_US
dc.relation.isPartOf7th European Conference on Principles and Practice of Knowledge Discovery in Databasesen_NZ
pubs.volumeLNCS 2838en_NZ

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