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Text categorisation using document profiling

Abstract
This 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.
Type
Conference Contribution
Type of thesis
Series
Citation
Sauban, 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.
Date
2003
Publisher
Springer, Berlin
Degree
Supervisors
Rights