Abstract
It is known that Support Vector Machines (SVM) provide a fast and powerful means for the classification of documents. In this paper we examine how different representations of documents effect the performance of SVM in different text classification tasks. We discuss the role of importance-weights (inverse document frequency and redundancy) and we show that time consuming lemmatization can be avoided even when classifying a highly inflectional language like German.
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© 2002 Springer-Verlag Berlin Heidelberg
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Kindermann, J., Leopold, E. (2002). Classification of Texts with Support Vector Machines: An Examination of the Efficiency of Kernels and Data-transformations. In: Gaul, W., Ritter, G. (eds) Classification, Automation, and New Media. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55991-4_20
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DOI: https://doi.org/10.1007/978-3-642-55991-4_20
Publisher Name: Springer, Berlin, Heidelberg
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