Wikipedia-Based Kernels for Text Categorization

10 years 8 months ago
Wikipedia-Based Kernels for Text Categorization
In recent years several models have been proposed for text categorization. Within this, one of the widely applied models is the vector space model (VSM), where independence between indexing terms, usually words, is assumed. Since training corpora sizes are relatively small – compared to ≈ ∞ what would be required for a realistic number of words – the generalization power of the learning algorithms is low. It is assumed that a bigger text corpus can boost the representation and hence the learning process. Based on the work of Gabrilovich and Markovitch [6], we incorporate Wikipedia articles into the system to give word distributional representation for documents. The extension with this new corpus causes dimensionality increase, therefore clustering of features is needed. We use Latent Semantic Analysis (LSA), Kernel Principal Component Analysis (KPCA) and Kernel Canonical Correlation Analysis (KCCA) and present results for these experiments on the Reuters corpus.
Zsolt Minier, Zalan Bodo, Lehel Csató
Added 04 Jun 2010
Updated 04 Jun 2010
Type Conference
Year 2007
Authors Zsolt Minier, Zalan Bodo, Lehel Csató
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