Wikipedia-Based Kernels for Text Categorization

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Wikipedia-Based Kernels for Text Categorization
Authors
Zsolt Minier
Zalán Bodó
Lehel Csató
Publication date
2007
DOI
10.1109/SYNASC.2007.8
Links
Original

Wikipedia-Based Kernels for Text Categorization - scientific work related to Wikipedia quality published in 2007, written by Zsolt Minier, Zalán Bodó and Lehel Csató.

Overview

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 ap infin 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], authors 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. Authors 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.

Embed

Wikipedia Quality

Minier, Zsolt; Bodó, Zalán; Csató, Lehel. (2007). "[[Wikipedia-Based Kernels for Text Categorization]]".DOI: 10.1109/SYNASC.2007.8.

English Wikipedia

{{cite journal |last1=Minier |first1=Zsolt |last2=Bodó |first2=Zalán |last3=Csató |first3=Lehel |title=Wikipedia-Based Kernels for Text Categorization |date=2007 |doi=10.1109/SYNASC.2007.8 |url=https://wikipediaquality.com/wiki/Wikipedia-Based_Kernels_for_Text_Categorization}}

HTML

Minier, Zsolt; Bodó, Zalán; Csató, Lehel. (2007). &quot;<a href="https://wikipediaquality.com/wiki/Wikipedia-Based_Kernels_for_Text_Categorization">Wikipedia-Based Kernels for Text Categorization</a>&quot;.DOI: 10.1109/SYNASC.2007.8.