News Article Classification Based on a Vector Representation Including Words’ Collocations

Please use this identifier to cite or link to this item: http://hdl.handle.net/10506/696

Title:	News Article Classification Based on a Vector Representation Including Words’ Collocations
Authors:	Kompan, Michal Bieliková, Mária
Keywords:	text pre-processing news recommendation news classification vector representation
Issue Date:	2011
Publisher:	Springer-Verlag Berlin Heidelberg 2011
Citation:	Kompan, Michal, Bieliková, Mária (2011), News Article Classification Based on a Vector Representation Including Words’ Collocations, Advances in Intelligent and Soft Computing, 2011, Volume 101, Third International Conference on Software, Services and Semantic Technologies S3T 2011, September 1-3, 2011, Bourgas, Bulgaria, ISBN 978-3-642-23162-9, pp. 1-8
Abstract:	In this paper we present a proposal including collocations into the pre-processing of the text mining, which we use for the fast news article recommendation and experiments based on real data from the biggest Slovak newspaper. The news article section can be predicted based on several article’s characteristics as article name, content, keywords etc. We provided experiments aimed at comparison of several approaches and algorithms including expressive vector representation, with considering most popular words collocations obtained from Slovak National Corpus.
URI:	http://hdl.handle.net/10506/696
ISBN:	978-3-642-23162-9
ISSN:	1867-5662
Appears in Collections:	S3T 2011

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