Difference between revisions of "Short Text Classification Using Wikipedia Concept based Document Representation"
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| − | '''Short Text Classification Using Wikipedia Concept based Document Representation''' - scientific work related to Wikipedia quality published in 2013, written by Xiang Wang, Ruhua Chen, Yan Jia and Bin Zhou. | + | '''Short Text Classification Using Wikipedia Concept based Document Representation''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Xiang Wang]], [[Ruhua Chen]], [[Yan Jia]] and [[Bin Zhou]]. |
== Overview == | == Overview == | ||
| − | Short text classification is a difficult and challenging task in information retrieval systems since the text data is short, sparse and multidimensional. In this paper, authors represent short text with Wikipedia concepts for classification. Short document text is mapped to Wikipedia concepts and the concepts are then used to represent document for text categorization. Traditional methods for classification such as SVM can be used to perform text categorization on the Wikipedia concept document representation. Experimental evaluation on real Google search snippets shows that approach outperforms the traditional BOW method and gives good performance. Although it's not better than the state-of-the-art classifier (see e.g. Phan et al. WWW '08), method can be easily implemented with low cost. | + | Short text classification is a difficult and challenging task in [[information retrieval]] systems since the text data is short, sparse and multidimensional. In this paper, authors represent short text with [[Wikipedia]] concepts for classification. Short document text is mapped to Wikipedia concepts and the concepts are then used to represent document for text categorization. Traditional methods for classification such as SVM can be used to perform text categorization on the Wikipedia concept document representation. Experimental evaluation on real [[Google]] search snippets shows that approach outperforms the traditional BOW method and gives good performance. Although it's not better than the state-of-the-art classifier (see e.g. Phan et al. WWW '08), method can be easily implemented with low cost. |
Revision as of 22:50, 10 March 2021
Short Text Classification Using Wikipedia Concept based Document Representation - scientific work related to Wikipedia quality published in 2013, written by Xiang Wang, Ruhua Chen, Yan Jia and Bin Zhou.
Overview
Short text classification is a difficult and challenging task in information retrieval systems since the text data is short, sparse and multidimensional. In this paper, authors represent short text with Wikipedia concepts for classification. Short document text is mapped to Wikipedia concepts and the concepts are then used to represent document for text categorization. Traditional methods for classification such as SVM can be used to perform text categorization on the Wikipedia concept document representation. Experimental evaluation on real Google search snippets shows that approach outperforms the traditional BOW method and gives good performance. Although it's not better than the state-of-the-art classifier (see e.g. Phan et al. WWW '08), method can be easily implemented with low cost.
