Title :
Short Text Classification Using Wikipedia Concept Based Document Representation
Author :
Xiang Wang ; Ruhua Chen ; Yan Jia ; Bin Zhou
Author_Institution :
Sch. of Comput., Nat. Univ. of Defense Technol., Changsha, China
Abstract :
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, we 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 our 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), our method can be easily implemented with low cost.
Keywords :
Web sites; information retrieval; pattern classification; text analysis; Google search snippets; SVM; information retrieval systems; multidimensional text data; short document text data mapping; short text classification; sparse text data; text categorization; wikipedia concept document representation; Electronic publishing; Encyclopedias; Indexes; Internet; Support vector machines; Text categorization; Document Representation; Short Text Classification; Wikipedia;
Conference_Titel :
Information Technology and Applications (ITA), 2013 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4799-2876-7
DOI :
10.1109/ITA.2013.114