Improving Text Categorization with Semantic Knowledge in Wikipedia

Xiang WANG
Yan JIA
Ruhua CHEN
Hua FAN
Bin ZHOU

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E96-D    No.12    pp.2786-2794
Publication Date: 2013/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.2786
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
Keyword: 
text categorization,  Wikipedia,  document representation,  semantic matrix,  

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Summary: 
Text categorization, especially short text categorization, is a difficult and challenging task since the text data is sparse and multidimensional. In traditional text classification methods, document texts are represented with “Bag of Words (BOW)” text representation schema, which is based on word co-occurrence and has many limitations. In this paper, we mapped document texts to Wikipedia concepts and used the Wikipedia-concept-based document representation method to take the place of traditional BOW model for text classification. In order to overcome the weakness of ignoring the semantic relationships among terms in document representation model and utilize rich semantic knowledge in Wikipedia, we constructed a semantic matrix to enrich Wikipedia-concept-based document representation. Experimental evaluation on five real datasets of long and short text shows that our approach outperforms the traditional BOW method.


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