Improving Text Categorization with Semantic Knowledge in Wikipedia

DOI Web Site 参考文献16件
  • WANG Xiang
    School of Computer, National University of Defense Technology
  • JIA Yan
    School of Computer, National University of Defense Technology
  • CHEN Ruhua
    School of Computer, National University of Defense Technology
  • FAN Hua
    School of Computer, National University of Defense Technology
  • ZHOU Bin
    School of Computer, National University of Defense Technology

説明

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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