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A Heuristic Approach for Topical Information Extraction from News Pages

  • Yan Liu
  • Qiang Wang
  • QingXian Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4255)

Abstract

Topical information extraction from news pages could facilitate news searching and retrieval etc. A web page could be partitioned into multiple blocks. The importance of different blocks varies from each other. The estimation of the block importance could be defined as a classification problem. First, an adaptive vision-based page segmentation algorithm is used to partition a web page into semantic blocks. Then spatial features and content features are used to represent each block. Shannon’s information entropy is adopted to represent each feature’s ability for differentiating. A weighted Naïve Bayes classifier is used to estimate whether the block is important or not. Finally, a variation of TF-IDF is utilized to represent weight of each keyword. As a result, the similar blocks are united as topical region. The approach is tested with several important English and Chinese news sites. Both recall and precision rates are greater than 96%.

Keywords

Information Retrieval Entropy Naive Bayes classifier 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yan Liu
    • 1
  • Qiang Wang
    • 1
  • QingXian Wang
    • 1
  1. 1.Information Engineering InstituteInformation Engineering UniversityZhengzhouP.R. China

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