Leveraging Visual Features and Hierarchical Dependencies for Conference Information Extraction

  • Yue You
  • Guandong Xu
  • Jian Cao
  • Yanchun Zhang
  • Guangyan Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7808)


Traditional information extraction methods mainly rely on visual feature assisted techniques; but without considering the hierarchical dependencies within the paragraph structure, some important information is missing. This paper proposes an integrated approach for extracting academic information from conference Web pages. Firstly, Web pages are segmented into text blocks by applying a new hybrid page segmentation algorithm which combines visual feature and DOM structure together. Then, these text blocks are labeled by a Tree-structured Random Fields model, and the block functions are differentiated using various features such as visual features, semantic features and hierarchical dependencies. Finally, an additional post-processing is introduced to tune the initial annotation results. Our experimental results on real-world data sets demonstrated that the proposed method is able to effectively and accurately extract the needed academic information from conference Web pages.


Information Extraction Visual Feature DOM Structure Tree-structured Conditional Random Fields 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yue You
    • 1
    • 2
  • Guandong Xu
    • 3
  • Jian Cao
    • 1
  • Yanchun Zhang
    • 2
    • 4
  • Guangyan Huang
    • 2
  1. 1.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityChina
  2. 2.Centre for Applied InformaticsVictoria UniversityAustralia
  3. 3.Advanced Analytics InstituteUniversity of Technology SydneyAustralia
  4. 4.University of Chinese Academy of SciencesChina

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