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Visual webpage block importance prediction using conditional random fields.

We have developed a system that segments web pages into blocks and predicts those blocks' importance (block importance prediction or BIP). First, we use VIPS to partition a page into a tree composed of blocks and then extracts features from each block and labels all leaf nodes. This paper makes two...

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Publicado en:Journal of the American Society for Information Science & Technology Vol. 62; no. 11; pp. 2225 - 2236
Autores principales: Tzong-Han Tsai, Richard, Chiu, Borong, Wu, Chi-En
Formato: pictorial research tables/charts Journal Article
Publicado: Wiley-Blackwell Nov2011
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Nov2011
      vid: 62
      iid: 11
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        104703162
        66607208
        10.1002/asi.21605
        104703162
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        atl: Visual webpage block importance prediction using conditional random fields.
      aug:
        au:
          Tzong-Han Tsai, Richard
          Chiu, Borong
          Wu, Chi-En
        affil: Department of Computer Science and Engineering, Yuan-Ze University, 135 Yuan-Tung Road, Chungli, Taoyuan, Taiwan
      sug:
        subj:
          World Wide Web Applications
          Computers, Hand-Held
          Wireless Communications Equipment and Supplies
          Data Display
          Evaluation Research
          Models, Theoretical
          Information Retrieval
      ab: We have developed a system that segments web pages into blocks and predicts those blocks' importance (block importance prediction or BIP). First, we use VIPS to partition a page into a tree composed of blocks and then extracts features from each block and labels all leaf nodes. This paper makes two main contributions. Firstly, we are pioneering the formulation of BIP as a sequence tagging task. We employ DFS, which outputs a single sequence for the whole tree in which related sub-blocks are adjacent. Our second contribution is using the conditional random fields (CRF) model for labeling these sequences. CRF's transition features model correlations between neighboring labels well, and CRF can simultaneously label all blocks in a sequence to find the global optimal solution for the whole sequence, not only the best solution for each block. In our experiments, our CRF-based system achieves an F1-measure of 97.41%, which significantly outperforms our ME-based baseline (95.64%). Lastly, we tested the CRF-based system using sites which were not covered in the training data. On completely novel sites CRF performed slightly worse than ME. However, when given only two training pages from a given site, CRF improved almost three times as much as ME.
      pubtype: Academic Journal
      doctype:
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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