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A novel approach for Web page modeling in personal information extraction

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Abstract

The target of personal information extraction (PIE) is to extract content associated with a name form Web pages. Available Web page models, which are also used widely in content extraction and automatic wrapper algorithms, include text model, document object model, and vision-based page segmentation model. Because of existing models focus on Web structure rather than semantic relevance, they are difficult to be directly used for PIE. To deal with this problem, we introduce the sequence block model (SBM), by which is easy to determine the relevance of each page block to the retrieval name. Then, we give the definition of PIE based on the SBM. Depending on the sequence correlation of SBM, we design a 4-layer seq2seq deep learning network for PIE. Experiment result shows that our new model extracts twice as much data as content extraction algorithms. And the recall rate of the network is 7% higher than the traditional model with classification algorithm.

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Acknowledgements

This work is partially supported by National Key Research and Development Program of China (No. 2016YFB0800802) and Shandong Key Research and Development Plan under grant (No.2016ZDJS01A04 and No.2017CXGC0706).

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Authors and Affiliations

  1. Harbin Institute of Technology, Weihai, Shandong, People’s Republic of China

    Wei Yuliang, Zhou Qi, Lv Fang, Han Xixian, Xin Guodong & Wang Bailing

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  1. Wei Yuliang
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  2. Zhou Qi
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  3. Lv Fang
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  4. Han Xixian
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  5. Xin Guodong
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Correspondence to Wang Bailing.

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This article belongs to the Topical Collection: Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications

Guest Editors: Jingkuan Song, Shuqiang Jiang, Elisa Ricci, and Zi Huang

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Yuliang, W., Qi, Z., Fang, L. et al. A novel approach for Web page modeling in personal information extraction. World Wide Web 22, 603–620 (2019). https://doi.org/10.1007/s11280-018-0631-9

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  • DOI: https://doi.org/10.1007/s11280-018-0631-9

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