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A probabilistic model with multi-dimensional features for object extraction

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Abstract

To identify recruitment information in different domains, we propose a novel model of hierarchical tree-structured conditional random fields (HT-CRFs). In our approach, first, the concept of a Web object (WOB) is discussed for the description of special Web information. Second, in contrast to traditional methods, the Boolean model and multi-rule are introduced to denote a one-dimensional text feature for a better representation of Web objects. Furthermore, a two-dimensional semantic texture feature is developed to discover the layout of a WOB, which can emphasize the structural attributes and the specific semantics term attributes of WOBs. Third, an optimal WOB information extraction (IE) based on HT-CRF is performed, addressing the problem of a model having an excessive dependence on the page structure and optimizing the efficiency of the model’s training. Finally, we compare the proposed model with existing decoupled approaches for WOB IE. The experimental results show that the accuracy rate of WOB IE is significantly improved and that time complexity is reduced.

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Jing Wang & Zhijing Liu

  2. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Hui Zhao

Authors
  1. Jing Wang
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  2. Zhijing Liu
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  3. Hui Zhao
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Corresponding author

Correspondence to Jing Wang.

Additional information

Jing Wang is a lecturer in the School of Computer Science and Technology, Xidian University, China. She Received her PhD, MS, and BS in Computer Science from Xidian University in 2011, 2008, and 2003, respectively. Her research interests are data mining and machine learning.

Zhijing Liu, is a professor and advisor for doctoral students. He graduated from the Department of Computer Engineering of Northwestern Telecommunications Engineering Institute in 1982. He currently serves as the head of the Research Center of Computer Information Research Application, and is the director of the China and America Associated Laboratory of key Technologies of Mobile Electronic Commerce. His research works focus on the fields of data mining and vision computing.

Hui Zhao is a PhD candidate at the School of Electronic and Information Engineering, Xi’an Jiaotong University. He Received his BS and MS from Xidian University in 2008. His research interests include data mining, information retrieval, and mobile learning.

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Wang, J., Liu, Z. & Zhao, H. A probabilistic model with multi-dimensional features for object extraction. Front. Comput. Sci. 6, 513–526 (2012). https://doi.org/10.1007/s11704-012-1093-3

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