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Extracting Information from Semistructured Data

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Advances in Web-Age Information Management (WAIM 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2419))

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Abstract

This paper describes work towards automatically building on-line structured information resources from information sources that are comprised largely of natural language but with some structuring conventions. Such conversion requires two phases: region identification of the incoming documents, and mapping the information they contain into a more structured form. We describe a system that uses decision-tree-based machine learning techniques to build a classifier that can accurately identify document regions and discuss pattern-discovery methods for extracting information from the identified regions. Experiments demonstrate that this approach works well.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, The University of New South Wales, Australia

    Liping Ma & John Shepherd

  2. School of Computer Science, University of Tasmania, Australia

    Yanchun Zhang

Authors
  1. Liping Ma
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  2. John Shepherd
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  3. Yanchun Zhang
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Editor information

Editors and Affiliations

  1. Information School, Renmin University of China, Beijing, 100872, China

    Xiaofeng Meng

  2. Department of Computer Science, University of California, Santa Barbara, CA, 93106-5110, USA

    Jianwen Su  & Yujun Wang  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Ma, L., Shepherd, J., Zhang, Y. (2002). Extracting Information from Semistructured Data. In: Meng, X., Su, J., Wang, Y. (eds) Advances in Web-Age Information Management. WAIM 2002. Lecture Notes in Computer Science, vol 2419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45703-8_13

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  • DOI: https://doi.org/10.1007/3-540-45703-8_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44045-1

  • Online ISBN: 978-3-540-45703-9

  • eBook Packages: Springer Book Archive

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