Bootstrapping Information Extraction from Semi-structured Web Pages

  • Andrew Carlson
  • Charles Schafer
Conference paper

DOI: 10.1007/978-3-540-87479-9_31

Volume 5211 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Carlson A., Schafer C. (2008) Bootstrapping Information Extraction from Semi-structured Web Pages. In: Daelemans W., Goethals B., Morik K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2008. Lecture Notes in Computer Science, vol 5211. Springer, Berlin, Heidelberg

Abstract

We consider the problem of extracting structured records from semi-structured web pages with no human supervision required for each target web site. Previous work on this problem has either required significant human effort for each target site or used brittle heuristics to identify semantic data types. Our method only requires annotation for a few pages from a few sites in the target domain. Thus, after a tiny investment of human effort, our method allows automatic extraction from potentially thousands of other sites within the same domain. Our approach extends previous methods for detecting data fields in semi-structured web pages by matching those fields to domain schema columns using robust models of data values and contexts. Annotating 2–5 pages for 4–6 web sites yields an extraction accuracy of 83.8% on job offer sites and 91.1% on vacation rental sites. These results significantly outperform a baseline approach.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Andrew Carlson
    • 1
  • Charles Schafer
    • 2
  1. 1.Machine Learning DepartmentCarnegie Mellon UniversityPittsburghUSA
  2. 2.Google, Inc.PittsburghUSA