Bootstrapping Information Extraction from Semi-structured Web Pages

  • Authors:

  • Andrew Carlson;Charles Schafer

  • Affiliations:

  • Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA PA 15213;Google, Inc., Pittsburgh, USA PA 15213

  • Venue:
  • ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
  • Year:
  • 2008

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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.