Learning Page-Independent Heuristics for Extracting Data from Web Pages (1999) [32 citations — 4 self]
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author = {William W. Cohen and Wei Fan},
title = {Learning Page-Independent Heuristics for Extracting Data from Web Pages},
booktitle = {In AAAI Spring Symposium on Intelligent Agents in Cyberspace},
year = {1999}
}
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Abstract:
One bottleneck in implementing a system that intelligently queries the Web is developing "wrappers"---programs that extract data from Web pages. Here we describe a method for learning general, page-independent heuristics for extracting data from HTML documents. The input to our learning system is a set of working wrapper programs, paired with HTML pages they correctly wrap. The output is a general procedure for extracting data that works for many formats and many pages. In experiments with a collection of 84 constrained but realistic extraction problems, we demonstrate that 30% of the problems can be handled perfectly by learned extraction heuristics, and around 50% can be handled acceptably. We also demonstrate that learned page-independent extraction heuristics can substantially improve the performance of methods for learning page-specific wrappers. Keywords: information integration, machine learning, extraction. 1 Introduction A number of recent systems operate by taking informatio...
