Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction

  • Califf M
  • Mooney R
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Abstract

Information extraction is a form of shallow text processing that locates a specified set of relevant items in a natural-language document. Systems for this task require significant domain-specific knowledge and are time-consuming and difficult to build by hand, making them a good application for machine learning. We present an algorithm, RAPIER, that uses pairs of sample documents and filled templates to induce pattern-match rules that directly extract fillers for the slots in the template. RAPIER is a bottom-up learning algorithm that incorporates techniques from several inductive logic programming systems. We have implemented the algorithm in a system that allows patterns to have constraints on the words, part-of-speech tags, and semantic classes present in the filler and the surrounding text. We present encouraging experimental results on two domains.

Author-supplied keywords

  • information extraction
  • natural language processing
  • relational learning

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Authors

  • Mary Elaine Califf

  • Raymond J Mooney

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