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10.1162/153244304322972685
2000
Applied Physics Letters
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.
doi:10.1162/153244304322972685
fatcat:f2mbjnvhwra7latl2olzokvtmm