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EMNLP
2006

Learning Field Compatibilities to Extract Database Records from Unstructured Text

13 years 11 months ago
Learning Field Compatibilities to Extract Database Records from Unstructured Text
Named-entity recognition systems extract entities such as people, organizations, and locations from unstructured text. Rather than extract these mentions in isolation, this paper presents a record extraction system that assembles mentions into records (i.e. database tuples). We construct a probabilistic model of the compatibility between field values, then employ graph partitioning algorithms to cluster fields into cohesive records. We also investigate compatibility functions over sets of fields, rather than simply pairs of fields, to examine how higher representational power can impact performance. We apply our techniques to the task of extracting contact records from faculty and student homepages, demonstrating a 53% error reduction over baseline approaches.
Michael L. Wick, Aron Culotta, Andrew McCallum
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where EMNLP
Authors Michael L. Wick, Aron Culotta, Andrew McCallum
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