Paper: Learning Field Compatibilities To Extract Database Records From Unstructured Text

ACL ID W06-1671
Title Learning Field Compatibilities To Extract Database Records From Unstructured Text
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
Authors

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 sys- tem 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 ex- amine how higher representational power can impact performance. We apply our techniques to the task of extracting contact records from faculty and student home- pages, demonstrating a 53% error reduc- tion over baseline approaches.