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Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
ISBN: 978-1-4244-5445-7
pp: 173-176
Daisy Zhe Wang , EECS, University of California, Berkeley, USA
Eirinaios Michelakis , EECS, University of California, Berkeley, USA
Michael J. Franklin , EECS, University of California, Berkeley, USA
Minos Garofalakis , Technical University of Crete, Greece
Joseph M. Hellerstein , EECS, University of California, Berkeley, USA
ABSTRACT
Unstructured text represents a large fraction of the world's data. It often contains snippets of structured information (e.g., people's names and zip codes). Information Extraction (IE) techniques identify such structured information in text. In recent years, database research has pursued IE on two fronts: declarative languages and systems for managing IE tasks, and probabilistic databases for querying the output of IE. In this paper, we make the first step to merge these two directions, without loss of statistical robustness, by implementing a state-of-the-art statistical IE model - Conditional Random Fields (CRF) - in the setting of a Probabilistic Database that treats statistical models as first-class data objects. We show that the Viterbi algorithm for CRF inference can be specified declaratively in recursive SQL. We also show the performance benefits relative to a standalone open-source Viterbi implementation. This work opens up the optimization opportunities for queries involving both inference and relational operators over IE models.
CITATION
Daisy Zhe Wang, Eirinaios Michelakis, Michael J. Franklin, Minos Garofalakis, Joseph M. Hellerstein, "Probabilistic declarative information extraction", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 173-176, doi:10.1109/ICDE.2010.5447844
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