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Uncertainty Management in Rule-Based Information Extraction Systems

Summary: Proposes a probabilistic, max-entropy model to quantify uncertainty in rule-based information extraction and its compositional rules. Adds scalable learning via model decomposition; enables incremental accuracy as new rules or data are added. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4112
Venue
SIGMOD
Year
2009
Pagerank
6.3999205e-05
Overall Rank
4,156 | 71.09%
DOI
-

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