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Adaptive Rule Discovery for Labeling Text Data

Summary: Weakly supervised labeling of text with feedback; Darwin auto-generates and refines rules from an initial cue and scales to 1M+ sentences. CFG-based labeling functions; yields ~40% more positives than Snuba with the same effort. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6224
Venue
SIGMOD
Year
2021
Pagerank
5.5560452e-05
Overall Rank
5,347 | 62.81%
DOI
10.1145/3448016.3457334

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Showing 8 of 8 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
254 Snorkel: Rapid Training Data Creation with Weak Supervision 2018 VLDB 0.00030540555
398 Big Data Integration 2013 VLDB 0.00024372588
1,215 Snuba: Automating Weak Supervision to Label Training Data 2019 VLDB 0.0001323375
3,897 SLiMFast: Guaranteed Results for Data Fusion and Source Reliability 2017 SIGMOD 6.6554845e-05
6,868 Cost-Effective Data Annotation using Game-Based Crowdsourcing 2019 VLDB 4.9010083e-05
7,766 ICARUS: Minimizing Human Effort in Iterative Data Completion 2018 VLDB 4.6564959e-05
8,585 Robust Entity Resolution using Random Graphs 2018 SIGMOD 4.4905755e-05
11,755 Scalable Semantic Querying of Text 2018 VLDB 4.1945683e-05
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