Steered Training Data Generation for Learned Semantic Type Detection
Summary: STEER adapts learned semantic type extraction to unseen data lakes via a data-programming labeling framework. Steered-Labeling generates labeled data for numeric and non-numeric columns to fine-tune learned models, boosting performance across four data lakes. (summarized by gpt-5-nano on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 252 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00030532082 |
| 514 | TURL: Table Understanding through Representation Learning | 2021 | VLDB | 0.00021280726 |
| 1,218 | Snuba: Automating Weak Supervision to Label Training Data | 2019 | VLDB | 0.00013221309 |
| 2,513 | Annotating Columns with Pre-trained Language Models | 2022 | SIGMOD | 8.6155767e-05 |
| 2,895 | Sato: Contextual Semantic Type Detection in Tables | 2020 | VLDB | 7.9539265e-05 |
| 3,520 | GitTables: A Large-Scale Corpus of Relational Tables | 2023 | SIGMOD | 7.0136102e-05 |
| 3,826 | Automatic Discovery of Attributes in Relational Databases | 2011 | SIGMOD | 6.7204879e-05 |
| 7,284 | Witan: Unsupervised Labelling Function Generation for Assisted Data Programming | 2022 | VLDB | 4.7716465e-05 |
| 8,915 | Making Table Understanding Work in Practice | 2022 | CIDR | 4.4229886e-05 |
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