Self-Training for Label-Efficient Information Extraction from Semi-Structured Web-Pages
Summary: LEAST: self-training that synthesizes weakly-labeled fine-tuning corpora for semi-structured web IE using minimal human annotations. Uses uncertainty-aware training to mitigate noisy labels, generalizes across backbones/verticals and cuts human labels up to 11x (<10 pages/site). (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Ritesh Sarkhel
- 2. Binxuan Huang
- 3. Colin Lockard
- 4. Prashant Shiralkar
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,607 | Extraction and Integration of Partially Overlapping Web Sources | 2013 | VLDB | 8.4615436e-05 |
| 3,305 | Fonduer: Knowledge Base Construction from Richly Formatted Data | 2018 | SIGMOD | 7.2417724e-05 |
| 3,578 | KBQA: Learning Question Answering over QA Corpora and Knowledge Bases | 2017 | VLDB | 6.9466938e-05 |
| 6,407 | CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web | 2018 | VLDB | 5.0691335e-05 |
| 8,456 | Visual Segmentation for Information Extraction from Heterogeneous Visually Rich Documents | 2019 | SIGMOD | 4.5018004e-05 |
| 9,259 | Improving Information Extraction from Visually Rich Documents using Visual Span Representations | 2021 | VLDB | 4.3648789e-05 |
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