Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution
Summary: End-to-end iterative co-learning framework that jointly trains blocker and matcher for low-resource ER by exchanging iteratively updated pseudo-labels to broaden supervision. Introduces noise-mitigation for label generation, selection and training, yielding mutual gains and 9–51% improvements on benchmarks. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Shiwen Wu
- 2. Qiyu Wu
- 3. Honghua Dong
- 4. Wen Hua
- 5. Xiaofang Zhou
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