ZeroER: Entity Resolution using Zero Labeled Examples
Summary: ZeroER uses zero-labeled data for ER with a Gaussian Mixture Model separating match vs. unmatch. It adds adaptive regularization and a transitivity-informed generative model, yielding strong unsupervised results close to supervised on five ER benchmarks. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Renzhi Wu (Georgia Institute of Technology)
- 2. Sanya Chaba (Georgia Institute of Technology)
- 3. Saurabh Sawlani (Georgia Institute of Technology)
- 4. Xu Chu (Georgia Institute of Technology)
- 5. Saravanan Thirumuruganathan (Hamad Bin Khalifa University; Qatar Computing Research Institute)
BibTeX Citation
@inproceedings{wu_sigmod20,
title = {{ZeroER: Entity Resolution using Zero Labeled Examples}},
author = {Wu, Renzhi and Chaba, Sanya and Sawlani, Saurabh and Chu, Xu and Thirumuruganathan, Saravanan},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389743},
url = {https://dl.acm.org/doi/10.1145/3318464.3389743},
year = {2020}
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