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Migrating a Privacy-Safe Information Extraction System to a Software 2.0 Design

Summary: Case study converting Gmail's privacy-safe, production rule-based IE to Software 2.0: use rule outputs as training labels to build ML extractors that improve precision/recall, shrink codebase, and enable cross-language extraction. Discusses challenges in training-data generation/management, model evaluation, and necessary Software‑1.0 infrastructure to safely deploy ML extractors. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
367
Venue
CIDR
Year
2020
Pagerank
5.093636e-05
Overall Rank
11,740 | 19.46%
DOI
-

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BibTeX Citation

@inproceedings{sheng_cidr20,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '20},
        title = {{Migrating a Privacy-Safe Information Extraction System to a Software 2.0 Design}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Sheng, Ying and Vo, Nguyen and Wendt, James B. and Tata, Sandeep and Najork, Marc},
        year = {2020}
}

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