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Provenance-based Dictionary Refinement in Information Extraction

Summary: Provenance of extraction outputs drives dictionary refinement, formulating an optimization to maximize quality by pruning entries. Efficient algorithms with a probabilistic model for incomplete labeling are proposed and validated on real extractors, with implications for view maintenance in relational settings. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4755
Venue
SIGMOD
Year
2013
Pagerank
5.093636e-05
Overall Rank
12,250 | 15.96%
DOI
10.1145/2463676.2465284

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BibTeX Citation

@inproceedings{roy_sigmod13,
        title = {{Provenance-based Dictionary Refinement in Information Extraction}},
        author = {Roy, Sudeepa and Chiticariu, Laura and Feldman, Vitaly and Reiss, Frederick R. and Zhu, Huaiyu},
        series = {{SIGMOD} '13},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2463676.2465284},
        url = {https://dl.acm.org/doi/10.1145/2463676.2465284},
        year = {2013}
}

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