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Scalable Ad-hoc Entity Extraction from Text Collections

Summary: Introduces ad-hoc entity extraction where target entities come from a task-specific list, avoiding full-document processing. Proposes an inverted-index-driven pruning approach that identifies and processes only task-relevant documents, with empirical gains on real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9918
Venue
VLDB
Year
2008
Pagerank
6.4100657e-05
Overall Rank
4,992 | 65.76%
DOI
10.14778/1454159.1454164

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{agrawal_vldb08,
        title = {{Scalable Ad-hoc Entity Extraction from Text Collections}},
        author = {Agrawal, Sanjay and Chakrabarti, Kaushik and Chaudhuri, Surajit and Ganti, Venkatesh},
        journal = {PVLDB},
        series = {{VLDB} '08},
        volume = {1},
        number = {1},
        pages = {945--956},
        doi = {10.14778/1454159.1454164},
        url = {https://doi.org/10.14778/1454159.1454164},
        year = {2008}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
169 Efficient Exact Set-Similarity Joins 2006 VLDB 0.0002743469
200 Efficient set joins on similarity predicates 2004 SIGMOD 0.00025597287
974 To Search or to Crawl? Towards a Query Optimizer for Text-Centric Tasks 2006 SIGMOD 0.00012870746
3,442 An Efficient Filter for Approximate Membership Checking 2008 SIGMOD 7.4122197e-05
5,861 Factorizing Complex Predicates in Queries to Exploit Indexes 2003 SIGMOD 6.0636778e-05
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