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Pre-trained Embeddings for Entity Resolution: An Experimental Analysis

Summary: Thorough empirical study of 12 pre-trained embeddings (fastText, BERT variants) on 17 ER benchmarks, measuring vectorization cost, blocking scalability vs a SOTA deep blocker, and supervised/unsupervised matching performance. Provides actionable insights on encoding vs accuracy trade-offs. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13073
Venue
VLDB
Year
2023
Pagerank
4.8497453e-05
Overall Rank
7,052 | 50.95%
DOI
10.14778/3598581.3598594

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Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
6,894 TableDC: Deep Clustering for Tabular Data 2025 SIGMOD 4.8925595e-05
8,852 Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation 2023 SIGMOD 4.4356508e-05
9,855 Progressive Entity Matching: A Design Space Exploration 2025 SIGMOD 4.269353e-05
9,984 Towards Scalable Visual Data Wrangling via Direct Manipulation 2026 CIDR 4.1945683e-05
11,063 Searching Data Lakes for Nested and Joined Data 2024 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

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

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