Dual-Objective Fine-Tuning of BERT for Entity Matching

Summary: JointBERT dual-objective fine-tuning for entity matching: binary match and multi-class identifier prediction under partial identifier coverage. With ample data, it yields 1–5% F1 gains on seen products over single-objective BERT, but falters on unseen products; LIME-based analysis highlights emphasis on informative word classes. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: hc78f4f66c3cf978c
Venue: VLDB
Year: 2021
Pagerank: 6.8394738e-05
Overall Rank: 4,033 | 72.89%
DOI: 10.14778/3467861.3467878

Incoming Non-self Citations Over Time

Authors

1. Ralph Peeters (University of Mannheim)
2. Christian Bizer (University of Mannheim)

BibTeX Citation

@article{peeters_vldb21,
        title = {{Dual-Objective Fine-Tuning of BERT for Entity Matching}},
        author = {Peeters, Ralph and Bizer, Christian},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {10},
        pages = {1913--1921},
        doi = {10.14778/3467861.3467878},
        url = {https://doi.org/10.14778/3467861.3467878},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
5,540	Pre-trained Embeddings for Entity Resolution: An Experimental Analysis	2023	VLDB	6.0907591e-05
5,705	Analyzing How BERT Performs Entity Matching	2022	VLDB	6.0295759e-05
7,181	FlexER: Flexible Entity Resolution for Multiple Intents	2023	SIGMOD	5.5923113e-05
9,785	The Battleship Approach to the Low Resource Entity Matching Problem	2023	SIGMOD	5.1283279e-05
10,210	Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution	2024	VLDB	5.0596605e-05
10,419	BEACON: Budget-Aware Entity Matching Across Domains	2026	SIGMOD	4.9793485e-05
10,527	In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration	2026	SIGMOD	4.9793485e-05
10,748	ALER: An Active Learning Hybrid System for Efficient Entity Resolution	2026	VLDB	4.9793485e-05
10,878	Can we trust LLM Self-Explanations for Entity Resolution?	2026	VLDB	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
134	Deep Entity Matching with Pre-Trained Language Models	2021	VLDB	0.00030043481
158	Deep Learning for Entity Matching: A Design Space Exploration	2018	SIGMOD	0.00028046388
457	Distributed Representations of Tuples for Entity Resolution	2018	VLDB	0.00017907103
530	Magellan: Toward Building Entity Matching Management Systems	2016	VLDB	0.00016855162

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#	Overall Rank	Paper	Year	Venue
1	10,462	Generalized Entity Matching with Adaptivity via Large Language Models	2026	SIGMOD
2	244	Evaluation of entity resolution approaches on real-world match problems	2010	VLDB
3	4,676	Entity Resolution with Hierarchical Graph Attention Networks	2022	SIGMOD
4	10,419	BEACON: Budget-Aware Entity Matching Across Domains	2026	SIGMOD
5	2,514	Deep Learning for Blocking in Entity Matching: A Design Space Exploration	2021	VLDB
6	2,475	A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching	2020	SIGMOD
7	5,540	Pre-trained Embeddings for Entity Resolution: An Experimental Analysis	2023	VLDB
8	10,210	Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution	2024	VLDB
9	134	Deep Entity Matching with Pre-Trained Language Models	2021	VLDB
10	5,705	Analyzing How BERT Performs Entity Matching	2022	VLDB