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)
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 |
|---|---|---|---|---|
| 5,539 | Pre-trained Embeddings for Entity Resolution: An Experimental Analysis | 2023 | VLDB | 6.0745352e-05 |
| 5,700 | Analyzing How BERT Performs Entity Matching | 2022 | VLDB | 6.0127959e-05 |
| 7,190 | FlexER: Flexible Entity Resolution for Multiple Intents | 2023 | SIGMOD | 5.5737453e-05 |
| 9,823 | The Battleship Approach to the Low Resource Entity Matching Problem | 2023 | SIGMOD | 5.1089508e-05 |
| 10,242 | Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution | 2024 | VLDB | 5.0418764e-05 |
| 10,497 | BEACON: Budget-Aware Entity Matching Across Domains | 2026 | SIGMOD | 4.9605343e-05 |
| 10,604 | In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration | 2026 | SIGMOD | 4.9605343e-05 |
| 10,819 | ALER: An Active Learning Hybrid System for Efficient Entity Resolution | 2026 | VLDB | 4.9605343e-05 |
| 10,948 | Can we trust LLM Self-Explanations for Entity Resolution? | 2026 | VLDB | 4.9605343e-05 |
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 132 | Deep Entity Matching with Pre-Trained Language Models | 2021 | VLDB | 0.00030213215 |
| 158 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD | 0.00028049555 |
| 459 | Distributed Representations of Tuples for Entity Resolution | 2018 | VLDB | 0.00017863654 |
| 517 | Magellan: Toward Building Entity Matching Management Systems | 2016 | VLDB | 0.00016946929 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,539 | 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 | 4,691 | Entity Resolution with Hierarchical Graph Attention Networks | 2022 | SIGMOD |
| 4 | 10,497 | BEACON: Budget-Aware Entity Matching Across Domains | 2026 | SIGMOD |
| 5 | 2,520 | Deep Learning for Blocking in Entity Matching: A Design Space Exploration | 2021 | VLDB |
| 6 | 2,476 | A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching | 2020 | SIGMOD |
| 7 | 5,539 | Pre-trained Embeddings for Entity Resolution: An Experimental Analysis | 2023 | VLDB |
| 8 | 10,242 | Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution | 2024 | VLDB |
| 9 | 132 | Deep Entity Matching with Pre-Trained Language Models | 2021 | VLDB |
| 10 | 5,700 | Analyzing How BERT Performs Entity Matching | 2022 | VLDB |