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
- 2. Christian Bizer
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,558 | Pre-trained Embeddings for Entity Resolution: An Experimental Analysis | 2023 | VLDB | 5.0060112e-05 |
| 6,696 | Analyzing How BERT Performs Entity Matching | 2022 | VLDB | 4.9542861e-05 |
| 8,094 | FlexER: Flexible Entity Resolution for Multiple Intents | 2023 | SIGMOD | 4.5840579e-05 |
| 9,462 | The Battleship Approach to the Low Resource Entity Matching Problem | 2023 | SIGMOD | 4.3324933e-05 |
| 10,022 | In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration | 2026 | SIGMOD | 4.1905499e-05 |
| 10,279 | ALER: An Active Learning Hybrid System for Efficient Entity Resolution | 2026 | VLDB | 4.1905499e-05 |
| 11,050 | Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution | 2024 | VLDB | 4.1905499e-05 |
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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 |
|---|---|---|---|---|
| 219 | Deep Entity Matching with Pre-Trained Language Models | 2021 | VLDB | 0.00033354456 |
| 293 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD | 0.00028661817 |
| 705 | Magellan: Toward Building Entity Matching Management Systems | 2016 | VLDB | 0.00017779048 |
| 740 | Distributed Representations of Tuples for Entity Resolution | 2018 | VLDB | 0.00017358024 |
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