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Entity matching is the task of deciding whether two entity descriptions refer to the same real-world entity.
Language Models are Few-Shot Learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, et al · 1901
Earlier work this paper cites.
Dual-Objective Fine-Tuning of BERT for Entity Matching
Ralph Peeters and Christian Bizer. 2021 · 1921
Earlier work this paper cites.
A Theory for Record Linkage
Ivan P. Fellegi and Alan B. Sunter. 1969 · 1969
Earlier work this paper cites.
Duplicate Record Detection: A Survey
Ahmed K. Elmagarmid, Panagiotis G. Ipeirotis, and Vassilios S. Verykios. 2007 · 2007
Earlier work this paper cites.
Evaluation of Entity Resolution Approaches on Real-World Match Problems
Hanna Köpcke, Andreas Thor, and Erhard Rahm. 2010 · 2010
Earlier work this paper cites.
Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection
Peter Christen. 2012 · 2012
Earlier work this paper cites.
Entity Resolution in the Web of Data
Vassilis Christophides, Vasilis Efthymiou, and Kostas Stefanidis. 2015 · 2015
Earlier work this paper cites.
A Survey of Current Link Discovery Frameworks
Markus Nentwig, Michael Hartung, Axel-Cyrille Ngonga Ngomo, and Erhard Rahm. 2017 · 2017
Earlier work this paper cites.
Attention Is All You Need. In Proceedings of the 31st International Conference on Neural Information Processing Systems . 6000–6010
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, et al · 2017
Earlier work this paper cites.
Distributed representations of tuples for entity resolution
Muhammad Ebraheem, Saravanan Thirumuruganathan, Shafiq Joty, Mourad Ouzzani, and Nan Tang. 2018 · 2018
Earlier work this paper cites.
Deep Learning for Entity Matching: A Design Space Exploration. In Proceedings of the 2018 International Conference on Management of Data . 19–34
Sidharth Mudgal, Han Li, Theodoros Rekatsinas, AnHai Doan, Youngchoon Park, et al · 2018
Earlier work this paper cites.
Neural Network Based Extreme Classification and Similarity Models for Product Matching. In Proceedings of the 2018 Conference of the Association for Computational Linguistics, Volume 3 . 8–15
Kashif Shah, Selcuk Kopru, and Jean David Ruvini. 2018 · 2018
Earlier work this paper cites.
BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 . 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Interpreting Deep Learning Models for Entity Resolution: An Experience Report Using LIME. In Proceedings of the Second International Workshop on Exploiting Artificial Intelligence Techniques for Data Management . 8:1–8:4
Vincenzo Di Cicco, Donatella Firmani, Nick Koudas, Paolo Merialdo, and Divesh Srivastava. 2019 · 2019
Earlier work this paper cites.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, et al · 2019
Earlier work this paper cites.
Entity Matching with Transformer Architectures - a Step Forward in Data Integration. In Proceedings of the International Conference on Extending Database Technology . 463–473
Ursin Brunner and Kurt Stockinger. 2020 · 2020
Earlier work this paper cites.
Creating Embeddings of Heterogeneous Relational Datasets for Data Integration Tasks. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (SIGMOD ’20) . 1335–1349
Riccardo Cappuzzo, Paolo Papotti, and Saravanan Thirumuruganathan. 2020 · 2020
Earlier work this paper cites.
A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning . 1597–1607
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2020
Cited alongside, same era.
An Overview of End-to-End Entity Resolution for Big Data
Vassilis Christophides, Vasilis Efthymiou, Themis Palpanas, George Papadakis, and Kostas Stefanidis. 2020 · 2020
Cited alongside, same era.
Supervised Contrastive Learning. In Advances in Neural Information Processing Systems , Vol. 33. 18661–18673
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, et al · 2020
Cited alongside, same era.
Deep Entity Matching with Pre-Trained Language Models
Yuliang Li, Jinfeng Li, Yoshihiko Suhara, AnHai Doan, and Wang-Chiew Tan. 2020 · 2020
Cited alongside, same era.
Neural Networks for Entity Matching: A Survey
Nils Barlaug and Jon Atle Gulla. 2021 · 2021
Cited alongside, same era.
PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching
Pengfei Wang, Xiaocan Zeng, Lu Chen, Fan Ye, Yuren Mao, et al · 2022
Later among the works it cites.
Emergent Abilities of Large Language Models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, et al · 2022
Later among the works it cites.
Entity Resolution with Hierarchical Graph Attention Networks. In Proceedings of the 2022 International Conference on Management of Data . 429–442
Dezhong Yao, Yuhong Gu, Gao Cong, Hai Jin, and Xinqiao Lv. 2022 · 2022
Later among the works it cites.
An Intrinsically Interpretable Entity Matching System. In Proceedings 26th International Conference on Extending Database Technology, Ioannina, Greece, March 28-31, 2023 . 645–657
Andrea Baraldi, Francesco Del Buono, Francesco Guerra, Matteo Paganelli, and Maurizio Vincini. 2023 · 2023
Closest in time.
Pre-Train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, et al · 2023
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SimCSE: Simple Contrastive Learning of Sentence Embeddings. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . 6894–6910
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
Cited alongside, same era.
CollaborER: A Self-supervised Entity Resolution Framework Using Multi-features Collaboration
Congcong Ge, Pengfei Wang, Lu Chen, Xiaoze Liu, Baihua Zheng, et al · 2021
Cited alongside, same era.
Knowledge Transfer for Entity Resolution with Siamese Neural Networks
Michael Loster, Ioannis Koumarelas, and Felix Naumann. 2021 · 2021
Cited alongside, same era.
Machamp: A Generalized Entity Matching Benchmark. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 4633–4642
Jin Wang, Yuliang Li, and Wataru Hirota. 2021 · 2021
Cited alongside, same era.
Calibrate Before Use: Improving Few-Shot Performance of Language Models. In Proceedings of the 38th International Conference on Machine Learning . 12697–12706
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
Cited alongside, same era.
Probing the Robustness of Pre-trained Language Models for Entity Matching. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management . 3786–3790
Mehdi Akbarian Rastaghi, Ehsan Kamalloo, and Davood Rafiei. 2022 · 2022
Cited alongside, same era.
What Makes Good In-Context Examples for GPT-3?. In Proceedings of Deep Learning Inside Out: The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures . Association for Computational Linguistics, 100–114
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, et al · 2022
Cited alongside, same era.
Closest in time.
Using ChatGPT for Entity Matching. In New Trends in Database and Information Systems (Communications in Computer and Information Science) . Springer Nature Switzerland, Cham, 221–230
Ralph Peeters and Christian Bizer. 2023 · 2023
Closest in time.
Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and Preparation. In 2023 IEEE 39th International Conference on Data Engineering . 1502–1515
Runhui Wang, Yuliang Li, and Jin Wang. 2023 · 2023
Closest in time.
Pre-trained embeddings for entity resolution: An experimental analysis
Alexandros Zeakis, George Papadakis, Dimitrios Skoutas, and Manolis Koubarakis. 2023 · 2023
Closest in time.
A Survey of Large Language Models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, et al · 2023
Closest in time.
SETEM: Self-ensemble Training with Pre-trained Language Models for Entity Matching
Huahua Ding, Chaofan Dai, Yahui Wu, Wubin Ma, and Haohao Zhou. 2024 · 2024
Closest in time.
Cost-effective in-context learning for entity resolution: A design space exploration. In 2024 IEEE 40th International Conference on Data Engineering . IEEE, 3696–3709
Meihao Fan, Xiaoyue Han, Ju Fan, Chengliang Chai, Nan Tang, et al · 2024
Closest in time.
Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution. In Forty-first International Conference on Machine Learning
Chrisantha Fernando, Dylan Sunil Banarse, Henryk Michalewski, Simon Osindero, and Tim Rocktäschel. 2024 · 2024
Closest in time.
Cost-Efficient Prompt Engineering for Unsupervised Entity Resolution in the Product Matching Domain
Navapat Nananukul, Khanin Sisaengsuwanchai, and Mayank Kejriwal. 2024 · 2024
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WDC Products: A Multi-Dimensional Entity Matching Benchmark. In Proceedings of the 27th International Conference on Extending Database Technology, Paestum, Italy, March 25 - March 28 . 22–33
Ralph Peeters, Reng Chiz Der, and Christian Bizer. 2024 · 2024
Closest in time.
Fine-tuning Large Language Models for Entity Matching
Aaron Steiner, Ralph Peeters, and Christian Bizer. 2024 · 2024
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Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching
Tianshu Wang, Hongyu Lin, Xiaoyang Chen, Xianpei Han, Hao Wang, et al · 2024
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Jellyfish: Instruction-Tuning Local Large Language Models for Data Preprocessing. In Proceedings of the Conference on Empirical Methods in Natural Language Processing
Haochen Zhang, Yuyang Dong, Chuan Xiao, and Masafumi Oyamada. 2024 · 2024
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