Fetching the paper…
Reading the bibliography…
Many recent works on Entity Resolution (ER) leverage Deep Learning techniques involving language models to improve effectiveness.
Dual-Objective Fine-Tuning of BERT for Entity Matching
Ralph Peeters and Christian Bizer. 2021 · 1921
Earlier work this paper cites.
A Survey on Contextual Embeddings
Qi Liu, Matt J. Kusner, and Phil Blunsom. 2020 · 2003
Earlier work this paper cites.
Deep Entity Matching with Pre-Trained Language Models
Yuliang Li, Jinfeng Li, Yoshihiko Suhara, AnHai Doan, and Wang-Chiew Tan. 2020b · 2004
Earlier work this paper cites.
Introduction to information retrieval
Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008 · 2008
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.
Efficient entity resolution for large heterogeneous information spaces. In WSDM . 535–544
George Papadakis, Ekaterini Ioannou, Claudia Niederée, and Peter Fankhauser. 2011 · 2011
Earlier work this paper cites.
A Survey of Indexing Techniques for Scalable Record Linkage and Deduplication
Peter Christen. 2012b · 2012
Earlier work this paper cites.
Big Data Integration
Xin Luna Dong and Divesh Srivastava. 2013 · 2013
Earlier work this paper cites.
MFIBlocks: An effective blocking algorithm for entity resolution
Batya Kenig and Avigdor Gal. 2013 · 2013
Earlier work this paper cites.
SIGMa: simple greedy matching for aligning large knowledge bases. In KDD . 572–580
Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, and Zoubin Ghahramani. 2013 · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013b · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) . 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
Earlier work this paper cites.
Schema-agnostic vs Schema-based Configurations for Blocking Methods on Homogeneous Data
George Papadakis, George Alexiou, George Papastefanatos, and Georgia Koutrika. 2015 · 2015
Earlier work this paper cites.
Comparative Analysis of Approximate Blocking Techniques for Entity Resolution
George Papadakis, Jonathan Svirsky, Avigdor Gal, and Themis Palpanas. 2016 · 2016
Earlier work this paper cites.
Enriching Word Vectors with Subword Information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
Cited alongside, same era.
Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Inigo Lopez-Gazpio, and Lucia Specia. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Distributed Representations of Tuples for Entity Resolution
Muhammad Ebraheem, Saravanan Thirumuruganathan, Shafiq R. Joty, Mourad Ouzzani, and Nan Tang. 2018 · 2018
Cited alongside, same era.
GNEM: A Generic One-to-Set Neural Entity Matching Framework. In WWW . 1686–1694
Runjin Chen, Yanyan Shen, and Dongxiang Zhang. 2020 · 2020
Later among the works it cites.
Hierarchical Matching Network for Heterogeneous Entity Resolution. In IJCAI . 3665–3671
Cheng Fu, Xianpei Han, Jiaming He, and Le Sun. 2020 · 2020
Later among the works it cites.
Deep Entity Matching with Pre-Trained Language Models
Yuliang Li, Jinfeng Li, Yoshihiko Suhara, AnHai Doan, and Wang-Chiew Tan. 2020a · 2020
Later among the works it cites.
Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs
Yury A. Malkov and Dmitry A. Yashunin. 2020 · 2020
Later among the works it cites.
Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning
Mohammad Taher Pilehvar and José Camacho-Collados. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep Learning for Entity Matching: A Design Space Exploration. In SIGMOD . 19–34
Sidharth Mudgal, Han Li, Theodoros Rekatsinas, AnHai Doan, Youngchoon Park, Ganesh Krishnan, Rohit Deep, Esteban Arcaute, and Vijay Raghavendra. 2018 · 2018
Cited alongside, same era.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018 · 2018
Cited alongside, same era.
Entity Resolution: Theory, Practice & Open Challenges
Lise Getoor and Ashwin Machanavajjhala. 2012 · 2019
Cited alongside, same era.
Tinybert: Distilling bert for natural language understanding
Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu. 2019 · 2019
Cited alongside, same era.
Low-resource Deep Entity Resolution with Transfer and Active Learning. In ACL . 5851–5861
Jungo Kasai, Kun Qian, Sairam Gurajada, Yunyao Li, and Lucian Popa. 2019 · 2019
Cited alongside, same era.
Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 2019
Cited alongside, same era.
Approximate nearest neighbor search on high dimensional data—experiments, analyses, and improvement
Wen Li, Ying Zhang, Yifang Sun, Wei Wang, Mingjie Li, Wenjie Zhang, and Xuemin Lin. 2019 · 2019
Cited alongside, same era.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Later among the works it cites.
Mpnet: Masked and permuted pre-training for language understanding
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2020 · 2020
Later among the works it cites.
Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers
Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. 2020b · 2020
Later among the works it cites.
Zeroer: Entity resolution using zero labeled examples. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data . 1149–1164
Renzhi Wu, Sanya Chaba, Saurabh Sawlani, Xu Chu, and Saravanan Thirumuruganathan. 2020 · 2020
Later among the works it cites.
An Overview of End-to-End Entity Resolution for Big Data
Vassilis Christophides, Vasilis Efthymiou, Themis Palpanas, George Papadakis, and Kostas Stefanidis. 2021 · 2021
Later among the works it cites.
Large dual encoders are generalizable retrievers
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y Zhao, Yi Luan, Keith B Hall, Ming-Wei Chang, et al · 2021
Later among the works it cites.
EAGER: Embedding-Assisted Entity Resolution for Knowledge Graphs
Daniel Obraczka, Jonathan Schuchart, and Erhard Rahm. 2021 · 2021
Later among the works it cites.
Automated machine learning for entity matching tasks. In EDBT
Matteo Paganelli, Francesco Del Buono, Pevarello Marco, Francesco Guerra, and Maurizio Vincini. 2021 · 2021
Later among the works it cites.
Blocking and Filtering Techniques for Entity Resolution: A Survey
George Papadakis, Dimitrios Skoutas, Emmanouil Thanos, and Themis Palpanas. 2021b · 2021
Later among the works it cites.
Deep Learning for Blocking in Entity Matching: A Design Space Exploration
Saravanan Thirumuruganathan, Han Li, Nan Tang, Mourad Ouzzani, Yash Govind, Derek Paulsen, Glenn Fung, and AnHai Doan. 2021 · 2021
Later among the works it cites.
Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making. In ACL/IJCNLP . 2770–2781
Zijun Yao, Chengjiang Li, Tiansi Dong, Xin Lv, Jifan Yu, Lei Hou, Juanzi Li, Yichi Zhang, and Zelin Dai. 2021 · 2021
Later among the works it cites.
Analyzing How BERT Performs Entity Matching
Matteo Paganelli, Francesco Del Buono, Andrea Baraldi, and Francesco Guerra. 2022 · 2022
Later among the works it cites.
Bipartite Graph Matching Algorithms for Clean-Clean Entity Resolution: An Empirical Evaluation. In EDBT . 2:462–2:474
George Papadakis, Vasilis Efthymiou, Emmanouil Thanos, and Oktie Hassanzadeh. 2022 · 2022
Later among the works it cites.
From BERT to GPT-3 Codex: Harnessing the Potential of Very Large Language Models for Data Management
Immanuel Trummer. 2022 · 2022
Later among the works it cites.