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Entity resolution (ER) is the task of identifying different representations of the same real-world entities across databases.
A theory for record linkage
Ivan P. Fellegi and Alan B. Sunter. 1969 · 1969
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Finding structure in time
Jeffrey L. Elman. 1990 · 1990
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The merge/purge problem for large databases
Mauricio A. Hernández and Salvatore J. Stolfo. 1995 · 1995
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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A survey of approaches to automatic schema matching
Erhard Rahm and Philip A. Bernstein. 2001 · 2001
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Learning object identification rules for information integration
Sheila Tejada, Craig A. Knoblock, and Steven Minton. 2001 · 2001
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Identity uncertainty and citation matching
Hanna M. Pasula, Bhaskara Marthi, Brian Milch, Stuart J. Russell, and Ilya Shpitser. 2002 · 2002
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Interactive deduplication using active learning
Sunita Sarawagi and Anuradha Bhamidipaty. 2002 · 2002
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Adaptive duplicate detection using learnable string similarity measures
Mikhail Bilenko and Raymond J. Mooney. 2003 · 2003
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Reference reconciliation in complex information spaces
Xin Dong, Alon Halevy, and Jayant Madhavan. 2005 · 2005
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Febrl: A freely available record linkage system with a graphical user interface
Peter Christen. 2008 · 2008
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Natural Language Processing with Python
Steven Bird, Ewan Klein, and Edward Loper. 2009 · 2009
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Clio: Schema mapping creation and data exchange
Ronald Fagin, Laura M. Haas, Mauricio A. Hernández, Renée J. Miller, Lucian Popa, and Yannis Velegrakis. 2009 · 2009
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On active learning of record matching packages
Arvind Arasu, Michaela Götz, and Raghav Kaushik. 2010 · 2010
Cited alongside, same era.
Active learning genetic programming for record deduplication
Junio de Freitas, Gisele Lobo Pappa, Altigran Soares da Silva, Marcos André Gonçalves, Edleno Silva de Moura, Adriano Veloso, Alberto H. F. Laender, and Moisés G. de Carvalho. 2010 · 2010
Cited alongside, same era.
Evaluation of entity resolution approaches on real-world match problems
Hanna Köpcke, Andreas Thor, and Erhard Rahm. 2010 · 2010
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Entity matching: How similar is similar
Jiannan Wang, Guoliang Li, Jeffrey Xu Yu, and Jianhua Feng. 2011 · 2011
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Active sampling for entity matching
Kedar Bellare, Suresh Iyengar, Aditya G. Parameswaran, and Vibhor Rastogi. 2012 · 2012
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Scaling multiple-source entity resolution using statistically efficient transfer learning
ADAM: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Lei Ba. 2015 · 2015
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Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber. 2015 · 2015
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Cross-domain entity resolution in social media
William M. Campbell, Lin Li, Charlie K. Dagli, Joel Acevedo-Aviles, K. Geyer, Joseph P. Campbell, and C. Priebe. 2016 · 2016
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The Magellan data repository
Sanjib Das, AnHai Doan, Paul Suganthan G. C., Chaitanya Gokhale, and Pradap Konda. 2016 · 2016
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Magellan: Toward building entity matching management systems
Pradap Konda, Sanjib Das, C. PaulSuganthanG., AnHai Doan, Adel Ardalan, Jeffrey R. Ballard, Han Li, Fatemah Panahi, Haojun Zhang, Jeffrey F. Naughton, Shishir Prasad, Ganesh Krishnan, Rohit Deep, and Vijay Raghavendra. 2016 · 2016
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Sahand N. Negahban, Benjamin I. P. Rubinstein, and Jim Gemmell. 2012 · 2012
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Active learning of expressive linkage rules using genetic programming
Robert Isele and Christian Bizer. 2013 · 2013
Cited alongside, same era.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
DeCAF: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. 2014 · 2014
Cited alongside, same era.
GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Group based self training for e-commerce product record linkage
Xin Zhao, Yuexin Wu, Hongfei Yan, and Xiaoming Li. 2014 · 2014
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky. 2015 · 2015
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
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Active learning for large-scale entity resolution
Kun Qian, Lucian Popa, and Prithviraj Sen. 2017 · 2017
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Cost-effective active learning for deep image classification
Keze Wang, Dongyu Zhang, Ya Li, Ruimao Zhang, and Liang Lin. 2017 · 2017
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Distributed representations of tuples for entity resolution
Muhammad Ebraheem, Saravanan Thirumuruganathan, Shafiq Joty, Mourad Ouzzani, and Nan Tang. 2018 · 2018
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Deep learning for entity matching: A design space exploration
Sidharth Mudgal, Han Li, Theodoros Rekatsinas, AnHai Doan, Youngchoon Park, Ganesh Krishnan, Rohit Deep, Esteban Arcaute, and Vijay Raghavendra. 2018 · 2018
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke S. Zettlemoyer. 2018 · 2018
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Reuse and adaptation for entity resolution through transfer learning
Saravanan Thirumuruganathan, Shameem Puthiya Parambath, Mourad Ouzzani, Nan Tang, and Shafiq R. Joty. 2018 · 2018
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