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Entities are at the center of how we represent and aggregate knowledge.
Retrieval-augmented generation for knowledge-intensive NLP tasks
Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2005
Earlier work this paper cites.
Average R-Precision , pp. 195–195
Steven M. Beitzel, Eric C. Jensen, and Ophir Frieder · 2009
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Introduction to algorithms
Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, and Clifford Stein · 2009
Earlier work this paper cites.
KILT: a Benchmark for Knowledge Intensive Language Tasks
Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vassilis Plachouras, Tim Rocktäschel, et al · 2009
Earlier work this paper cites.
Robust disambiguation of named entities in text
Johannes Hoffart, Mohamed Amir Yosef, Ilaria Bordino, Hagen Fürstenau, Manfred Pinkal, Marc Spaniol, Bilyana Taneva, Stefan Thater, and Gerhard Weikum · 2011
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Generating text with recurrent neural networks
Ilya Sutskever, James Martens, and Geoffrey E Hinton · 2011
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Introduction to “this is watson”
David A Ferrucci · 2012
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KORE: Keyphrase Overlap Relatedness for Entity Disambiguation
Johannes Hoffart, Stephan Seufert, Dat Ba Nguyen, Martin Theobald, and Gerhard Weikum · 2012
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Facc1: Freebase annotation of clueweb corpora, version 1 (release date 2013-06-26, format version 1, correction level 0)
Evgeniy Gabrilovich, Michael Ringgaard, and Amarnag Subramanya · 2013
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Semantic multimedia information retrieval based on contextual descriptions
Nadine Steinmetz and Harald Sack · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, and Jeffrey David Ullman · 2014
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Entity linking meets word sense disambiguation: a unified approach
Andrea Moro, Alessandro Raganato, and Roberto Navigli · 2014
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From tagme to wat: A new entity annotator
Francesco Piccinno and Paolo Ferragina · 2014
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N 3 -a collection of datasets for named entity recognition and disambiguation in the nlp interchange format
Michael Röder, Ricardo Usbeck, Sebastian Hellmann, Daniel Gerber, and Andreas Both · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
Earlier work this paper cites.
Analysis of named entity recognition and linking for tweets
Leon Derczynski, Diana Maynard, Giuseppe Rizzo, Marieke Van Erp, Genevieve Gorrell, Raphaël Troncy, Johann Petrak, and Kalina Bontcheva · 2015
Earlier work this paper cites.
Leveraging deep neural networks and knowledge graphs for entity disambiguation
Hongzhao Huang, Larry Heck, and Heng Ji · 2015
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Open knowledge extraction challenge
Andrea Giovanni Nuzzolese, Anna Lisa Gentile, Valentina Presutti, Aldo Gangemi, Darío Garigliotti, and Roberto Navigli · 2015
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URL https://www.seobythesea.com/2015/09/disambiguate-entities-in-queries-and-pages/
Bill Slawski, Sep 2015 · 2015
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
Earlier work this paper cites.
Joint learning of the embedding of words and entities for named entity disambiguation
Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, and Yoshiyasu Takefuji · 2016
Earlier work this paper cites.
Guided open vocabulary image captioning with constrained beam search
Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould · 2017
Cited alongside, same era.
Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes · 2017
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Deep joint entity disambiguation with local neural attention
Octavian-Eugen Ganea and Thomas Hofmann · 2017
Cited alongside, same era.
Lexically constrained decoding for sequence generation using grid beam search
Chris Hokamp and Qun Liu · 2017
Cited alongside, same era.
TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer · 2017
Cited alongside, same era.
Neural AMR: Sequence-to-sequence models for parsing and generation
Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, and Luke Zettlemoyer · 2017
ELI5: long form question answering
Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli · 2019
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Joint entity linking with deep reinforcement learning
Zheng Fang, Yanan Cao, Qian Li, Dongjie Zhang, Zhenyu Zhang, and Yanbing Liu · 2019
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2019
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Natural questions: A benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov · 2019
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Boosting entity linking performance by leveraging unlabeled documents
Phong Le and Ivan Titov · 2019
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Zero-shot entity linking by reading entity descriptions
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Cited alongside, same era.
Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer · 2017
Cited alongside, same era.
A sequence-to-sequence model for semantic role labeling
Angel Daza and Anette Frank · 2018
Cited alongside, same era.
T-rex: A large scale alignment of natural language with knowledge base triples
Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Elena Simperl, and Frederique Laforest · 2018
Cited alongside, same era.
Robust named entity disambiguation with random walks
Zhaochen Guo and Denilson Barbosa · 2018
Cited alongside, same era.
End-to-end neural entity linking
Nikolaos Kolitsas, Octavian-Eugen Ganea, and Thomas Hofmann · 2018
Cited alongside, same era.
Improving entity linking by modeling latent relations between mentions
Phong Le and Ivan Titov · 2018
Cited alongside, same era.
Lajanugen Logeswaran, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, Jacob Devlin, and Honglak Lee · 2019
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How decoding strategies affect the verifiability of generated text
Luca Massarelli, Fabio Petroni, Aleksandra Piktus, Myle Ott, Tim Rocktäschel, Vassilis Plachouras, Fabrizio Silvestri, and Sebastian Riedel · 2019
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fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller · 2019
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Language models are unsupervised multitask learners
A. Radford, Jeffrey Wu, R. Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation
Hamed Shahbazi, Xiaoli Z Fern, Reza Ghaeini, Rasha Obeidat, and Prasad Tadepalli · 2019
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Learning dynamic context augmentation for global entity linking
Xiyuan Yang, Xiaotao Gu, Sheng Lin, Siliang Tang, Yueting Zhuang, Fei Wu, Zhigang Chen, Guoping Hu, and Xiang Ren · 2019
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Improving entity linking by modeling latent entity type information
Shuang Chen, Jinpeng Wang, Feng Jiang, and Chin-Yew Lin · 2020
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Poly-encoders: Architectures and pre-training strategies for fast and accurate multi-sentence scoring
Samuel Humeau, Kurt Shuster, Marie-Anne Lachaux, and Jason Weston · 2020
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
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Evaluating the impact of knowledge graph context on entity disambiguation models
Isaiah Onando Mulang’, Kuldeep Singh, Chaitali Prabhu, Abhishek Nadgeri, Johannes Hoffart, and Jens Lehmann · 2020
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Document ranking with a pretrained sequence-to-sequence model
Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin · 2020
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Fine-grained entity typing for domain independent entity linking
Yasumasa Onoe and Greg Durrett · 2020
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Recipes for building an open-domain chatbot
Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M Smith, et al · 2020
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Don’t parse, generate! a sequence to sequence architecture for task-oriented semantic parsing
Subendhu Rongali, Luca Soldaini, Emilio Monti, and Wael Hamza · 2020
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Rel: An entity linker standing on the shoulders of giants
Johannes M. van Hulst, Faegheh Hasibi, Koen Dercksen, Krisztian Balog, and Arjen P. de Vries · 2020
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Scalable zero-shot entity linking with dense entity retrieval
Ledell Wu, Fabio Petroni, Martin Josifoski, Sebastian Riedel, and Luke Zettlemoyer · 2020
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Joint learning of named entity recognition and entity linking
Pedro Henrique Martins, Zita Marinho, and André F. T. Martins · 2026
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