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A typical architecture for end-to-end entity linking systems consists of three steps: mention detection, candidate generation and entity disambiguation.
Biobert: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019 · 1901
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Scibert: Pretrained contextualized embeddings for scientific text
Iz Beltagy, Arman Cohan, and Kyle Lo. 2019 · 1903
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ERNIE: enhanced language representation with informative entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019 · 2003
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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 · 2011
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Improving candidate generation for entity linking
Yuhang Guo, Bing Qin, Yuqin Li, Ting Liu, and Sheng Li. 2013 · 2013
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Evaluating entity linking with wikipedia
Ben Hachey, Will Radford, Joel Nothman, Matthew Honnibal, and James R. Curran. 2013 · 2013
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A joint model for entity analysis: Coreference, typing, and linking
Greg Durrett and Dan Klein. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Personalized page rank for named entity disambiguation
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J-NERD: joint named entity recognition and disambiguation with rich linguistic features
Dat Ba Nguyen, Martin Theobald, and Gerhard Weikum. 2016 · 2016
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Joint learning of the embedding of words and entities for named entity disambiguation
Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, and Yoshiyasu Takefuji. 2016 · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Learning distributed representations of texts and entities from knowledge base
Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, and Yoshiyasu Takefuji. 2017 · 2017
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Universal language model fine-tuning for text classification
Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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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
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SWAG: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018 · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 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 · 2019
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Jeremy Howard and Sebastian Ruder. 2018 · 2018
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End-to-end neural entity linking
Nikolaos Kolitsas, Octavian-Eugen Ganea, and Thomas Hofmann. 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 Zettlemoyer. 2018 · 2018
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Matthew E. Peters, Sebastian Ruder, and Noah A. Smith. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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BERT rediscovers the classical NLP pipeline
Ian Tenney, Dipanjan Das, and Ellie Pavlick. 2019 · 2019
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