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Despite efforts to distinguish three different evaluation setups (Bekoulis et al., 2018), numerous end-to-end Relation Extraction (RE) articles present unreliable performance comparison to previous work.
Transformers: State-of-the-art Natural Language Processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models
John M Giorgi, Xindi Wang, Nicola Sahar, Won Young Shin, Gary D Bader, Bo Wang, Young Shin, Gary D Bader, and Bo Wang. 2019 · 1912
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Probabilistic reasoning for entity & relation recognition
Dan Roth and Wen-tau Yih. 2002 · 2002
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The automatic content extraction (ACE) program – tasks, data, and evaluation
George Doddington, Alexis Mitchell, Mark Przybocki, Lance Ramshaw, Stephanie Strassel, and Ralph Weischedel. 2004 · 2004
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A linear programming formulation for global inference in natural language tasks
Dan Roth and Wen-tau Yih. 2004 · 2004
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A Review of Relation Extraction
Nguyen Bach and Sameer Badaskar. 2007 · 2007
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Design challenges and misconceptions in named entity recognition
Lev Ratinov and Dan Roth. 2009 · 2009
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Joint entity and relation extraction using card-pyramid parsing
Rohit J. Kate and Raymond Mooney. 2010 · 2010
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Word representations: A simple and general method for semi-supervised learning
Joseph Turian, Lev-Arie Ratinov, and Yoshua Bengio. 2010 · 2010
Earlier work this paper cites.
Natural language processing (almost) from scratch
Ronan Collobert and Jason Weston. 2011 · 2011
Earlier work this paper cites.
Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports
Harsha Gurulingappa, Abdul Mateen Rajput, Angus Roberts, Juliane Fluck, Martin Hofmann-Apitius, and Luca Toldo. 2012 · 2012
Earlier work this paper cites.
Distributed Representations of Words and Phrases and their Compositionality
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
Incremental joint extraction of entity mentions and relations
Qi Li and Heng Ji. 2014 · 2014
Earlier work this paper cites.
Modeling joint entity and relation extraction with table representation
Makoto Miwa and Yutaka Sasaki. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Earlier work this paper cites.
Bidirectional LSTM-CRF Models for Sequence Tagging
Zhiheng Huang, Wei Xu, and Kai Yu. 2015 · 2015
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Distant supervision for relation extraction via piecewise convolutional neural networks
Daojian Zeng, Kang Liu, Yubo Chen, and Jun Zhao. 2015 · 2015
Earlier work this paper cites.
Table filling multi-task recurrent neural network for joint entity and relation extraction
Pankaj Gupta, Hinrich Schütze, and Bernt Andrassy. 2016 · 2016
Cited alongside, same era.
Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
Cited alongside, same era.
Joint Models for Extracting Adverse Drug Events from Biomedical Text
Fei Li, Yue Zhang, Meishan Zhang, and Donghong Ji. 2016 · 2016
Cited alongside, same era.
End-to-end relation extraction using LSTMs on sequences and tree structures
Makoto Miwa and Mohit Bansal. 2016 · 2016
Cited alongside, same era.
Global normalization of convolutional neural networks for joint entity and relation classification
Heike Adel and Hinrich Schütze. 2017 · 2017
Cited alongside, same era.
Generalisation in named entity recognition: A quantitative analysis
Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction
Yi Luan, Luheng He, Mari Ostendorf, and Hannaneh Hajishirzi. 2018 · 2018
Later among the works it cites.
On the State of the Art of Evaluation in Neural Language Models
Gabor Melis, Chris Dyer, and Phil Blunsom. 2018 · 2018
Later among the works it cites.
Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
Extracting entities and relations with joint minimum risk training
Changzhi Sun, Yuanbin Wu, Man Lan, Shiliang Sun, Wenting Wang, Kuang-Chih Lee, and Kewen Wu. 2018 · 2018
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
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Isabelle Augenstein, Leon Derczynski, and Kalina Bontcheva. 2017 · 2017
Cited alongside, same era.
Deep Biaffine Attention for Neural Dependency Parsing
Timothy Dozat and Christopher D Manning. 2017 · 2017
Cited alongside, same era.
Going out on a limb: Joint extraction of entity mentions and relations without dependency trees
Arzoo Katiyar and Claire Cardie. 2017 · 2017
Cited alongside, same era.
End-to-end neural coreference resolution
Kenton Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer. 2017 · 2017
Cited alongside, same era.
Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017 · 2017
Cited alongside, same era.
A neural joint model for entity and relation extraction from biomedical text
Fei Li, Meishan Zhang, Guohong Fu, and Donghong Ji. 2017 · 2017
Cited alongside, same era.
Lexical features in coreference resolution: To be used with caution
Nafise Sadat Moosavi and Michael Strube. 2017 · 2017
Cited alongside, same era.
Kalpit Dixit and Yaser Al-Onaizan. 2019 · 2019
Later among the works it cites.
Entity-relation extraction as multi-turn question answering
Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, and Jiwei Li. 2019 · 2019
Later among the works it cites.
A general framework for information extraction using dynamic span graphs
Yi Luan, Dave Wadden, Luheng He, Amy Shah, Mari Ostendorf, and Hannaneh Hajishirzi. 2019 · 2019
Later among the works it cites.
End-to-end neural relation extraction using deep biaffine attention
Dat Quoc Nguyen and Karin Verspoor. 2019 · 2019
Later among the works it cites.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury Google, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf Xamla, Edward Yang, Zach Devito, Martin Raison Nabla, Alykhan Tejani, Sasank Chilamkurthy, Qure Ai, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Later among the works it cites.
A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks
Victor Sanh, Thomas Wolf, and Sebastian Ruder. 2019 · 2019
Later among the works it cites.
Entity, Relation, and Event Extraction with Contextualized Span Representations
David Wadden, Ulme Wennberg, Yi Luan, and Hannaneh Hajishirzi. 2019 · 2019
Later among the works it cites.
Span-based Joint Entity and Relation Extraction with Transformer Pre-training
Markus Eberts and Adrian Ulges. 2020 · 2020
Closest in time.
Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study
Jinlan Fu, Pengfei Liu, Qi Zhang, and Xuanjing Huang. 2020 · 2020
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Learning from Context or Names? An Empirical Study on Neural Relation Extraction
Hao Peng, Tianyu Gao, Xu Han, Yankai Lin, Peng Li, Zhiyuan Liu, Maosong Sun, and Jie Zhou. 2020 · 2020
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Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data
Shachar Rosenman, Alon Jacovi, and Yoav Goldberg. 2020 · 2020
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Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization
Bruno Taillé, Vincent Guigue, and Patrick Gallinari. 2020 · 2020
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