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Sequence generation demonstrates promising performance in recent information extraction efforts, by incorporating large-scale pre-trained Seq2Seq models.
Kernel Methods for Relation Extraction
Zelenko, D.; Aone, C.; and Richardella, A. 2003 · 2003
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Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations
Kambhatla, N. 2004 · 2004
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Automatic information extraction
Boschee, E.; Weischedel, R.; and Zamanian, A. 2005 · 2005
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Exploiting constituent dependencies for tree kernel-based semantic relation extraction
Qian, L.; Zhou, G.; Kong, F.; Zhu, Q.; and Qian, P. 2008 · 2008
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Convolution Kernels on Constituent, Dependency and Sequential Structures for Relation Extraction
Nguyen, T. T.; Moschitti, A.; and Riccardi, G. 2009 · 2009
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SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals
Hendrickx, I.; Kim, S. N.; Kozareva, Z.; Nakov, P.; Séaghdha, D. Ó.; Padó, S.; Pennacchiotti, M.; Romano, L.; and Szpakowicz, S. 2010 · 2010
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Employing Word Representations and Regularization for Domain Adaptation of Relation Extraction
Nguyen, T. H.; and Grishman, R. 2014 · 2014
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Sequence to Sequence Learning with Neural Networks
Sutskever, I.; Vinyals, O.; and Le, Q. V. 2014 · 2014
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Classifying Relations by Ranking with Convolutional Neural Networks
dos Santos, C. N.; Xiang, B.; and Zhou, B. 2015 · 2015
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End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
Miwa, M.; and Bansal, M. 2016 · 2016
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Relation Classification via Multi-Level Attention CNNs
Wang, L.; Cao, Z.; de Melo, G.; and Liu, Z. 2016 · 2016
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Position-aware Attention and Supervised Data Improve Slot Filling
Zhang, Y.; Zhong, V.; Chen, D.; Angeli, G.; and Manning, C. D. 2017 · 2017
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Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
Luan, Y.; He, L.; Ostendorf, M.; and Hajishirzi, H. 2018 · 2018
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SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I.; Lo, K.; and Cohan, A. 2019 · 2019
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Attention Guided Graph Convolutional Networks for Relation Extraction
Guo, Z.; Zhang, Y.; and Lu, W. 2019 · 2019
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Decoupled Weight Decay Regularization
Loshchilov, I.; and Hutter, F. 2019 · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Köpf, A.; Yang, E. Z.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; and Chintala, S. 2019 · 2019
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Language Models are Unsupervised Multitask Learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; and Sutskever, I. 2019 · 2019
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Matching the Blanks: Distributional Similarity for Relation Learning
Soares, L. B.; FitzGerald, N.; Ling, J.; and Kwiatkowski, T. 2019 · 2019
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Structured Prediction as Translation between Augmented Natural Languages
Paolini, G.; Athiwaratkun, B.; Krone, J.; Ma, J.; Achille, A.; Anubhai, R.; dos Santos, C. N.; Xiang, B.; and Soatto, S. 2021 · 2021
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Incorporating medical knowledge in BERT for clinical relation extraction
Roy, A.; and Pan, S. 2021 · 2021
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Paradigm Shift in Natural Language Processing
Sun, T.; Liu, X.; Qiu, X.; and Huang, X. 2021 · 2021
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GDPNet: Refining Latent Multi-View Graph for Relation Extraction
Xue, F.; Sun, A.; Zhang, H.; and Chng, E. S. 2021 · 2021
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A Unified Generative Framework for Various NER Subtasks
Yan, H.; Gui, T.; Dai, J.; Guo, Q.; Zhang, Z.; and Qiu, X. 2021 · 2021
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Ye, W.; Li, B.; Xie, R.; Sheng, Z.; Chen, L.; and Zhang, S. 2019 · 2019
Cited alongside, same era.
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Lewis, M.; Liu, Y.; Goyal, N.; Ghazvininejad, M.; Mohamed, A.; Levy, O.; Stoyanov, V.; and Zettlemoyer, L. 2020 · 2020
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Graph Enhanced Dual Attention Network for Document-Level Relation Extraction
Li, B.; Ye, W.; Sheng, Z.; Xie, R.; Xi, X.; and Zhang, S. 2020 · 2020
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 2020
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LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
Yamada, I.; Asai, A.; Shindo, H.; Takeda, H.; and Matsumoto, Y. 2020 · 2020
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REBEL: Relation Extraction By End-to-end Language generation
Cabot, P. H.; and Navigli, R. 2021 · 2021
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Autoregressive Entity Retrieval
Cao, N. D.; Izacard, G.; Riedel, S.; and Petroni, F. 2021 · 2021
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Zhou, W.; and Chen, M. 2021 · 2021
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Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph
Chen, Y.; Zhang, Y.; and Huang, Y. 2022 · 2022
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GenIE: Generative Information Extraction
Josifoski, M.; Cao, N. D.; Peyrard, M.; Petroni, F.; and West, R. 2022 · 2022
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Unified Structure Generation for Universal Information Extraction
Lu, Y.; Liu, Q.; Dai, D.; Xiao, X.; Lin, H.; Han, X.; Sun, L.; and Wu, H. 2022 · 2022
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Seq2Path: Generating Sentiment Tuples as Paths of a Tree
Mao, Y.; Shen, Y.; Yang, J.; Zhu, X.; and Cai, L. 2022 · 2022
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Sequence-to-Sequence Knowledge Graph Completion and Question Answering
Saxena, A.; Kochsiek, A.; and Gemulla, R. 2022 · 2022
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Paradigm Shift in Natural Language Processing
Sun, T.; Liu, X.; Qiu, X.; and Huang, X. 2022 · 2022
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De-Bias for Generative Extraction in Unified NER Task
Zhang, S.; Shen, Y.; Tan, Z.; Wu, Y.; and Lu, W. 2022b · 2022
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