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Information extraction (IE) is an important task in Natural Language Processing (NLP), involving the extraction of named entities and their relationships from unstructured text.
Span-based joint entity and relation extraction with transformer pre-training
Markus Eberts and Adrian Ulges. 2019 · 1909
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 1909
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Fastus: A finite-state processor for information extraction from real-world text
Douglas E. Appelt, Jerry R. Hobbs, John Bear, David J. Israel, and Mabry Tyson. 1993 · 1993
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Automatically constructing a dictionary for information extraction tasks
Ellen Riloff. 1993 · 1993
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Multitask learning
Rich Caruana. 1997 · 1997
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Extracting patterns and relations from the world wide web
Sergey Brin. 1999 · 1999
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Kernel methods for relation extraction
Dmitry Zelenko, Chinatsu Aone, and Anthony Richardella. 2002a · 2002
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Introduction to the CoNLL-2004 shared task: Semantic role labeling
Xavier Carreras and Lluís Màrquez. 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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Learning and inference over constrained output
Vasin Punyakanok, Dan Roth, Wen tau Yih, and Dav Zimak. 2005 · 2005
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Ace 2005 multilingual training corpus
Christopher Walker, Stephanie Strassel, Julie Medero, and Kazuaki Maeda. 2006 · 2005
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A review of relation extraction
Nguyen Bach and Sameer Badaskar. 2007 · 2007
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A systematic exploration of the feature space for relation extraction
Jing Jiang and ChengXiang Zhai. 2007 · 2007
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A survey of named entity recognition and classification
David Nadeau and Satoshi Sekine. 2007 · 2007
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Using corpus statistics on entities to improve semi-supervised relation extraction from the web
Benjamin Rosenfeld and Ronen Feldman. 2007 · 2007
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Named entity recognition with bidirectional lstm-cnns
Jason P. C. Chiu and Eric Nichols. 2015 · 2015
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Table filling multi-task recurrent neural network for joint entity and relation extraction
Pankaj Gupta, Hinrich Schütze, and Bernt Andrassy. 2016 · 2016
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Semi-supervised classification with graph convolutional networks
Thomas Kipf and Max Welling. 2016 · 2016
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Neural relation extraction with selective attention over instances
Yankai Lin, Shiqi Shen, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. 2016 · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017 · 2017
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Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Neural relational inference for interacting systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel. 2018 · 2018
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Adaptive graph convolutional neural networks
Ruoyu Li, Sheng Wang, Feiyun Zhu, and Junzhou Huang. 2018 · 2018
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Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction
Yi Luan, Luheng He, Mari Ostendorf, and Hannaneh Hajishirzi. 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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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
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SciBERT: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 2019
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Structured prediction as translation between augmented natural languages
Giovanni Paolini, Ben Athiwaratkun, Jason Krone, Jie Ma, Alessandro Achille, RISHITA ANUBHAI, Cicero Nogueira dos Santos, Bing Xiang, and Stefano Soatto. 2021 · 2021
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A novel global feature-oriented relational triple extraction model based on table filling
Feiliang Ren, Longhui Zhang, Shujuan Yin, Xiaofeng Zhao, Shilei Liu, Bochao Li, and Yaduo Liu. 2021 · 2021
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UniRE: A unified label space for entity relation extraction
Yijun Wang, Changzhi Sun, Yuanbin Wu, Hao Zhou, Lei Li, and Junchi Yan. 2021 · 2021
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A partition filter network for joint entity and relation extraction
Zhiheng Yan, Chong Zhang, Jinlan Fu, Qi Zhang, and Zhongyu Wei. 2021 · 2021
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Heterogeneous graph structure learning for graph neural networks
Jianan Zhao, Xiao Wang, Chuan Shi, Binbin Hu, Guojie Song, and Yanfang Ye. 2021 · 2021
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Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, and Xu Sun. 2019 · 2019
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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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Span-level model for relation extraction
Kalpit Dixit and Yaser Al-Onaizan. 2019 · 2019
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Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen. 2019 · 2019
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Learning discrete structures for graph neural networks
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He. 2019 · 2019
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Exploring structure-adaptive graph learning for robust semi-supervised classification
Xiang Gao, Wei Hu, and Zongming Guo. 2019 · 2019
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Entity, relation, and event extraction with contextualized span representations
David Wadden, Ulme Wennberg, Yi Luan, and Hannaneh Hajishirzi. 2019 · 2019
Cited alongside, same era.
A frustratingly easy approach for entity and relation extraction
Zexuan Zhong and Danqi Chen. 2021 · 2021
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LasUIE: Unifying information extraction with latent adaptive structure-aware generative language model
Hao Fei, Shengqiong Wu, Jingye Li, Bobo Li, Fei Li, Libo Qin, Meishan Zhang, Min Zhang, and Tat-Seng Chua. 2022 · 2022
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Pure transformers are powerful graph learners
Jinwoo Kim, Dat Tien Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, and Seunghoon Hong. 2022 · 2022
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Autoregressive structured prediction with language models
Tianyu Liu, Yuchen Eleanor Jiang, Nicholas Monath, Ryan Cotterell, and Mrinmaya Sachan. 2022 · 2022
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Unified structure generation for universal information extraction
Yaojie Lu, Qing Liu, Dai Dai, Xinyan Xiao, Hongyu Lin, Xianpei Han, Le Sun, and Hua Wu. 2022 · 2022
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Joint entity and relation extraction based on table labeling using convolutional neural networks
Youmi Ma, Tatsuya Hiraoka, and Naoaki Okazaki. 2022 · 2022
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Packed levitated marker for entity and relation extraction
Deming Ye, Yankai Lin, Peng Li, and Maosong Sun. 2022 · 2022
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A survey on graph structure learning: Progress and opportunities
Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Yuanqi Du, Jieyu Zhang, Qiang Liu, Carl Yang, and Shu Wu. 2022 · 2022
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Grammar-constrained decoding for structured NLP tasks without finetuning
Saibo Geng, Martin Josifoski, Maxime Peyrard, and Robert West. 2023 · 2023
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Optimal transport for unsupervised hallucination detection in neural machine translation
Nuno M. Guerreiro, Pierre Colombo, Pablo Piantanida, and André Martins. 2023 · 2023
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Ridong Han, Tao Peng, Chaohao Yang, Benyou Wang, Lu Liu, and Xiang Wan. 2023 · 2023
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Gslb: The graph structure learning benchmark
Zhixun Li, Liang Wang, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, Liang Wang, and Jeffrey Xu Yu. 2023 · 2023
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SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models
Potsawee Manakul, Adian Liusie, and Mark Gales. 2023 · 2023
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Gpt-4 technical report
OpenAI. 2023 · 2023
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Revisiting relation extraction in the era of large language models
Somin Wadhwa, Silvio Amir, and Byron Wallace. 2023 · 2023
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UTC-IE: A unified token-pair classification architecture for information extraction
Hang Yan, Yu Sun, Xiaonan Li, Yunhua Zhou, Xuanjing Huang, and Xipeng Qiu. 2023 · 2023
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Opengsl: A comprehensive benchmark for graph structure learning
Zhiyao Zhou, Sheng Zhou, Bochao Mao, Xuanyi Zhou, Jiawei Chen, Qiaoyu Tan, Daochen Zha, Yan Feng, Chun Chen, and Can Wang. 2023 · 2023
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An autoregressive text-to-graph framework for joint entity and relation extraction
Urchade Zaratiana, Nadi Tomeh, Pierre Holat, and Thierry Charnois. 2024 · 2024
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