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Relation Extraction (RE) serves as a crucial technology for transforming unstructured text into structured information, especially within the framework of Knowledge Graph development.
Modeling relations and their mentions without labeled text
Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010 · 2010
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Knowledge base population: Successful approaches and challenges
Heng Ji and Ralph Grishman. 2011 · 2011
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Information extraction: Capabilities and challenges
Ralph Grishman. 2012 · 2012
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Pattern-based relation extraction
Caroline Barrière. 2016 · 2016
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Relation schema induction using tensor factorization with side information
Madhav Nimishakavi, Uday Singh Saini, and Partha P. Talukdar. 2016 · 2016
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Improved neural relation detection for knowledge base question answering
Mo Yu, Wenpeng Yin, Kazi Saidul Hasan, Cícero Nogueira dos Santos, Bing Xiang, and Bowen Zhou. 2017 · 2017
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FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
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Improving relation extraction by pre-trained language representations
Christoph Alt, Marc Hübner, and Leonhard Hennig. 2019 · 2019
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Connecting the dots: Document-level neural relation extraction with edge-oriented graphs
Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou. 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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A BERT-based universal model for both within-and cross-sentence clinical temporal relation extraction
Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, and Guergana Savova. 2019 · 2019
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Combining position-aware CNN and RNN for relation extraction
Meng Ma, Weiyu Hao, and Pengfei Li. 2019 · 2019
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Relation extraction from clinical narratives using pre-trained language models
Qiang Wei, Zongcheng Ji, Yuqi Si, Jingcheng Du, Jingqi Wang, Firat Tiryaki, Stephen Wu, Cui Tao, Kirk Roberts, and Hua Xu. 2019 · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
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Relation extraction with BERT-based pre-trained model
Haitao Yu, Yi Cao, Gang Cheng, Ping Xie, Yang Yang, and Peng Yu. 2020 · 2020
Cited alongside, same era.
Pattern-based bootstrapping framework for biomedical relation extraction
S. S. Deepika and T. V. Geetha. 2021 · 2021
Cited alongside, same era.
The combination of CNN, RNN, and DNN for relation extraction
Yunzhou Li. 2021 · 2021
Cited alongside, same era.
Multi-type microbial relation extraction by transfer learning
Xia Sun, Chengcheng Fu, Suoqi Liu, Wenjie Chen, Ran Zhong, Tingting He, and Xingpeng Jiang. 2021 · 2021
Cited alongside, same era.
A frustratingly easy approach for entity and relation extraction
Zexuan Zhong and Danqi Chen. 2021 · 2021
Cited alongside, same era.
CrossRE: A cross-domain dataset for relation extraction
Elisa Bassignana and Barbara Plank. 2022 · 2022
Cited alongside, same era.
Prompt-learning for cross-lingual relation extraction
Chiaming Hsu, Changtong Zan, Liang Ding, Longyue Wang, Xiaoting Wang, Weifeng Liu, Fu Lin, and Wenbin Hu. 2023 · 2023
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Evaluating open-domain question answering in the era of large language models
Ehsan Kamalloo, Nouha Dziri, Charles Clarke, and Davood Rafiei. 2023 · 2023
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Zero-shot information extraction for clinical meta-analysis using large language models
David Kartchner, Selvi Ramalingam, Irfan Al-Hussaini, Olivia Kronick, and Cassie Mitchell. 2023 · 2023
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Adaptive machine translation with large language models
Yasmin Moslem, Rejwanul Haque, John D. Kelleher, and Andy Way. 2023 · 2023
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Crosslingual generalization through multitask finetuning
Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M. Saiful Bari, Sheng Shen, Zheng Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, and Colin Raffel. 2023 · 2023
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No language left behind: Scaling human-centered machine translation
Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Y. Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loïc Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, and Jeff Wang. 2022 · 2022
Cited alongside, same era.
Graph-based model generation for few-shot relation extraction
Wanli Li and Tieyun Qian. 2022 · 2022
Cited alongside, same era.
Few-shot learning with multilingual generative language models
Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona T. Diab, Veselin Stoyanov, and Xian Li. 2022 · 2022
Cited alongside, same era.
Meta-learning adaptive knowledge distillation for efficient biomedical natural language processing
Abiola Obamuyide and Blair Johnston. 2022 · 2022
Cited alongside, same era.
Introducing ChatGPT
OpenAI. 2022 · 2022
Cited alongside, same era.
Cross-lingual transfer learning for relation extraction using universal dependencies
Nasrin Taghizadeh and Heshaam Faili. 2022 · 2022
Cited alongside, same era.
Weakly-supervised questions for zero-shot relation extraction
Saeed Najafi and Alona Fyshe. 2023 · 2023
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Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay. 2023 · 2023
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LLaMA: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Revisiting relation extraction in the era of large language models
Somin Wadhwa, Silvio Amir, and Byron C. Wallace. 2023 · 2023
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Improving extraction of chinese open relations using pre-trained language model and knowledge enhancement
Chaojie Wen, Xudong Jia, and Tao Chen. 2023 · 2023
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Llmre: A zero-shot entity relation extraction method based on the large language model
Wei Zhao, Qinghui Chen, and Junling You. 2023 · 2023
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Enhancing relation extraction from biomedical texts by large language models
Masaki Asada and Ken Fukuda. 2024 · 2024
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OLMo: Accelerating the science of language models
Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, and Hannaneh Hajishirzi. 2024 · 2024
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