Fetching the paper…
Reading the bibliography…
Compared with traditional sentence-level relation extraction, document-level relation extraction is a more challenging task where an entity in a document may be mentioned multiple times and associated with multiple relations.
Huggingface’s 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, et al. 2019 · 1910
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Relation classification via convolutional deep neural network
Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, and Jun Zhao. 2014 · 2014
Earlier work this paper cites.
Bidirectional recurrent convolutional neural network for relation classification
Rui Cai, Xiaodong Zhang, and Houfeng Wang. 2016 · 2016
Earlier work this paper cites.
Context-aware representations for knowledge base relation extraction
Daniil Sorokin and Iryna Gurevych. 2017 · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2018 · 2018
Earlier work this paper cites.
Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018 · 2018
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Inter-sentence relation extraction with document-level graph convolutional neural network
Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou. 2019 · 2019
Cited alongside, same era.
DocRED: A large-scale document-level relation extraction dataset
Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, and Maosong Sun. 2019 · 2019
Cited alongside, same era.
Reasoning with latent structure refinement for document-level relation extraction
Guoshun Nan, Zhijiang Guo, Ivan Sekulic, and Wei Lu. 2020 · 2020
Cited alongside, same era.
Global-to-local neural networks for document-level relation extraction
Difeng Wang, Wei Hu, Ermei Cao, and Weijian Sun. 2020 · 2020
Cited alongside, same era.
MRN: A locally and globally mention-based reasoning network for document-level relation extraction
Jingye Li, Kang Xu, Fei Li, Hao Fei, Yafeng Ren, and Donghong Ji. 2021 · 2021
Later among the works it cites.
Learning logic rules for document-level relation extraction
Dongyu Ru, Changzhi Sun, Jiangtao Feng, Lin Qiu, Hao Zhou, Weinan Zhang, Yong Yu, and Lei Li. 2021 · 2021
Later among the works it cites.
Entity structure within and throughout: Modeling mention dependencies for document-level relation extraction
Benfeng Xu, Quan Wang, Yajuan Lyu, Yong Zhu, and Zhendong Mao. 2021 · 2021
Later among the works it cites.
Dwie: An entity-centric dataset for multi-task document-level information extraction
Klim Zaporojets, Johannes Deleu, Chris Develder, and Thomas Demeester. 2021 · 2021
Later among the works it cites.
Document-level relation extraction as semantic segmentation
Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Chuanqi Tan, Mosha Chen, Fei Huang, Luo Si, and Huajun Chen. 2021 · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Coreferential Reasoning Learning for Language Representation
Deming Ye, Yankai Lin, Jiaju Du, Zhenghao Liu, Peng Li, Maosong Sun, and Zhiyuan Liu. 2020 · 2020
Cited alongside, same era.
Double graph based reasoning for document-level relation extraction
Shuang Zeng, Runxin Xu, Baobao Chang, and Lei Li. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Document-level relation extraction with adaptive thresholding and localized context pooling
Wenxuan Zhou, Kevin Huang, Tengyu Ma, and Jing Huang. 2021 · 2021
Later among the works it cites.