Fetching the paper…
Reading the bibliography…
Distantly supervised relation extraction is widely used to extract relational facts from text, but suffers from noisy labels.
A Shortest Path Dependency Kernel for Relation Extraction
Razvan C. Bunescu and Raymond J. Mooney. 2005 · 2005
Earlier work this paper cites.
Distant supervision for relation extraction without labeled data
Mike Mintz, Steven Bills, Rion Snow, and Daniel Jurafsky. 2009 · 2009
Earlier work this paper cites.
Semeval-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals
Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid Ó Séaghdha, Sebastian Padó, Marco Pennacchiotti, Lorenza Romano, and Stan Szpakowicz. 2010 · 2010
Earlier work this paper cites.
Modeling Relations and Their Mentions without Labeled Text
Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010 · 2010
Earlier work this paper cites.
Word representations: A simple and general method for semi-supervised learning
Joseph P. Turian, Lev-Arie Ratinov, and Yoshua Bengio. 2010 · 2010
Earlier work this paper cites.
Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations
Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, and Daniel S. Weld. 2011 · 2011
Earlier work this paper cites.
Knowledge base population: Successful approaches and challenges
Heng Ji and Ralph Grishman. 2011 · 2011
Earlier work this paper cites.
Semantic compositionality through recursive matrix-vector spaces
Richard Socher, Brody Huval, Christopher D. Manning, and Andrew Y. Ng. 2012 · 2012
Earlier work this paper cites.
Multi-instance Multi-label Learning for Relation Extraction
Mihai Surdeanu, Julie Tibshirani, Ramesh Nallapati, and Christopher D. Manning. 2012 · 2012
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
Distant Supervision for Relation Extraction with an Incomplete Knowledge Base
Bonan Min, Ralph Grishman, Li Wan, Chang Wang, and David Gondek. 2013 · 2013
Earlier work this paper cites.
Distant Supervision for Relation Extraction with Matrix Completion
Miao Fan, Deli Zhao, Qiang Zhou, Zhiyuan Liu, Thomas Fang Zheng, and Edward Y. Chang. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Earlier work this paper cites.
Grounded compositional semantics for finding and describing images with sentences
Richard Socher, Andrej Karpathy, Quoc V. Le, Christopher D. Manning, and Andrew Y. Ng. 2014 · 2014
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.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Cited alongside, same era.
Classifying relations via long short term memory networks along shortest dependency paths
Yan Xu, Lili Mou, Ge Li, Yunchuan Chen, Hao Peng, and Zhi Jin. 2015b · 2015
Cited alongside, same era.
Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
Daojian Zeng, Kang Liu, Yubo Chen, and Jun Zhao. 2015 · 2015
Cited alongside, same era.
Relation classification via recurrent neural network
Dongxu Zhang and Dong Wang. 2015 · 2015
Cited alongside, same era.
Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Hierarchical relation extraction with coarse-to-fine grained attention
Xu Han, Pengfei Yu, Zhiyuan Liu, Maosong Sun, and Peng Li. 2018 · 2018
Later among the works it cites.
See: Syntax-aware entity embedding for neural relation extraction
Zhengqiu He, Wenliang Chen, Zhenghua Li, Meishan Zhang, Wei Zhang, and Min Zhang. 2018 · 2018
Later among the works it cites.
Neural relation extraction via inner-sentence noise reduction and transfer learning
Tianyi Liu, Xinsong Zhang, Wanhao Zhou, and Weijia Jia. 2018b · 2018
Later among the works it cites.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke S. Zettlemoyer. 2018 · 2018
Later among the works it cites.
Dsgan: Generative adversarial training for distant supervision relation extraction
Pengda Qin, Weiran XU, and William Yang Wang. 2018 · 2018
Later among the works it cites.
Improving language understanding by generative pre-training
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Neural Relation Extraction with Selective Attention over Instances
Yankai Lin, Shiqi Shen, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. 2016 · 2016
Cited alongside, same era.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Cited alongside, same era.
A soft-label method for noise-tolerant distantly supervised relation extraction
Tianyu Liu, Kexiang Wang, Baobao Chang, and Zhifang Sui. 2017 · 2017
Cited alongside, same era.
Learning with noise: Enhance distantly supervised relation extraction with dynamic transition matrix
Bingfeng Luo, Yansong Feng, Zheng Wang, Zhanxing Zhu, Songfang Huang, Rui Yan, and Dongyan Zhao. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Adversarial training for relation extraction
Yi Wu, David Bamman, and Stuart Russell. 2017 · 2017
Cited alongside, same era.
Noise mitigation for neural entity typing and relation extraction
Yadollah Yaghoobzadeh, Heike Adel, and Hinrich Schütze. 2017 · 2017
Cited alongside, same era.
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Later among the works it cites.
Universal language model fine-tuning for text classification
Sebastian Ruder and Jeremy Howard. 2018 · 2018
Later among the works it cites.
Reside: Improving distantly-supervised neural relation extraction using side information
Shikhar Vashishth, Rishabh Joshi, Sai Suman Prayaga, Chiranjib Bhattacharyya, and Partha Talukdar. 2018 · 2018
Later among the works it cites.
Simultaneously self-attending to all mentions for full-abstract biological relation extraction
Patrick Verga, Emma Strubell, and Andrew McCallum. 2018 · 2018
Later among the works it cites.
Label-free distant supervision for relation extraction via knowledge graph embedding
Guanying Wang, Wen Zhang, Ruoxu Wang, Yalin Zhou, Xi Chen, Wei Zhang, Hai Zhu, and Huajun Chen. 2018 · 2018
Later among the works it cites.
Attention-based capsule networks with dynamic routing for relation extraction
Ningyu Zhang, Shumin Deng, Zhanling Sun, Xi Chen, Wei Zhang, and Huajun Chen. 2018a · 2018
Later among the works it cites.
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
Yuhao Zhang, Peng Qi, and Christopher D. Manning. 2018b · 2018
Later among the works it cites.
Improving relation extraction by pre-trained language representations
Christoph Alt, Marc Hübner, and Leonhard Hennig. 2019 · 2019
Closest in time.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Closest in time.