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Merge pull request #18 from nicolay-r/gnn-based-models
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Updating list of GNN-based models
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roomylee authored Mar 30, 2021
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Expand Up @@ -97,10 +97,25 @@ A curated list of awesome resources dedicated to Relation Extraction, inspired b
* Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning [[paper]](https://arxiv.org/abs/1808.06738)
* Tianyi Liu, Xinsong Zhang, Wanhao Zhou, Weijia Jia
* EMNLP 2018

#### GNN-based Models
* Matching the Blanks: Distributional Similarity for Relation Learning [[paper]](https://arxiv.org/abs/1906.03158)
* Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, Tom Kwiatkowski
* ACL 2019
* Relation of the Relations: A New Paradigm of the Relation Extraction Problem [[paper]](https://arxiv.org/abs/2006.03719)
* Zhijing Jin, Yongyi Yang, Xipeng Qiu, Zheng Zhang
* EMNLP 2020
* GDPNet: Refining Latent Multi-View Graph for Relation Extraction
[[paper]](https://arxiv.org/abs/2012.06780.pdf)
[[code]](https://github.com/XueFuzhao/GDPNet)
* Fuzhao Xue, Aixin Sun, Hao Zhang, Eng Siong Chng
* AAAI 21
* RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network
[[parer]](https://arxiv.org/abs/2009.08694.pdf)
[[code]](https://github.com/ansonb/RECON)
* Anson Bastos, Abhishek Nadgeri, Kuldeep Singh, Isaiah Onando Mulang', Saeedeh Shekarpour, Johannes Hoffart, Manohar Kaul
* WWW'21

### Distant Supervision Approaches
* Distant supervision for relation extraction without labeled data [[paper]](https://web.stanford.edu/~jurafsky/mintz.pdf) [[review]](https://github.com/roomylee/paper-review/blob/master/relation_extraction/Distant_supervision_for_relation_extraction_without_labeled_data/review.md)
* Mike Mintz, Steven Bills, Rion Snow and Dan Jurafsky
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* EMNLP 2018

### Language Models
* Enriching Pre-trained Language Model with Entity Information for Relation Classification [[paper]](https://arxiv.org/pdf/1905.08284.pdf)
* Enriching Pre-trained Language Model with Entity Information for Relation Classification [[paper]](https://arxiv.org/abs/1905.08284.pdf)
* Shanchan Wu, Yifan He
* arXiv 2019
* SpanBERT: Improving pre-training by representing and predicting spans [[paper]](https://arxiv.org/pdf/1907.10529.pdf) [[code]](https://github.com/facebookresearch/SpanBERT)
* SpanBERT: Improving pre-training by representing and predicting spans [[paper]](https://arxiv.org/abs/1907.10529.pdf) [[code]](https://github.com/facebookresearch/SpanBERT)
* Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer and Omer Levy
* Transactions of the Association for Computational Linguistics 2020

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