2019各顶会中的关系抽取论文呢汇总

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#### 一、ACL 2019
1. **Graph Neural Networks with Generated Parameters for Relation** *Hao Zhu and Yankai Lin and Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun*
https://arxiv.org/pdf/1902.00756.pdf
2. **Entity-Relation Extraction as Multi-turn Question Answering** *Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li Arianna Yuan, Duo Chai, Mingxin Zhou and Jiwei Li*
https://arxiv.org/abs/1905.05529
3. **Matching the Blanks: Distributional Similarity for Relation Learning** *Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, Tom Kwiatkowski*
https://arxiv.org/abs/1905.05529
4. **Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data**
https://arxiv.org/pdf/1906.08931.pdf
5. **GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction** *Tsu-Jui Fu, Peng-Hsuan Li and Wei-Yun Ma*
https://tsujuifu.github.io/pubs/acl19_graph-rel.pdf
6. **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, Maosong Sun*
https://www.aclweb.org/anthology/P19-1074
7. **Attention Guided Graph Convolutional Networks for Relation Extraction** *Zhijiang Guo, Yan Zhang and Wei Lu*
https://www.aclweb.org/anthology/P19-1024.pdf
8. **Neural Relation Extraction for Knowledge Base Enrichment** *Bayu Distiawan Trisedya, Gerhard Weikum, Jianzhong Qi, Rui Zhang*
https://www.aclweb.org/anthology/P19-1023.pdf
9. **Joint Type Inference on Entities and Relations via Graph Convolutional Networks** *Changzhi Sun, Yeyun Gong, Yuanbin Wu, Ming Gong, Daxin Jiang, Man Lan, Shiliang Sun, Nan Duan*
https://pdfs.semanticscholar.org/7ce8/ce2768907421fb1a6cbfe13a8a36992721a7.pdf
二、AAAI 2019
1. **Hybrid Attention-based Prototypical Networks for Noisy Few-Shot Relation Classification** *Tianyu Gao, Xu Han, Zhiyuan Liu, Maosong Sun.*
https://gaotianyu1350.github.io/assets/aaai2019_hatt_paper.pdf
2. **A Hierarchical Framework for Relation Extraction with Reinforcement Learning** *Takanobu, Ryuichi and Zhang, Tianyang and Liu, Jiexi and Huang, Minlie*
https://arxiv.org/pdf/1811.03925.pdf
3. **Kernelized Hashcode Representations for Biomedical Relation Extraction** *Sahil Garg, Aram Galstyan, Greg Ver Steeg Irina Rish, Guillermo Cecchi, Shuyang Gao*
https://arxiv.org/pdf/1711.04044.pdf
4. **Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction** *Yujin Yuan, Liyuan Liu, Siliang Tang, Zhongfei Zhang, Yueting Zhuang, Shiliang Pu, Fei Wu, Xiang Ren*
https://arxiv.org/pdf/1812.10604.pdf
#### 三、NAACL 2019
1. **Structured Minimally Supervised Learning for Neural Relation Extraction** *Fan Bai and Alan Ritter*
https://arxiv.org/pdf/1904.00118.pdf
2. **Combining Distant and Direct Supervision for Neural Relation Extraction** *Iz Beltagy, Kyle Lo and Waleed Ammar*
https://arxiv.org/pdf/1810.12956.pdf
3. **Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions** *Ye, Zhi-Xiu and Ling, Zhen-Hua*
https://www.aclweb.org/anthology/N19-1288
4. **A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction** *Duy-Cat Can, Hoang-Quynh Le, Quang-Thuy Ha, Nigel Collier*
https://www.aclweb.org/anthology/N19-1298
5. **Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction** *Peng Xu and Denilson Barbosa*
https://arxiv.org/abs/1903.10126
6. **GAN Driven Semi-distant Supervision for Relation Extraction** *Pengshuai Li, Xinsong Zhang, Weijia Jia, Hai Zhao*
https://www.aclweb.org/anthology/N19-1307
7. **Exploiting Noisy Data in Distant Supervision Relation Classification** *Kaijia Yang, Liang He, Xin-yu Dai, Shujian Huang, Jiajun Chen*
https://www.aclweb.org/anthology/N19-1325
8. **Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks** *Ningyu Zhang, Shumin Deng, Zhanlin Sun,Guanying Wang, Xi Chen, Wei Zhang, Huajun Chen*
https://www.aclweb.org/anthology/N19-1306
#### Reference
https://github.com/WindChimeRan/NREPapers2019