DeepIE/docs/2019各顶会中的关系抽取论文]汇总.md

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