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Must-read papers on neural relation extraction (NRE)

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Must-read papers on NRE

NRE: Neural Relation Extraction.

Contributed by Tianyu Gao and Xu Han.

We released OpenNRE, an open-source framework for neural relation extraction. This repository provides several relation extraction methods and an easy-to-use training and testing framework.

Reviews

  1. Nguyen Bach, Sameer Badaskar. A review of relation extraction. [paper]
  2. Shantanu Kumar. 2017. A survey of deep learning methods for relation extraction. [paper]
  3. Sachin Pawar, Girish K. Palshikara, Pushpak Bhattacharyyab. 2017. Relation extraction: a survey. [paper]

Datasets

You can download most of the following datasets in json format from OpenNRE.

Sentence-Level Relation Extraction

  1. ACE 2005 Dataset. [link] [paper]
  2. SemEval-2010 Task 8 Dataset. [link] [paper]
  3. TACREDD. [link] [paper]

Distantly Supervised Relation Extraction Datasets

  1. NYT Dataset. [link] [paper]

Few-shot Relation Extraction Datasets

  1. FewRel. [link] [1.0 paper] [2.0 paper]

Document-Level Relation Extraction Datasets

  1. DocRED. [link] [paper]

Papers

Pattern-Based Methods

  1. Stephen Soderland, David Fisher, Jonathan Aseltine, and Wendy Lehnert. 1995. Crystal inducing a conceptual dictionary. In Proceedings of IJCAI. [paper]
  2. Jun-Tae Kim and Dan I. Moldovan. 1995. Acquisition of linguistic patterns for knowledge-based information extraction. TKDE. [paper]
  3. Scott B Huffman. 1995. Learning information extraction patterns from examples. In Proceedings of IJCAI. [paper]
  4. Mary Elaine Califf and Raymond J. Mooney. 1997. Relational learning of pattern-match rules for information extraction. In Proceedings of CoNLL. [paper]
  5. Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R Hruschka, and Tom M Mitchell. 2010. Toward an architecture for never-ending language learning. In Proceedings of AAAI. [paper]
  6. Ndapandula Nakashole, Gerhard Weikum, and Fabian Suchanek. 2012. PATTY: A taxonomy of relational patterns with semantic types. In Proceedings of EMNLP-CoNLL. [paper]
  7. Shun Zheng, Xu Han, Yankai Lin, Peilin Yu, Lu Chen, Ling Huang, Zhiyuan Liu, and Wei Xu. 2019. DIAG-NRE: A neural pattern diagnosis framework for distantly supervised neural relation extraction. In Proceedings of ACL. [paper]

Statistical Methods

Feature-Based

  1. Nanda Kambhatla. 2004. Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations. [paper]
  2. Guodong Zhou, Jian Su, Jie Zhang, and Min Zhang. 2005. Exploring various knowledge in relation extraction. In Proceedings of ACL, pages 427–434. [paper]
  3. Jing Jiang and ChengXiang Zhai. 2007. A systematic exploration of the feature space for relation extraction. In Proceedings of NAACL, pages 113–120. [paper]
  4. Dat PT Nguyen, Yutaka Matsuo, and Mitsuru Ishizuka. 2007. Relation extraction from wikipedia using subtree mining. In Proceedings of AAAI, pages 1414–1420. [paper]

Kernel-Based

  1. Aron Culotta and Jeffrey Sorensen. 2004. Dependency tree kernels for relation extraction. In Proceedings of ACL, page 423. [paper]
  2. Razvan C Bunescu and Raymond J Mooney. 2005. A shortest path dependency kernel for relation extraction. In Proceedings of EMNLP, pages 724–731. [paper]
  3. Shubin Zhao and Ralph Grishman. 2005. Extracting relations with integrated information using kernel methods. In Proceedings of ACL, pages 419–426. [paper]
  4. Raymond J Mooney and Razvan C Bunescu. 2006. Subsequence kernels for relation extraction. In Proceedings of NIPS, pages 171–178. [paper]
  5. Min Zhang, Jie Zhang, Jian Su, and Guodong Zhou. 2006. A composite kernel to extract relations between entities with both flat and structured features. In Proceedings of ACL, pages 825–832. [paper]
  6. Mengqiu Wang. 2008. A re-examination of dependency path kernels for relation extraction. In Proceedings of IJCNLP, pages 841–846. [paper]

Graphical Models

  1. Dan Roth and Wen-tau Yih. 2002. Probabilistic reasoning for entity & relation recognition. In Proceedings of COLING. [paper]
  2. Sunita Sarawagi and William W Cohen. 2005. Semimarkov conditional random fields for information extraction. In Proceedings of NIPS, pages 1185–1192. [paper]
  3. Xiaofeng Yu and Wai Lam. 2010. Jointly identifying entities and extracting relations in encyclopedia text via a graphical model approach. In Proceedings of ACL, pages 1399–1407. [paper]

Embedding Models

  1. Jason Weston, Antoine Bordes, Oksana Yakhnenko, and Nicolas Usunier. 2013. Connecting language and knowledge bases with embedding models for relation extraction. In Proceedings of EMNLP, pages 1366–1371. [paper]
  2. Sebastian Riedel, Limin Yao, Andrew McCallum, and Benjamin M Marlin. 2013. Relation extraction with matrix factorization and universal schemas. In Proceedings of NAACL, pages 74–84. [paper]
  3. Matthew R Gormley, Mo Yu, and Mark Dredze. 2015. Improved relation extraction with feature-rich compositional embedding models. In Proceedings of EMNLP, pages 1774–1784. [paper]
  4. Antoine Bordes, Nicolas Usunier, Alberto Garcia- Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multirelational data. In Proceedings of NIPS, pages 2787– 2795. [paper]
  5. Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge graph embedding by translating on hyperplanes. In Proceedings of AAAI. [paper]
  6. Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of AAAI. [paper]

Neural Methods

Recursive Neural Networks

  1. Richard Socher, Brody Huval, Christopher D Manning, and Andrew Y Ng. 2012. Semantic compositionality through recursive matrix-vector spaces. In Proceedings of EMNLP, pages 1201–1211. [paper]
  2. Makoto Miwa and Mohit Bansal. 2016. End-to-end relation extraction using lstms on sequences and tree structures. In Proceedings of ACL, pages 1105–1116. [paper]

Convolutional Neural Networks

  1. Chunyang Liu, Wenbo Sun, Wenhan Chao, and Wanxiang Che. 2013. Convolution neural network for relation extraction. In Proceedings of ICDM, pages 231–242. [paper]
  2. Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, and Jun Zhao. 2014. Relation classification via convolutional deep neural network. In Proceedings of COLING, pages 2335–2344. [paper]
  3. Cicero Nogueira dos Santos, Bing Xiang, and Bowen Zhou. 2015. Classifying relations by ranking with convolutional neural networks. In Proceedings of ACL-IJCNLP, pages 626–634. [paper]
  4. Thien Huu Nguyen and Ralph Grishman. 2015. Relation extraction: Perspective from convolutional neural networks. In Proceedings of the NAACL Workshop on Vector Space Modeling for NLP, pages 39–48. [paper]

Recurrent Neural Networks

  1. Dongxu Zhang and Dong Wang. 2015. Relation classification via recurrent neural network. arXiv preprint arXiv:1508.01006. [paper]
  2. Thien Huu Nguyen and Ralph Grishman. 2015. Combining neural networks and log-linear models to improve relation extraction. arXiv preprint arXiv:1511.05926. [paper]
  3. Ngoc Thang Vu, Heike Adel, Pankaj Gupta, et al. 2016. Combining recurrent and convolutional neural networks for relation classification. In Proceedings of NAACL, pages 534–539. [paper]
  4. Shu Zhang, Dequan Zheng, Xinchen Hu, and Ming Yang. 2015. Bidirectional long short-term memory networks for relation classification. In Proceedings of PACLIC, pages 73–78. [paper]

Graph Neural Networks

  1. Yuhao Zhang, Peng Qi, and Christopher D. Manning. 2018. Graph convolution over pruned dependency trees improves relation extraction. In Proceedings of EMNLP, pages 2205–2215. [paper]
  2. Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-Seng Chua, and Maosong Sun. 2019. Graph neural networks with generated parameters for relation extraction. In Proceedings of ACL, pages 1331–1339. [paper]
  3. Zhijing Jin, Yongyi Yang, Xipeng Qiu, Zheng Zhang. 2020. Relation of the Relations: A New Paradigm of the Relation Extraction Problem. In Proceedings of EMNLP. [paper]

Attention

  1. Peng Zhou, Wei Shi, Jun Tian, Zhenyu Qi, Bingchen Li, Hongwei Hao, and Bo Xu. 2016. Attention-based bidirectional long short-term memory networks for relation classification. In Proceedings of ACL, pages 207–212. [paper]
  2. Linlin Wang, Zhu Cao, Gerard De Melo, and Zhiyuan Liu. 2016. Relation classification via multi-level attention cnns. In Proceedings of ACL, pages 1298–1307. [paper]
  3. Minguang Xiao and Cong Liu. 2016. Semantic relation classification via hierarchical recurrent neural network with attention. In Proceedings of COLING, pages 1254–1263. [paper]

Word & Position Embedding

  1. Joseph Turian, Lev Ratinov, and Yoshua Bengio. 2010. Word representations: a simple and general method for semi-supervised learning. In Proceedings of ACL, pages 384–394. [paper]
  2. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of NIPS, pages 3111–3119. [paper]
  3. Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, and Jun Zhao. 2014. Relation classification via convolutional deep neural network. In Proceedings of COLING, pages 2335–2344. [paper]

Shortest Dependency Path

  1. Yang Liu, Furu Wei, Sujian Li, Heng Ji, Ming Zhou, and WANG Houfeng. 2015. A dependency-based neural network for relation classification. In Proceedings of ACL-IJCNLP, pages 285–290. [paper]
  2. Yan Xu, Lili Mou, Ge Li, Yunchuan Chen, Hao Peng, and Zhi Jin. 2015. Classifying relations via long short term memory networks along shortest dependency paths. In Proceedings of EMNLP, pages 1785–1794. [paper]

Universal Schema

  1. Patrick Verga, David Belanger, Emma Strubell, Benjamin Roth, and Andrew McCallum. 2016. Multilingual relation extraction using compositional universal schema. In Proceedings of NAACL, pages 886– 896. [paper]
  2. Patrick Verga and Andrew McCallum. 2016. Row-less universal schema. In Proceedings of ACL, pages 63– 68. [paper]
  3. Sebastian Riedel, Limin Yao, Andrew McCallum, and Benjamin M Marlin. 2013. Relation extraction with matrix factorization and universal schemas. In Proceedings of NAACL, pages 74–84. [paper]

Transformer and BERT

  1. Jinhua Du, Jingguang Han, Andy Way, and Dadong Wan. 2018. Multi-level structured self-attentions for distantly supervised relation extraction. In Proceedings of EMNLP, pages 2216–2225. [paper]
  2. Patrick Verga, Emma Strubell, and Andrew McCallum. 2018. Simultaneously self-attending to all mentions for full-abstract biological relation extraction. In Proceedings of NAACL-HLT, pages 872–884. [paper]
  3. Shanchan Wu and Yifan He. 2019. Enriching pre-trained language model with entity information for relation classification. arXiv preprint arXiv:1905.08284. [paper]
  4. Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski. 2019. Matching the blanks: Distributional similarity for relation learning. In Proceedings of ACL, pages 2895–2905. [paper]

Distant Supervision

  1. Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. 2009. Distant supervision for relation extraction without labeled data. In Proceedings of ACLIJCNLP, pages 1003–1011. [paper]
  2. Truc-Vien T Nguyen and Alessandro Moschitti. 2011. End-to-end relation extraction using distant supervision from external semantic repositories. In Proceedings of ACL, pages 277–282. [paper]
  3. Bonan Min, Ralph Grishman, Li Wan, Chang Wang, and David Gondek. 2013. Distant supervision for relation extraction with an incomplete knowledge base. In Proceedings of NAACL, pages 777–782. [paper]

Selecting Informative Instances

  1. Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010. Modeling relations and their mentions without labeled text. In Proceedings of ECML-PKDD, pages 148–163. [paper]
  2. Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, and Daniel S Weld. 2011. Knowledgebased weak supervision for information extraction of overlapping relations. In Proceedings of ACL, pages 541–550. [paper]
  3. Mihai Surdeanu, Julie Tibshirani, Ramesh Nallapati, and Christopher D Manning. 2012. Multi-instance multi-label learning for relation extraction. In Proceedings of EMNLP, pages 455–465. [paper]
  4. Daojian Zeng, Kang Liu, Yubo Chen, and Jun Zhao. 2015. Distant supervision for relation extraction via piecewise convolutional neural networks. In Proceedings of EMNLP, pages 1753–1762. [paper]
  5. Yankai Lin, Shiqi Shen, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. 2016. Neural relation extraction with selective attention over instances. In Proceedings of ACL, pages 2124–2133. [paper]
  6. Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D Manning. 2017. Positionaware attention and supervised data improve slot filling. In Proceedings of EMNLP, pages 35–45. [paper]
  7. Xu Han, Pengfei Yu, Zhiyuan Liu, Maosong Sun, and Peng Li. 2018c. Hierarchical relation extraction with coarse-to-fine grained attention. In Proceedings of EMNLP, pages 2236–2245. [paper]
  8. Yang Li, Guodong Long, Tao Shen, Tianyi Zhou, Lina Yao, Huan Huo, and Jing Jiang. 2019. Self attention enhanced selective gate with entity-aware embedding for distantly supervised relation extraction. arXiv preprint arXiv:1911.11899. [paper]
  9. Linmei Hu, Luhao Zhang, Chuan Shi, Liqiang Nie, Weili Guan, and Cheng Yang. 2019. Improving distantly-supervised relation extraction with joint label embedding. In Proceedings of EMNLP-IJCNLP, pages 3812–3820. [paper]

Incorporating Extra Context

  1. Guoliang Ji, Kang Liu, Shizhu He, Jun Zhao, et al. 2017. Distant supervision for relation extraction with sentence-level attention and entity descriptions. In AAAI, pages 3060–3066. [paper]
  2. Xu Han, Zhiyuan Liu, and Maosong Sun. 2018b. Neural knowledge acquisition via mutual attention between knowledge graph and text. In Proceedings of AAAI. [paper]
  3. Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, and Huajun Chen. 2019. Long-tail relation extraction via knowledge graph embeddings and graph convolution networks. In Proceedings of NAACL-HLT, pages 3016–3025. [paper]
  4. Jianfeng Qu, Wen Hua, Dantong Ouyang, Xiaofang Zhou, and Ximing Li. 2019. A fine-grained and noise-aware method for neural relation extraction. In Proceedings of CIKM, pages 659–668. [paper]
  5. Patrick Verga, David Belanger, Emma Strubell, Benjamin Roth, and Andrew McCallum. 2016. Multilingual relation extraction using compositional universal schema. In Proceedings of NAACL, pages 886–896. [paper]
  6. Yankai Lin, Zhiyuan Liu, and Maosong Sun. 2017. Neural relation extraction with multi-lingual attention. In Proceedings of ACL, pages 34–43. [paper]
  7. Xiaozhi Wang, Xu Han, Yankai Lin, Zhiyuan Liu, and Maosong Sun. 2018. Adversarial multi-lingual neural relation extraction. In Proceedings of COLING, pages 1156–1166. [paper]

Sophisticated Mechanisms

  1. Ngoc Thang Vu, Heike Adel, Pankaj Gupta, et al. 2016. Combining recurrent and convolutional neural networks for relation classification. In Proceedings of NAACL, pages 534–539. [paper]
  2. Iz Beltagy, Kyle Lo, and Waleed Ammar. 2019. Combining distant and direct supervision for neural relation extraction. In Proceedings of NAACL-HLT, pages 1858–1867. [paper]
  3. Tianyu Liu, Kexiang Wang, Baobao Chang, and Zhifang Sui. 2017. A soft-label method for noisetolerant distantly supervised relation extraction. In Proceedings of EMNLP, pages 1790–1795. [paper]
  4. Jun Feng, Minlie Huang, Li Zhao, Yang Yang, and Xiaoyan Zhu. 2018. Reinforcement learning for relation classification from noisy data. In Proceedings of AAAI, pages 5779–5786. [paper]
  5. Xiangrong Zeng, Shizhu He, Kang Liu, and Jun Zhao. 2018. Large scaled relation extraction with reinforcement learning. In Proceedings of AAAI, pages 5658–5665. [paper]
  6. Yi Wu, David Bamman, and Stuart Russell. 2017. Adversarial training for relation extraction. In Proceeding of EMNLP, pages 1778–1783. [paper]
  7. Xu Han, Zhiyuan Liu, and Maosong Sun. 2018. Denoising distant supervision for relation extraction via instance-level adversarial training. arXiv preprint arXiv:1805.10959. [paper]

Few-Shot Learning

  1. Xu Han, Hao Zhu, Pengfei Yu, ZiyunWang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018d. Fewrel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In Proceedings of EMNLP, pages 4803–4809. [paper]
  2. Tianyu Gao, Xu Han, Hao Zhu, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2019. FewRel 2.0: Towards more challenging few-shot relation classification. In Proceedings of EMNLP-IJCNLP, pages 6251–6256. [paper]
  3. Tianyu Gao, Xu Han, Zhiyuan Liu, Maosong Sun. 2019. Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification. In Proceedings of AAAI. [paper]
  4. Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski. 2019. Matching the blanks: Distributional similarity for relation learning. In Proceedings of ACL, pages 2895–2905. [paper]
  5. Zhi-Xiu Ye and Zhen-Hua Ling. 2019. Multi-level matching and aggregation network for few-shot relation classification. In Proceedings of ACL, pages 2872–2881. [paper]

Document-Level Relation Extraction

  1. Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, and Maosong Sun. 2019. DocRED: A large-scale document-level relation extraction dataset. In Proceedings of ACL, pages 764–777. [paper]
  2. Michael Wick, Aron Culotta, et al. 2006. Learning field compatibilities to extract database records from unstructured text. In Proceedings of EMNLP. [paper]
  3. Matthew Gerber and Joyce Chai. 2010. Beyond Nom-Bank: A study of implicit arguments for nominal predicates. In Proceedings of ACL, pages 1583–1592. [paper]
  4. Kumutha Swampillai and Mark Stevenson. 2011. Extracting relations within and across sentences. In Proceedings of RANLP. [paper]
  5. Katsumasa Yoshikawa, Sebastian Riedel, et al. 2011. Coreference based event-argument relation extraction on biomedical text. J. Biomed. Semant. [paper]
  6. Chris Quirk and Hoifung Poon. 2017. Distant supervision for relation extraction beyond the sentence boundary. In Proceedings of EACL, pages 1171–1182. [paper]
  7. Wenyuan Zeng, Yankai Lin, Zhiyuan Liu, and Maosong Sun. 2017. Incorporating relation paths in neural relation extraction. In Proceedings of EMNLP, pages 1768–1777. [paper]
  8. Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou. 2018. A walk-based model on entity graphs for relation extraction. In Proceedings of ACL, pages 81–88. [paper]
  9. Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, and Wen-tau Yih. 2017. Cross-sentence n-ary relation extraction with graph LSTMs. TACL, 5:101–115. [paper]
  10. Linfeng Song, Yue Zhang, et al. 2018. N-ary relation extraction using graph-state lstm. In Proceedings of EMNLP. [paper]
  11. Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-Seng Chua, and Maosong Sun. 2019. Graph neural networks with generated parameters for relation extraction. In Proceedings of ACL, pages 1331–1339. [paper]

Open Information Extraction

  1. Michele Banko, Michael J Cafarella, Stephen Soderland, Matthew Broadhead, and Oren Etzioni. 2007. Open information extraction from the web. In Proceedings of IJCAI, pages 2670–2676. [paper]
  2. Anthony Fader, Stephen Soderland, and Oren Etzioni. 2011. Identifying relations for open information extraction. In Proceedings of EMNLP, pages 1535–1545. [paper]
  3. Mausam, Michael Schmitz, Stephen Soderland, Robert Bart, and Oren Etzioni. 2012. Open language learning for information extraction. In Proceedings of EMNLP-CoNLL, pages 523–534. [paper]
  4. Luciano Del Corro and Rainer Gemulla. 2013. Clausie: clause-based open information extraction. In Proceedings of WWW, pages 355–366. [paper]
  5. Gabor Angeli, Melvin Jose Johnson Premkumar, and Christopher D. Manning. 2015. Leveraging linguistic structure for open domain information extraction. In Proceedings of ACL-IJCNLP, pages 344–354. [paper]
  6. Gabriel Stanovsky and Ido Dagan. 2016. Creating a large benchmark for open information extraction. In Proceedings of EMNLP, pages 2300–2305. [paper]
  7. Mausam Mausam. 2016. Open information extraction systems and downstream applications. In Proceedings of IJCAI, pages 4074–4077. [paper] 1. Lei Cui, Furu Wei, and Ming Zhou. 2018. Neural open information extraction. In Proceedings of ACL, pages 407–413. [paper]

Relation Discovery

  1. Limin Yao, Aria Haghighi, Sebastian Riedel, and Andrew McCallum. 2011. Structured relation discovery using generative models. In Proceedings of EMNLP, pages 1456–1466. [paper]
  2. Diego Marcheggiani and Ivan Titov. 2016. Discretestate variational autoencoders for joint discovery and factorization of relations. TACL, 4:231–244. [paper]
  3. Yusuke Shinyama and Satoshi Sekine. 2006. Preemptive information extraction using unrestricted relation discovery. In Proceedings of NAACL, pages 304–311. [paper]
  4. Hady Elsahar, Elena Demidova, Simon Gottschalk, Christophe Gravier, and Frederique Laforest. 2017. Unsupervised open relation extraction. In Proceedings of ESWC, pages 12–16. [paper]
  5. Ruidong Wu, Yuan Yao, Xu Han, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, and Maosong Sun. 2019. Open relation extraction: Relational knowledge transfer from supervised data to unsupervised data. In Proceedings of EMNLP-IJCNLP, pages 219–228. [paper]

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Must-read papers on neural relation extraction (NRE)

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