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Relation Extraction

Papers (Survey)

  • paper: Pawar, S. , Palshikar, G. K. , & Bhattacharyya, P. . (2017). Relation extraction : a survey.
  • paper: Kumar, S. . (2017). A survey of deep learning methods for relation extraction.

Papers (Various)

  • paper/blog: Shang, Y. M. , Huang, H. , Sun, X. , Wei, W. , & Mao, X. L. . (2022). Relational triple extraction: one step is enough. arXiv
  • paper: Zhong Z. , Chen D. . (2020). A Frustratingly Easy Approach for Joint Entity and Relation Extraction. arXiv
  • paper: Fu, T. J. , & Ma, W. Y. . (2019). GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction. ACL 2019.
  • paper: Zhang, N. , Deng, S. , Sun, Z. , Wang, G. , Chen, X. , Zhang, W. , Chen, H. . (2019). Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks. NAACL 2019.
  • paper: Yu, B. , Zhang, Z. , Shu, X. , Wang, Y. , Liu, T. , & Wang, B. , et al. (2019). Joint extraction of entities and relations based on a novel decomposition strategy. arXiv.
  • paper: Qiu, L. , Zhou, H. , Qu, Y. , Zhang, W. , & Li, S. . (2018). QA4IE: A Question Answering based Framework for Information Extraction. ISWC 2018. Springer, Cham.
  • paper: Han, X. , Yu, P. , Liu, Z. , Sun, M. , Li P. . (2018). Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention. EMNLP 2018.

Papers (Surface Forms)

  • paper: Stanovsky, G. , Michael, J. , Zettlemoyer, L. , Dagan, I. . (2018). Supervised Open Information Extraction. NAACL 2018

Papers about Open Relation Extraction(ORE)

  • paper/code 1/code 2: Soares, L. B. , FitzGerald, N. , Ling, J. , Kwiatkowski T. . (2019). Matching the Blanks: Distributional Similarity for Relation Learning. ACL 2019.
  • paper/code: Wu, R. , Yao, Y. , Han, X. , Xie, R. , & Sun, M. . (2019). Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data. EMNLP-IJCNLP 2019.
  • paper: Jia, S. , Shijia, E. ,. , Li, M. , & Xiang, Y. . (2018). Chinese open relation extraction and knowledge base establishment. ACM Transactions on Asian & Low Resource Language Information Processing, 17(3), 1-22.
  • paper: Roth, B. , Conforti, C. , Poerner, N. , Karn, S. , & Schütze, Hinrich. (2018). Neural architectures for open-type relation argument extraction. Natural Language Engineering.
  • paper: Elsahar, H. , Demidova, E. , Gottschalk, S. , Gravier, C. , & Laforest, F. . (2017). Unsupervised open relation extraction. arXiv.
  • paper: Qiu, L. , & Zhang, Y. . (2014). ZORE: A Syntax-based System for Chinese Open Relation Extraction. EMNLP 2014.
  • paper: Yuen-Hsien Tseng, Lung-Hao Lee, Shu-Yen Lin, Bo-Shun Liao, Mei-Jun Liu, Hsin-Hsi Chen, Oren Etzioni, Anthony Fader (2014). Chinese open relation extraction for knowledge acquisition. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Volume 2: Short Papers. 12-16.
  • spo_extract_platform and knowledge_graph_demo by percent4.

Datasets

  • CORE: Chinese Open Entity-Relation Knowledge Base.

OpenNRE

  • link: https://github.com/thunlp/OpenNRE
  • author: thunlp
  • paper: Han, X. , Gao, T. , Yao, Y. , Ye, D. , Liu, Z. , & Sun, M. . (2019). Opennre: an open and extensible toolkit for neural relation extraction.
  • note: a tensorflow-based framework for neural relation extraction tasks (mainly for English corpus)
  • extra: various NRE-related works could be found under the lists of thunlp groups, such as NRE(C++ approach), JointNRE(Mutual Attention between graph and text), PathNRE(C++ approach), AMNRE(Adversarial Multi-lingual NRE), MNRE(Multi-Language NRE).

Chinese_NRE

  • link: https://github.com/thunlp/Chinese_NRE
  • author: thunlp
  • note: Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge
  • paper: Ziran, L. , Ning, D. , Zhiyuan, L. , Hai-Tao, Z. , & Ying, S. . (2019). Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge. The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019).

Information-Extraction-Chinese

Attention-Based-BiLSTM-relation-extraction

Tacred-Relation

Awesome Relation Extraction

NRE_awesome

NREPapers

BaiDu2019 Relation Extraction Competition

JointRE

Multiple-Relations-Extraction-Only-Look-Once

[Dataset/Competition] FewRel

[Dataset/Competition] DocRED

ChineseNRE

DeepKE

DISTRE

  • link：https://github.com/DFKI-NLP/DISTRE
  • author: DFKI-NLP
  • paper: Alt, C. , Hübner M. , & Hennig, L. . (2019). Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.

GraphRel

Falcon 2.0

One-shot-Relational-Learning

Lifelong Relation Detection

JointRE

BERT-Relation-Extraction

AREkit