Skip to main content
SpringerLink
Log in

ESRE: handling repeated entities in distant supervised relation extraction

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Distant supervised relation extraction has been widely used to find novel relational facts from unstructured text. As far as we know, nearly all existing relation extraction models assume that each sentence contains precisely one entity pair, i.e., two entities. However, in reality, the datasets constructed by distant supervision have lots of sentences which contain repeated entities. In other words, there may be more than two entities in a sentence. This phenomenon breaks the assumption of existing models and makes them inevitably encounter the attention bias problem; that is, the model focuses on the wrong entities during relation extraction. To alleviate this problem, in this paper, we utilize the idea of ensemble learning and propose a novel distant supervised relation extraction model. The proposed model follows the multi-instance multi-label learning mechanism and conducts relation extraction based on the sentence-bag representations. Specifically, it first tries to identify the most critical entity and keywords, and then it uses voting mechanism to determine the sentence-level and bag-level features. Experimental results show that our proposed model outperforms the state-of-the-art baselines in relation extraction on a popular benchmark dataset, and it also indicates that the proposed model can indeed alleviate the problem caused by repeated-entity phenomenon.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. https://github.com/yuanyu255/PCNN C2SA.

References

  1. Agichtein E, Gravano L (2000) Snowball: extracting relations from large plain-text collections. In: Proceedings of the fifth ACM conference on digital libraries. ACM, New York, pp 85–94

  2. Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J, (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD international conference on Management of data. ACM, pp 1247–1250

  3. Brin S (1998) Extracting patterns and relations from the world wide web. In: International workshop on the world wide web and databases. Springer, Berlin, pp 172–183

  4. Culotta A, Sorensen JS (2004) Dependency tree kernels for relation extraction. In: Meeting on association for computational linguistics

  5. Dietterich TG (2000) Ensemble methods in machine learning. In: International workshop on multiple classifier systems. Springer, Berlin, pp 1–15

  6. Feng J, Huang M, Zhao L, Yang Y, Zhu X (2018) Reinforcement learning for relation classification from noisy data. In: Proceedings of AAAI

  7. Hoffmann R, Zhang C, Ling X, Zettlemoyer LS, Weld DS (2011) Knowledge-based weak supervision for information extraction of overlapping relations. In: Meeting of the association for computational linguistics: human language technologies

  8. Ji G, Liu K, He S, Zhao J et al (2017) Distant supervision for relation extraction with sentence-level attention and entity descriptions. In: AAAI, pp 3060–3066

  9. Kim Y (2014) Convolutional neural networks for sentence classification, pp 1746–1751

  10. Lin Y, Shen S, Liu Z, Luan H, Sun M (2016) Neural relation extraction with selective attention over instances. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: Long Papers), pp 2124–2133

  11. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. Comput Sci

  12. Mintz M, Bills S, Snow R, Jurafsky D (2009) Distant supervision for relation extraction without labeled data. In: Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP: volume 2–volume 2. Association for Computational Linguistics, pp 1003–1011

  13. Miwa M, Bansal M (2016) End-to-end relation extraction using lstms on sequences and tree structures. arXiv:1601.00770

  14. Qin P, Xu W, Wang WY (2018) Robust distant supervision relation extraction via deep reinforcement learning. In: Proceedings of the 56th annual meeting of the association for computational linguistics (volume 1: long papers), pp 2137–2147

  15. Riedel S, Yao L, McCallum A (2010) Modeling relations and their mentions without labeled text. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, Berlin, pp 148–163

  16. Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. Adv Neural Inf Process Syst 30:3856–3866

    Google Scholar 

  17. Sadeghi F, Kumar Divvala SK, Farhadi A (2015) Viske: visual knowledge extraction and question answering by visual verification of relation phrases. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1456–1464

  18. Shore J, Johnson R (1980) Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Trans Inf Theory 26:26–37

    Article  MathSciNet  Google Scholar 

  19. Su S, Jia N, Cheng X , Zhu S, Li R (2018) Exploring encoder-decoder model for distant supervised relation extraction. In: IJCAI, pp 4389–4395

  20. Surdeanu M, Tibshirani J, Nallapati R, Manning CD (2012) Multi-instance multi-label learning for relation extraction. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. Association for Computational Linguistics, pp 455–465

  21. Yan Y, Okazaki N, Matsuo Y, Yang Z, Ishizuka M (2009) Unsupervised relation extraction by mining wikipedia texts using information from the web. In: Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP: volume 2–volume 2. Association for Computational Linguistics, pp 1021–1029

  22. Ye ZX, Ling ZH (2019) Distant supervision relation extraction with intra-bag and inter-bag attentions. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers). Association for Computational Linguistics, Minneapolis, pp 2810–2819

  23. Yuan C, Huang H, Feng C, Liu X, Wei X (2019a) Distant supervision for relation extraction with linear attenuation simulation and non–iid relevance embedding. In: National conference on artificial intelligence

  24. Yuan Y, Liu L, Tang S, Zhang Z, Zhuang Y, Pu S, Wu F, Ren X (2019b) Cross-relation cross-bag attention for distantly-supervised relation extraction. In: Proceedings of the AAAI conference on artificial intelligence, pp 419–426

  25. Zeng D, Liu K, Chen Y, Zhao J (2015) Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 1753–1762

  26. Zeng D, Liu K, Lai S, Zhou G, Zhao J (2014) Relation classification via convolutional deep neural network. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pp 2335–2344

  27. Zhou G, Su J, Zhang J, Zhang M (2002) Exploring various knowledge in relation extraction. In: ACL, meeting of the association for computational linguistics, conference, June, University of Michigan, USA

Download references

Acknowledgements

We thank editors and reviewers for their effort and constructive help to improve this work.

Author information

Authors and Affiliations

  1. School of Computer Science, Beijing Institute of Technology, Beijing, China

    Xin Sun, Jinghu Jiang & Yuming Shang

Authors
  1. Xin Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinghu Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuming Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Sun.

Ethics declarations

Conflict of interest

The authors have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, X., Jiang, J. & Shang, Y. ESRE: handling repeated entities in distant supervised relation extraction. Neural Comput & Applic 33, 11325–11337 (2021). https://doi.org/10.1007/s00521-020-05642-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05642-8

Keywords

Search

Navigation