Skip to main content
SpringerLink
Log in

Joint extraction of entities and overlapping relations by improved graph convolutional networks

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Joint extraction of entities and relations is to recognize entities and semantic relations simultaneously, which is significant for knowledge graph construction. Recently, many effective joint models use dependency trees to capture the structural information of sentences. However, most dependency-based methods cannot make full use of the dependency information. This is because these methods just consider the connection information of dependency trees and ignore the influence of different nodes on the connected edges. In this paper, we establish a novel model to extract entities and relations simultaneously by using improved Multi-task Graph Convolutional networks, called MGCN. Specifically, considering the importance of node information, we merge both node and edge information into Graph Convolutional networks (GCN). In addition, in order to recognize the overlapping relations, we propose an efficient strategy to map multiple relational labels of a sentence into a unique code. Finally, we evaluate our joint model on two public datasets, and the experimental results show that our model outperforms the state-of-art models.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Quirk C, Poon H (2017) Distant supervision for relation extraction beyond the sentence boundary. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pp 1171–1182

  2. Xiong C, Power R, Callan J (2017) Explicit semantic ranking for academic search via knowledge graph embedding. In: Proceedings of the 26th international conference on world wide web, pp 1271–1279

  3. Schlichtkrull M, Kipf TN, Bloem P, Van Den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: European Semantic Web Conference, pp 593–607. Springer

  4. Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C (2016) Neural architectures for named entity recognition. In: Proceedings of NAACL-HLT, pp 260–270

  5. Rei M, Crichton G, Pyysalo S (2016) Attending to characters in neural sequence labeling models. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp 309–318

  6. Wang L, Cao Z, De Melo G, Liu Z (2016) Relation classification via multi-level attention cnns. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 1298–1307

  7. Peng N, Poon H, Quirk C, Toutanova K, Yih W-t (2017) Cross-sentence n-ary relation extraction with graph lstms. Transactions of the Association for Computational Linguistics 5:101–115

    Article  Google Scholar 

  8. Zhang Y, Qi P, Manning CD (2018) Graph convolution over pruned dependency trees improves relation extraction. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp 2205–2215

  9. Guo Z, Zhang Y, Lu W (2019) Attention guided graph convolutional networks for relation extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 241–251

  10. Gormley MR, Yu M, Dredze M (2015) Improved relation extraction with feature-rich compositional embedding models. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp 1774–1784

  11. Miwa M, Bansal M (2016) End-to-end relation extraction using lstms on sequences and tree structures. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 1105–1116

  12. Li Q, Ji H (2014) Incremental joint extraction of entity mentions and relations. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 402–412

  13. Ren X, Wu Z, He W, Qu M, Voss CR, Ji H, Abdelzaher TF, Han J (2017) Cotype: Joint extraction of typed entities and relations with knowledge bases. In: Proceedings of the 26th International Conference on World Wide Web, pp 1015–1024

  14. Takanobu R, Zhang T, Liu J, Huang M (2019) A hierarchical framework for relation extraction with reinforcement learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 33, pp 7072–7079

  15. Wei Z, Jia Y, Tian Y, Hosseini MJ, Steedman M, Chang Y (2019) Joint extraction of entities and relations with a hierarchical multi-task tagging model

  16. Bai C, Pan L, Luo S, Wu Z (2020) Joint extraction of entities and relations by a novel end-to-end model with a double-pointer module. Neurocomputing 377:325–333

    Article  Google Scholar 

  17. Han X, Gao T, Lin Y, Peng H, Yang Y, Xiao C, Liu Z, Li P, Zhou J, Sun M (2020) More data, more relations, more context and more openness: A review and outlook for relation extraction. In: Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pp 745–758

  18. Zhai F, Potdar S, Xiang B, Zhou B (2017) Neural models for sequence chunking. In: AAAI

  19. Tran QH, MacKinlay A, Yepes AJ (2017) Named entity recognition with stack residual lstm and trainable bias decoding. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp 566–575

  20. Nguyen TH, Grishman R (2015) Relation extraction: Perspective from convolutional neural networks. In: Proceedings 2014of the 1st Workshop on Vector Space Modeling for Natural Language Processing, pp 39–48

  21. 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

  22. Jiang X, Wang Q, Li P, Wang B (2016) Relation extraction with multi-instance multi-label convolutional neural networks. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp 1471–1480

  23. Zhang Y, Zhong V, Chen D, Angeli G, Manning CD (2017) Position-aware attention and supervised data improve slot filling. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp 35–45

  24. Huang YY, Wang WY (2017) Deep residual learning for weakly-supervised relation extraction. In: EMNLP

  25. Xu Y, Mou L, Li G, Chen Y, Peng H, Jin Z (2015) Classifying relations via long short term memory networks along shortest dependency paths. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 1785– 1794

  26. Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp 1556–1566

  27. Zheng S, Wang F, Bao H, Hao Y, Zhou P, Xu B (2017) Joint extraction of entities and relations based on a novel tagging scheme. In: ACL (1)

  28. Dai D, Xiao X, Lyu Y, Dou S, She Q, Wang H (2019) Joint extraction of entities and overlapping relations using position-attentive sequence labeling. In: The conference on artificial intelligence, AAAI, pp 6300–6308

  29. Zeng X, Zeng D, He S, Liu K, Zhao J (2018) Extracting relational facts by an end-to-end neural model with copy mechanism. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 506– 514

  30. Fu T-J, Li P-H, Ma W-Y (2019) Graphrel: Modeling text as relational graphs for joint entity and relation extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 1409–1418

  31. Lai Q, Zhou Z, Liu S (2020) Joint entity-relation extraction via improved graph attention networks. Symmetry 12(10):1746

    Article  Google Scholar 

  32. Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, pp 4171–4186

  33. Wang Q, Lv L, Yu B, Li S (2020) End-to-end relation extraction using graph convolutional network with a novel entity attention. In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC), pp 2086–2093. IEEE

  34. Qiao B, Zou Z, Huang Y, Fang K, Zhu X, Chen Y (2021) A joint model for entity and relation extraction based on bert. Neural Comput & Applic, pp 1–11

  35. Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

  36. Liu Y, Wei F, Li S, Ji H, Zhou M, Wang H (2015) A dependency-based neural network for relation classification. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp 285–290

  37. Marcheggiani D, Titov I (2017) Encoding sentences with graph convolutional networks for semantic role labeling. In: EMNLP

  38. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net

  39. Hoffmann R, Zhang C, Ling X, Zettlemoyer L, Weld DS (2011) Knowledge-based weak supervision for information extraction of overlapping relations. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 541–550

  40. Hong Y, Liu Y, Yang S, Zhang K, Hu J (2020) Joint extraction of entities and relations using graph convolution over pruned dependency trees. Neurocomputing 411:302–312

    Article  Google Scholar 

  41. Geng Z, Zhang Y, Han Y (2021) Joint entity and relation extraction model based on rich semantics. Neurocomputing 429:132–140

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Qi Sun, Kun Zhang, Laishui Lv, Xun Li, Kun Huang & Ting Zhang

Authors
  1. Qi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laishui Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kun Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ting Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kun Zhang.

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, Q., Zhang, K., Lv, L. et al. Joint extraction of entities and overlapping relations by improved graph convolutional networks. Appl Intell 52, 5212–5224 (2022). https://doi.org/10.1007/s10489-021-02667-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02667-x

Keywords

Search

Navigation