Abstract
The purpose of relation extraction is to extract semantic relationships between entities in sentences, which can be seen as a classification task. In recent years, the use of graph neural networks to handle relation extraction tasks has become increasingly popular. However, most existing graph-based methods have the following problems: 1) they cannot fully utilize dependency relation information; 2) there is no consistent criterion for pruning dependency trees. To address these issues, we propose a two-stage graph convolutional networks for relation extraction. In the first stage of the model, the node representation, dependency relation type representation and dependency type weight jointly generate new node representations, fully utilizing the dependency relation information. In the second stage, with the help of the adjacency matrix derived from the dependency tree, the graph convolution operation is performed. In this way, the model can automatically complete the pruning operation. We evaluated our proposed method on two public datasets, and the results show that our model outperforms previous studies in terms of F1 score and achieves the best performance. Further ablation experiments also confirm the effectiveness of each component in our proposed model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, G., Tian, Y., Song, Y., Wan, X.: Relation extraction with type-aware map memories of word dependencies. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, Online Event, 1–6 August 2021. Findings of ACL, vol. ACL/IJCNLP 2021, pp. 2501–2512. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.findings-acl.221
Denecke, K.: Sentiment Analysis in the Medical Domain. Springer (2023). https://doi.org/10.1007/978-3-031-30187-2
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2–7 June 2019, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/n19-1423
Guo, Z., Zhang, Y., Lu, W.: Attention guided graph convolutional networks for relation extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 241–251 (2019)
Hendrickx, I., et al.: Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. In: Erk, K., Strapparava, C. (eds.) Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval@ACL 2010, Uppsala University, Uppsala, Sweden, 15–16 July 2010, pp. 33–38. The Association for Computer Linguistics (2010). https://aclanthology.org/S10-1006/
Kambhatla, N.: Combining lexical, syntactic, and semantic features with maximum entropy models for information extraction. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain, 21–26 July 2004 - Poster and Demonstration. ACL (2004). https://aclanthology.org/P04-3022/
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015)
Peng, N., Poon, H., Quirk, C., Toutanova, K., Yih, W.T.: Cross-sentence n-ary relation extraction with graph lstms. Trans. Assoc. Comput. Ling. 5, 101–115 (2017)
Pérez-Pérez, M., Ferreira, T., Igrejas, G., Fdez-Riverola, F.: A deep learning relation extraction approach to support a biomedical semi-automatic curation task: the case of the gluten bibliome. Expert Syst. Appl. 195, 116616 (2022). https://doi.org/10.1016/j.eswa.2022.116616
Sheng, S., et al.: Human-adversarial visual question answering. Adv. Neural. Inf. Process. Syst. 34, 20346–20359 (2021)
Soares, L.B., Fitzgerald, N., Ling, J., Kwiatkowski, T.: Matching the blanks: Distributional similarity for relation learning. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2895–2905 (2019)
Song, L., Zhang, Y., Wang, Z., Gildea, D.: N-ary relation extraction using graph-state lstm. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2226–2235 (2018)
Tian, Y., Chen, G., Song, Y., Wan, X.: Dependency-driven relation extraction with attentive graph convolutional networks. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 4458–4471 (2021)
Wang, X., et al.: Learning intents behind interactions with knowledge graph for recommendation. In: Proceedings of the Web Conference 2021, pp. 878–887 (2021)
Xu, Y., Mou, L., Li, G., Chen, Y., Peng, H., Jin, Z.: 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 (2015)
Yan, Q., Zhang, Y., Liu, Q., Wu, S., Wang, L.: Relation-aware heterogeneous graph for user profiling. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3573–3577 (2021)
Yu, B., Mengge, X., Zhang, Z., Liu, T., Yubin, W., Wang, B.: Learning to prune dependency trees with rethinking for neural relation extraction. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 3842–3852 (2020)
Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. J. Mach. Learn. Res. 3, 1083–1106 (2003). http://jmlr.org/papers/v3/zelenko03a.html
Zhang, Y., Qi, P., Manning, C.D.: 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 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Z., Yang, Y., Ma, J. (2024). Two-Stage Graph Convolutional Networks for Relation Extraction. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1969. Springer, Singapore. https://doi.org/10.1007/978-981-99-8184-7_37
Download citation
DOI: https://doi.org/10.1007/978-981-99-8184-7_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8183-0
Online ISBN: 978-981-99-8184-7
eBook Packages: Computer ScienceComputer Science (R0)