Abstract
Event extraction is a key task of information extraction. Existing methods are not effective due to two challenges of this task: 1) Most of previous methods only consider a single granularity information and they are often insufficient to distinguish ambiguity of triggers for some types of events. 2) The correlation among intra-sentence and inter-sentence event is non-trivial to model. Previous methods are weak in modeling interdependency among the correlated events and they have never modeled this problem for the whole event extraction task. In this paper, we propose a novel Multi-granularity Heterogeneous Graph-based event extraction model (MHGEE) to solve the two problems simultaneously. For the first challenge, MHGEE constructs multi-granularity nodes, including word, entity and context and captures interactions among nodes by R-GCN. It can strengthen semantic and distinguish ambiguity of triggers. For the second, MHGEE uses heterogeneous graph neural network to aggregating the information of relevant events and hence capture the interdependency among the events. The experiment results on ACE 2005 dataset demonstrate that our proposed MHGEE model achieves competitive results compared with state-of-the-art methods in event extraction. Then we demonstrate the effectiveness of our model in ambiguity of triggers and event interdependency.
Supported by the National Key R&D Program of China (2021YFB3101300) and the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDC02030000).
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
Similar content being viewed by others
References
Chen, Y., Xu, L., Liu, K., Zeng, D., Zhao, J.: Event extraction via dynamic multi-pooling convolutional neural networks. In: ACL-IJCNLP 2015–53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference, vol. 1, pp. 167–176 (2015). https://doi.org/10.3115/v1/p15-1017
Chen, Y., Yang, H., Liu, K., Zhao, J., Jia, Y.: Collective event detection via a hierarchical and bias tagging networks with gated multi-level attention mechanisms. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, pp. 1267–1276 (2020). https://doi.org/10.18653/v1/d18-1158
Cui, S., Yu, B., Liu, T., Zhang, Z., Wang, X., Shi, J.: Edge-enhanced graph convolution networks for event detection with syntactic relation. In: Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020, pp. 2329–2339 (2020). https://doi.org/10.18653/v1/2020.findings-emnlp.211
Du, X., Cardie, C.: Event extraction by answering (almost) natural questions. In: EMNLP 2020–2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, pp. 671–683 (2020). https://doi.org/10.18653/v1/2020.emnlp-main.49
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017)
Li, Q., Ji, H., Huang, L.: Joint event extraction via structured prediction with global features. In: ACL 2013–51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, vol. 1, pp. 73–82 (2013)
Liao, S., Grishman, R.: Using document level cross-event inference to improve event extraction. In: ACL 2010–48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, pp. 789–797 (2010)
Liu, S., Liu, K., He, S., Zhao, J.: A probabilistic soft logic based approach to exploiting latent and global information in event classification. In: 30th AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 2993–2999 (2016)
Liu, X., Luo, Z., Huang, H.: Jointly multiple events extraction via attention-based graph information aggregation. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, pp. 1247–1256 (2020). https://doi.org/10.18653/v1/d18-1156
Lu, Y., et al.: TEXT2EVENT: controllable sequence-to-structure generation for end-to-end event extraction. In: ACL-IJCNLP 2021–59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference, pp. 2795–2806 (2021). https://doi.org/10.18653/v1/2021.acl-long.217
Nguyen, T.H., Cho, K., Grishman, R.: Joint event extraction via recurrent neural networks. In: 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference, pp. 300–309 (2016). https://doi.org/10.18653/v1/n16-1034
Nguyen, T.H., Grishman, R.: Graph convolutional networks with argument-aware pooling for event detection. In: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, pp. 5900–5907 (2018)
Sha, L., Qian, F., Chang, B., Sui, Z.: Jointly extracting event triggers and arguments by dependency-bridge RNN and tensor-based argument interaction. In: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, pp. 5916–5923 (2018)
Tang, H., Cao, Y., Zhang, Z., Jia, R., Fang, F., Wang, S.: Multi-granularity heterogeneous graph for document-level relation extraction. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2021-June, pp. 7683–7687 (2021). https://doi.org/10.1109/ICASSP39728.2021.9414755
Wadden, D., Wennberg, U., Luan, Y., Hajishirzi, H.: Entity, relation, and event extraction with contextualized span representations. In: EMNLP-IJCNLP 2019–2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference, pp. 5784–5789 (2020). https://doi.org/10.18653/v1/d19-1585
Wang, X., Han, X., Liu, Z., Sun, M., Li, P.: Adversarial training for weakly supervised event detection. In: NAACL HLT 2019–2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, vol. 1, pp. 998–1008 (2019). https://doi.org/10.18653/v1/n19-1105
Yan, H., Jin, X., Meng, X., Guo, J., Cheng, X.: Event detection with multi-order graph convolution and aggregated attention. In: EMNLP-IJCNLP 2019–2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference, pp. 5766–5770 (2020). https://doi.org/10.18653/v1/d19-1582
Zhang, T., Ji, H., Sil, A.: Joint entity and event extraction with generative adversarial imitation learning. Data Intell. 1(2), 99–120 (2019). https://doi.org/10.1162/dint_a_00014
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, M., Fang, F., Li, H., Liu, Q., Li, Y., Wang, H. (2022). MHGEE: Event Extraction via Multi-granularity Heterogeneous Graph. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13350. Springer, Cham. https://doi.org/10.1007/978-3-031-08751-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-031-08751-6_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08750-9
Online ISBN: 978-3-031-08751-6
eBook Packages: Computer ScienceComputer Science (R0)