基于事件演化图与图卷积神经网络的事件预测

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (3975 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Focusing on improving the evolutionary graph construction and enriching the event representation, this paper proposes an event prediction model based on the event evolutionary graph and Graph Convolutional Network(GCN). This model applies an event extraction model, and redefines the edge’s weight on the event evolutionary graph by combining frequency and mutual information. The representation of the event context is learned by BiLSTM and memory network, which is fed as the input into GCN under the guidance of the event evolutionary graph. The final event prediction is jointly completed by such event-relationship aware, context-aware, and neighbor aware event embeddings. Experiment results on the Gigaword benchmark show that the proposed model outperforms six advanced models in event prediction accuracy, with 5.55% increase compared with the latest SGNN method.

Key words： event prediction event evolutionary graph graph convolutional network memory network

Received: 18 June 2021

ZTFLH:

TP391

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	TANG Yan
	CHEN Yi
	ZHANG Zuowei

Cite this article:

TANG Yan,CHEN Yi,ZHANG Zuowei. Event Prediction Based on Event Evolutionary Graph and GCN[J]. , 2022, 36(12): 123-132.

URL:

http://jcip.cipsc.org.cn/EN/ OR http://jcip.cipsc.org.cn/EN/Y2022/V36/I12/123

[1] Chambers N, Jurafsky D. Unsupervised learning of narrative event chains[C]//Proceedings of the Association for Computational Linguistics,2008: 789-797.
[2] Duvenaud D, Maclaurin D, Aguilera-iparraguirre J, et al. Convolutional networks on graphs for learning molecular fingerprints[J]//Proceedings of the Conference and Workshop on Neural Information Processing Systems, 2015: 2224-2232.
[3] Kipf T, Welling M. Semi-supervised classification with graph convolutional networks[C]//Proceedings of the 5th International Conference on Learning Representations, 2017.
[4] Li Y J, Daniel T, Marc B, et al. Gated graph sequence neural networks[C]//Proceedings of the International Conference on Learning Representations, 2016.
[5] Jans B, Bethard S, Vulic I, et al. Skip N-grams and ranking functions for predicting script events[C]//Proceedings of European Chapter of the Association for Computational Linguistics, 2012: 336-344.
[6] Pichotta K, Mooney R J. Learning statistical scripts with lstm recurrent neural networks[C]//Proceedings of the 30th Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence, 2015: 2800-2806.
[7] Glavas G, Snajder J. Construction and evaluation of event graphs[J]. Natural Language Engineering, 2015 21(4): 607-652.
[8] Luo W, Zhang H, Yang X, et al. Dynamic heterogeneous graph neural network for real-time event prediction[C]//Proceedings of KDD, 2020: 3213-3223.
[9] Ding D, Zhang M, Pan X, et al. Modeling extreme events in time series prediction[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019: 1114-1122.
[10] Li Z, Ding X, Liu T. Constructing narrative event evolutionary graph for script event prediction[C]//Proceedings of International Joint Conference on Artificial Intelligence, 2018: 4201-4207.
[11] Zhang Z, Tang Y. Context-aware event type identification based on context fusion and joint learning[C]//Proceedings of IEEE 5th International Conference on Data Science in Cyberspace, 2020: 105-112.
[12] Pichotta K, Mooney R. Statistical script learning with multi-argument events[C]//Proceedings of European chapter of the Association for Computational Linguistics, 2014: 220-229.
[13] Pichotta K, Mooney R J. Using sentence-level lstm language models for script inference[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, USA: ACL, 2016: 279-289.
[14] Wang Z, Zhang Y, Chang C Y. Integrating order information and event relation for script event prediction[C]//Proceedings of Empirical Methods in Natural Language Processing, 2017: 57-67.
[15] Zhao S,Wang Q, Massung S, et al. Constructing and embed-ding abstract event causality networks from text snip-pets[C]//Proceedings of the 10th ACM International Conference on Web Search and Data Mining, 2017: 335-344.
[16] Granroth-wilding M, Clark S. What happens next? Event prediction using a compositional neural network mod-el[C]//Proceedings of the Association for the Advancement of Artificial Intelligence. AAAI, 2016: 2727-2733.
[17] Graff D, Kong J, Chen K, et al. English Giga-word, LDC2003T05[C]//Proceedings of the Linguistic Data Consortium, Philadelphia, 2003.
[18] Wang Z, Zhang Y, Chang C Y. Integrating order information and event relation for script event prediction[C]//Proceedings of Empirical Methods in Natural Language Processing, 2017: 57-67.