Abstract
This paper tackles the task of event detection, which involves identifying and categorizing the events. Currently event detection remains a challenging task due to the difficulty at encoding the event semantics in complicate contexts. The core semantics of an event may derive from its trigger and arguments. However, most of previous studies failed to capture the argument semantics in event detection. To address this issue, this paper first provides a rule-based method to predict candidate arguments on the event types of possibilities, and then proposes a recurrent neural network model RNN-ARG with the attention mechanism for event detection to capture meaningful semantic regularities form these predicted candidate arguments. The experimental results on the ACE 2005 English corpus show that our approach achieves competitive results compared with previous work.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liao, S., Grishman, R.: Using document level cross-event inference to improve event extraction. In: ACL 2010, pp. 789–797 (2010)
Hong, Y., et al.: Using cross-entity inference to improve event extraction. In: ACL 2011, pp. 1127–1136 (2011)
Ji, H., Grishman, R.: Refining event extraction through cross-document inference. In: ACL-HLT 2008, pp. 254–262 (2008)
Nguyen, H.T., Grishman, R.: Event detection and domain adaptation with convolutional neural networks. In: ACL 2015, pp. 365–371 (2015)
Nguyen, H.T., Grishman, R.: Modeling skip-grams for event detection with convolutional neural networks. In: EMNLP 2016, pp. 886–891 (2016)
Chen, Y., Xu, L., Liu, K., Zeng, D., Zhao, J.: Event extraction via dynamic multi-pooling convolutional neural networks. In: ACL 2015, pp. 167–176 (2015)
Sha, L., Qian, F., Chang, B., Sui, Z.: Jointly extraction event trigger and arguments by dependency-bridge RNN and tensor-based argument interaction. In: AAAI 2018 (2018)
Ahn, D.: The stages of event extraction. In: Proceedings of the ACL 2006, pp. 1–8 (2006)
Gupta, P., Ji, H.: Predicting unknown time arguments based on cross event propagation. In: ACL-IJCNLP 2009, pp. 369–372 (2009)
Nguyen, H.T., Cho, K., Grishman, R.: Joint event extraction via recurrent neural networks. In: ACL 2016, pp. 300–309 (2016)
Liu, S., Chen, Y., Liu, K., Zhao, J.: Exploiting argument Information to improve event detection via supervised attention mechanisms. In: ACL-2017, pp. 1789–1798 (2017)
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS 2013, UK, pp. 3111–3119 (2013)
Li, Q., Ji, H., Huang L.: Joint event extraction via structured prediction with global features. In: ACL 2013, pp. 73–82 (2013)
Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP 2014, pp. 1746–1751 (2014)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Acknowledgments
The authors would like to thank three anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China under Grant Nos. 61772354, 61773276 and 61472265, and was also supported by the Strategic Pioneer Research Projects of Defense Science and Technology under Grant No. 17-ZLXDXX-02-06-02-04.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, W., Zhu, X., Tao, J., Li, P. (2018). Event Detection via Recurrent Neural Network and Argument Prediction. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-99501-4_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99500-7
Online ISBN: 978-3-319-99501-4
eBook Packages: Computer ScienceComputer Science (R0)