Abstract
Event detection suffers from data sparseness and label imbalance problem due to the expensive cost of manual annotations of events. To address this problem, we propose a novel approach that allows for information sharing among related event types. Specifically, we employ a fully connected three-layer artificial neural network as our basic model and propose a type-group regularization term to achieve the goal of information sharing. We conduct experiments with different configurations of type groups, and the experimental results show that information sharing among related event types remarkably improves the detecting performance. Compared with state-of-the-art methods, our proposed approach achieves a better \(F_1\) score on the widely used ACE 2005 event evaluation dataset.
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
Notes
- 1.
- 2.
- 3.
Appeal, Start-Org, Fine, Divorce, Execute, Merge-Org, Nominate, Extradite, Acquit, Declare-Bankruptcy, Pardon, End-Org, Be-Born, Sue and Release-Parole.
References
Ahn, D.: The stages of event extraction. In: Proceedings of the Workshop on Annotating and Reasoning about Time and Events, pp. 1–8 (2006)
Bach, S.H., Huang, B., London, B., Getoor, L.: Hinge-loss Markov random fields: Convex inference for structured prediction. In: Proceedings of Uncertainty in Artificial Intelligence (UAI) (2013)
Baker, C.F., Fillmore, C.J., Lowe, J.B.: The berkeley framenet project. In: Proceedings of 17th Annual Meeting of the Association for Computational Linguistics, pp. 86–90 (1998)
Baroni, M., Dinu, G., Kruszewski, G.: Dont count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 238–247 (2014)
Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)
Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41–75 (1997)
Chen, C., Ng, V.: Joint modeling for Chinese event extraction with rich linguistic features. In: COLING, pp. 529–544 (2012)
Chen, Y., Xu, L., Liu, K., Zeng, D., Zhao, J.: Event extraction via dynamic multi-pooling convolutional neural networks, pp. 167–176. Association for Computational Linguistics (2015)
Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning? J. Mach. Learn. Res. 11, 625–660 (2010)
Evgeniou, T., Micchelli, C.A., Pontil, M.: Learning multiple tasks with kernel methods. J. Mach. Learn. Res. 6(4), 615–637 (2005)
Fillmore, C.J., Johnson, C.R., Petruck, M.R.: Background to framenet. Int. J. Lexicogr. 16(3), 235–250 (2003)
Gupta, P., Ji, H.: Predicting unknown time arguments based on cross-event propagation. In: Proceedings of ACL-IJCNLP, pp. 369–372 (2009)
Hagan, M.T., Demuth, H.B., Beale, M.H., et al.: Neural Network Design. PWS Publishing, Boston (1996)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint. arXiv:1207.0580 (2012)
Hong, Y., Zhang, J., Ma, B., Yao, J., Zhou, G., Zhu, Q.: Using cross-entity inference to improve event extraction. In: Proceedings of ACL, pp. 1127–1136 (2011)
Ji, H., Grishman, R.: Refining event extraction through cross-document inference. In: Proceedings of ACL, pp. 254–262 (2008)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1746–1751 (2014)
Kimmig, A., Bach, S., Broecheler, M., Huang, B., Getoor, L.: A short introduction to probabilistic soft logic. In: Proceedings of NIPS Workshop, pp. 1–4 (2012)
Li, Q., Ji, H., Hong, Y., Li, S.: Constructing information networks using one single model. Association for Computational Linguistics (2014)
Li, Q., Ji, H., Huang, L.: Joint event extraction via structured prediction with global features. In: Proceedings of ACL, pp. 73–82 (2013)
Liao, S., Grishman, R.: Using document level cross-event inference to improve event extraction. In: Proceedings of ACL, pp. 789–797 (2010)
Liu, S., Chen, Y., He, S., Liu, K., Zhao, J.: Leveraging framenet to improve automatic event detection. In: Proceedings of ACL (2016)
Liu, S., Liu, K., He, S., Zhao, J.: A probabilistic soft logic based approach to exploiting latent and global information in event classification. In: Proceedings of the thirtieth AAAI Conference on Artificail Intelligence (2016)
McClosky, D., Surdeanu, M., Manning, C.D.: Event extraction as dependency parsing, pp. 1626–1635. Association for Computational Linguistics (2011)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint. arXiv:1301.3781 (2013)
Nguyen, H.T., Grishman, R.: Modeling skip-grams for event detection with convolutional neural networks. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 886–891. Association for Computational Linguistics (2016)
Nguyen, T.H., Grishman, R.: Event detection and domain adaptation with convolutional neural networks. Association for Computational Linguistics (2015)
Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: Proceedings of ACL, pp. 189–196 (1995)
Zeiler, M.D.: ADADELTA: An adaptive learning rate method. arXiv preprint. arXiv:1212.5701 (2012)
Acknowledgments
This work was supported by the Natural Science Foundation of China (No. 61533018) and the National Basic Research Program of China (No. 2014CB340503). And this research work was also supported by Google through focused research awards program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Liu, S., Chen, Y., Liu, K., Zhao, J., Luo, Z., Luo, W. (2017). Improving Event Detection via Information Sharing Among Related Event Types. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2017 2017. Lecture Notes in Computer Science(), vol 10565. Springer, Cham. https://doi.org/10.1007/978-3-319-69005-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-69005-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69004-9
Online ISBN: 978-3-319-69005-6
eBook Packages: Computer ScienceComputer Science (R0)