Abstract
Event extraction is an essential yet challenging task in information extraction. Previous approaches have paid little attention to the problem of roles overlap which is a common phenomenon in practice. To solve this problem, this paper defines event relation triple to explicitly represent relations among triggers, arguments and roles which are incorporated into the model to learn their inter-dependencies. A novel joint framework for multiple Chinese events extraction is proposed which jointly performs predictions for event triggers and arguments based on shared feature representations from pre-trained language model. Experimental comparison with state-of-the-art baselines on ACE 2005 dataset shows the superiority of the proposed method in both trigger classification and argument classification.
Supported by National Key Research and Development Program (No. 2019YFB1406302), China Postdoctoral Science Foundation (NO. 2020M670057) and Beijing Postdoctoral Research Foundation (No. ZZ2019-92).
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Xu, N., Xie, H., Zhao, D. (2020). A Novel Joint Framework for Multiple Chinese Events Extraction. In: Sun, M., Li, S., Zhang, Y., Liu, Y., He, S., Rao, G. (eds) Chinese Computational Linguistics. CCL 2020. Lecture Notes in Computer Science(), vol 12522. Springer, Cham. https://doi.org/10.1007/978-3-030-63031-7_13
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