Abstract
Event trigger recognition plays a crucial role in open-domain event extraction. To address issues of prior work on restricted domains and constraint types of events, so as to enable robust open event trigger recognition for various domains. In this paper, we propose a novel distantly supervised framework of event trigger extraction regardless of domains. This framework consists of three components: a trigger synonym generator, a synonym set scorer and an open trigger classifier. Given the specific knowledge bases, the trigger synonym generator generates high-quality synonym sets to train the remaining components. We employ distant supervision to produce instances of event trigger, then organizes them into fine-grained synonym sets. Inspired by recent deep metric learning, we also propose a novel neural method named hierarchical self-attentive neural network (HiSNN) to score the quality of generated synonym sets. Experimental results on three datasets (including two cross-domain datasets) demonstrate the superior of our proposal compared to the state-of-the-art approaches.
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Acknowledgment
The research reported in this paper was supported in part by the Natural Science Foundation of China under the grant No.91746203, National Natural Science Foundation of China under the grant No.61991415, and Ministry of Industry and Information Technology project of the Intelligent Ship Situation Awareness System under the grant No. MC-201920-X01.
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Pei, X., Wang, H., Luo, X., Gao, J. (2020). Open Event Trigger Recognition Using Distant Supervision with Hierarchical Self-attentive Neural Network. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_80
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