Abstract
Event factuality identification (EFI) is a task to judge the factuality of events in texts, and is also the basic task of many related applications in the field of Natural Language Processing (NLP), such as information extraction and rumor detection. Previous research on EFI relied on annotated information, which cannot be applied to real world applications directly, and some studies only considered the default source AUTHOR. To address the above issues, this paper launches an end-to-end EFI model considering different event-related sources, which constructs the candidate event sets from raw texts to capture various kinds of event-related information, and then proposes a hybrid neural network model on GCN and BiLSTM to learn semantic and syntactic features, respectively. The experimental results on FactBank show that our proposed approach outperforms the baselines.
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Acknowledgments
The authors would like to thank the three anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China (No. 61772354, 61836007 and 61773276.), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
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Cao, J., Qian, Z., Li, P., Zhu, X., Zhu, Q. (2021). End-to-End Event Factuality Identification via Hybrid Neural Networks. In: Chen, H., Liu, K., Sun, Y., Wang, S., Hou, L. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence. CCKS 2020. Communications in Computer and Information Science, vol 1356. Springer, Singapore. https://doi.org/10.1007/978-981-16-1964-9_16
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