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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References
SaurÃ, R., Pustejovsky, J.: Factbank: a corpus annotated with event factuality. Lang. Resour. Eval. 43(3), 227–268 (2009)
Lee, K., Artzi, Y., Choi, Y., Zettlemoyer, L.: Event detection and factuality assessment with non-expert supervision. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1643–1648 (2015)
Veyseh, A.P.B., Nguyen, T.H., Dou, D.: Graph based neural networks for event factuality prediction using syntactic and semantic structures. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4393–4399 (2019)
Stanovsky, G., Eckle-Kohler, J., Puzikov, Y., Dagan, I., Gurevych, I.: Integrating deep linguistic features in factuality prediction over unified datasets. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 2, pp. 352–357 (2017)
Rudinger, R., White, A.S., Van Durme, B.: Neural models of factuality. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 731–744 (2018)
Minard, A.L., et al.: Meantime, the newsreader multilingual event and time corpus. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), pp. 4417–4422 (2016)
Cao, Y., Zhu, Q.M., Li, P.F.: The construction of chinese event factuality corpus. J. Chinese Inf. Process. 27(6), 38–44 (2012)
Qian, Z., Li, P.F., Zhu, Q.M., Zhou, G.D.: Document-level event factuality identification via adversarial neural network. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 2799–2809 (2019)
SaurÃ, R.: A factuality profiler for eventualities in text. PhD dissertation, Brandeis University (2008)
Lotan, A., Stern, A., Dagan, I.: Truthteller: annotating predicate truth. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 752–757 (2013)
De Marneffe, M.C., Manning, C.D., Potts, C.: Did it happen? The pragmatic complexity of veridicality assessment. Comput. Linguist. 38(2), 301–333 (2012)
SaurÃ, R., Pustejovsky, J.: Are you sure that this happened? assessing the factuality degree of events in text. Comput. Linguist. 38(2), 261–299 (2012)
Qian, Z., Li, P.F., Zhu, Q.M.: A two-step approach for event factuality identification. In: Proceedings of the 2015 International Conference on Asian Language Processing (IALP), pp. 103–106 (2015)
He, T.X., Li, P.F., Zhu, Q.M.: Identifying chinese event factuality with convolutional neural networks. In: Proceedings of the Chinese Lexical Semantics - 18th Workshop (CLSW 2017), pp. 284–292 (2017)
Qian, Z., Li, P., Zhou, G., Zhu, Q.: Event factuality identification via hybrid neural networks. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11305, pp. 335–347. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04221-9_30
Qian, Z., Li, P.F., Zhang, Y., Zhou, G.D., Zhu, Q.M.: Event factuality identification via generative adversarial networks with auxiliary classification. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pp. 4293–4300 (2018)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations (2017)
Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. In: Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, pp. 5998–6008 (2017)
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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