Abstract
Document-level Event Factuality Identification (DEFI) is to identify the factuality of an event in document. DEFI is an important foundation for many Natural Language Understanding (NLU) tasks, such as information extraction and text understanding. The negation and speculation scope refers to the continuous segment of text, which is controlled by the words with negative or speculative semantics. In this paper, we explore the importance of negation and speculation scope for DEFI, and propose two methods to use the scope features. The model of detecting scope is trained from cross-domain corpus, and applied to the Document-Level Event Factuality (DLEF) corpus. Experimental results show that our DEFI model is superior to several baselines.
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Acknowledgments
The authors would like to thank the two anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China (No. 62006167, 61836007 and 61772354.), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
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Zhang, H., Qian, Z., Zhu, X., Li, P. (2021). Document-Level Event Factuality Identification Using Negation and Speculation Scope. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13108. Springer, Cham. https://doi.org/10.1007/978-3-030-92185-9_34
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