Abstract
Event factuality represents the factual nature of events in texts, and describes whether an event is a fact, a possibility, or an impossible situation. Previous work usually used the embedding of event sentence to represent the event factuality, ignoring the other helpful evidence, such as negative words, speculative words and time words. To address the above issue, this paper introduces various kinds of effective cues, i.e., time cue, negative cue and speculative, to a BERT-based convolutional neural network to identify Chinese sentence-level event factuality. Experimental results on the Chinese DLEF corpus showed that our model outperforms the baseline BERT on macro and micro F1 by 3.64% and 3.77%, respectively. Moreover, the training time of our model is just one-fifth of the benchmark.
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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. 61836007, 61772354 and 61773276), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
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Zhang, L., Li, P., Qian, Z., Zhu, X., Zhu, Q. (2021). Employing Multi-cues to Identify Event Factuality. 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_15
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