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A multi-view heterogeneous and extractive graph attention network for evidential document-level event factuality identification

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Abstract

Evidential Document-level Event Factuality Identification (EvDEFI) aims to predict the factual nature of an event and extract evidential sentences from the document precisely. Previous work usually limited to only predicting the factuality of an event with respect to a document, and neglected the interpretability of the task. As a more fine-grained and interpretable task, EvDEFI is still in the early stage. The existing model only used shallow similarity calculation to extract evidences, and employed simple attentions without lexical features, which is quite coarse-grained. Therefore, we propose a novel EvDEFI model named Heterogeneous and Extractive Graph Attention Network (HEGAT), which can update representations of events and sentences by multi-view graph attentions based on tokens and various lexical features from both local and global levels. Experiments on EB-DEF-v2 corpus demonstrate that HEGAT model is superior to several competitive baselines and can validate the interpretability of the task.

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Acknowledgements

The authors would like to thank the three anonymous reviewers for their comments on this paper. This work was supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. 62006167 and 62276177), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, 215006, China

    Zhong Qian, Peifeng Li, Qiaoming Zhu & Guodong Zhou

Authors
  1. Zhong Qian
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  2. Peifeng Li
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Correspondence to Zhong Qian.

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Competing interests The authors declare that they have no competing interests or financial conflicts to disclose.

Additional information

Zhong Qian received his BS and PhD degrees in computer science and technology from Soochow University, China in 2012 and 2018, respectively. He worked as a postdoctor in Singapore Management University, Singapore from 2018 to 2019. Currently, he is an associate professor in the School of Computer Science and Technology, Soochow University, China. His main research interests include information extraction in natural language processing.

Peifeng Li received his BS, MS, and PhD degrees all in computer science from Soochow University, China in 1994, 1997, and 2006, respectively. Now he is a professor at the School of Computer Science and Technology, Soochow University, China. His current research interests include Chinese information processing, machine learning and information extraction. He has published more than 40 papers on IEEE/ACM Transactions, ACL/IJCAI/EMNLP/COLING, and more than 60 SCI/EI-indexed papers.

Qiaoming ZHU received his PhD degree in computer science and technology from Soochow University, China in 2008. Currently, he is a professor at the School of Computer Science and Technology, Soochow University, and acts as the director of Department of Science, Technology and Industry in Soochow University, China. His research interests include natural language processing, information extraction and embedded systems. He has published more than 80 papers in recent years.

Guodong Zhou received the PhD degree from National University of Singapore in 1999. He joined the Institute for Infocomm Research, Singapore in 1999, and had been associate scientist, scientist, and associate lead scientist at the institute until August 2006. Currently, he is a professor at the School of Computer Science and Technology, Soochow University, China. His research interests include natural language processing, information extraction and machine learning. He has published more than 120 papers on CCF-A/B conferences and SCI/EI journals in recent 5 years.

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A multi-view heterogeneous and extractive graph attention network for evidential document-level event factuality identification

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Qian, Z., Li, P., Zhu, Q. et al. A multi-view heterogeneous and extractive graph attention network for evidential document-level event factuality identification. Front. Comput. Sci. 19, 196319 (2025). https://doi.org/10.1007/s11704-024-3809-6

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