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Document-level multi-task learning approach based on coreference-aware dynamic heterogeneous graph network for event extraction

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Abstract

Document-level event extraction aims to extract event-related information from an unstructured document composed of multiple sentences. Existing approaches are not effective due to the challenge of event arguments that are scattered across multi-sentences and they pay more attention to the coreference relationship between entity mentions. However, it is an extremely common phenomenon that there are a large number of crossing sentences pronouns that referring to entity mentions. These pronouns also contain rich semantic information related to events in the document. Therefore, there is still a challenge that how to effectively construct the mention–pronoun coreference relationship and better learn the rich semantic entities representations for DEE. Aiming at the above problems, we propose a novel document-level multi-task learning approach based on coreference-aware dynamic heterogeneous graph network for event extraction, named DMCGEE. Specifically, first, an information enhancement extractor module is constructed to effectively capture multi-types of semantic association information for mentions representations. Second, a mention–pronoun coreference resolution method is proposed to capture mention–pronoun coreference resolution pairs, and a coreference-aware dynamic heterogeneous graph network is constructed to help sentences and mentions representations to focus on the effective global related information, thereby improving the performance of DMCGEE. Experiments show that DMCGEE outperforms the state-of-the-art.

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Data Availability

The open datasets analyzed during this study have been properly cited in this published article (see reference section). If found difficulty in finding the data links, same can be available from the corresponding author on reasonable request. Our own dataset has been uploaded to Github, it can be accessed through https://github.com/just123cz/Paper_data.git.

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Acknowledgements

This research is funded by the Applied Basic Research Program of Liaoning Province (No. 2022JH2/101300250). Digital Liaoning Smart Building Strong Province (Direction of Digital Economy) (No. 13031307053000568). National Natural Science Foundation of China (No. 62072220, 61502215). Central Government Guides Local Science and Technology Development Foundation Project of Liaoning Province (No. 2022JH6/100100032). Natural Science Foundation of Liaoning Province (2022-KF-13-06). National Natural Science Foundation of China (61472169). The youth talent support program of ‘Xing Liao Talent Program’ (No. XLYC2203003).

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  1. Liaoning University, Chong Shan, Shen Yang, 110000, Liao Ning, China

    Ze Chen, Wanting Ji, Linlin Ding & Baoyan Song

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  1. Ze Chen
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  2. Wanting Ji
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Chen, Z., Ji, W., Ding, L. et al. Document-level multi-task learning approach based on coreference-aware dynamic heterogeneous graph network for event extraction. Neural Comput & Applic 36, 303–321 (2024). https://doi.org/10.1007/s00521-023-08977-0

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