A Review of Document-Level Multi-Event Extraction Methods

Guangshuai Ding,Jingjing Tao, Xiaomin Zhu, Bin Lin, Wen Zhang,Yanqing Ye

2023 9th International Conference on Big Data and Information Analytics (BigDIA)(2023)

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摘要
This paper presents a comprehensive review of the prominent methods and techniques employed in the field of document-level multi-event extraction, while also providing a critical evaluation of the associated research. The initial section of this paper introduces the fundamental concepts of document-level multi-event event extraction, including the definition and challenges of event extraction, as well as the specific obstacles encountered in document-level multi-event event extraction. Subsequently, the paper conducts a thorough review of the principal methods used for event extraction, along with an analysis of their applicability to different scenarios and the problems they addressed. Furthermore, the paper offers a detailed exposition and summary of key issues such as thesis element scattering in document-level event extraction, in addition to discussing the prevailing research paradigms in this domain. Lastly, the paper concludes by summarizing the strengths and weaknesses of current research efforts by highlighting the future directions and potential of document-level multi-event extraction research. To summarize, the objective of this paper is to furnish researchers with a thorough and methodical review of document-level multi-event extraction methods, facilitating readers in attaining a more profound comprehension of the most recent advancements and methodologies in this domain.
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关键词
Document-level event extraction,Multiple events,Argument dispersion,Deep learning
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