计算机科学 ›› 2024, Vol. 51 ›› Issue (2): 217-237.doi: 10.11896/jsjkx.221200142
刘涛1,2, 蒋国权2, 刘姗姗2, 刘浏2, 环志刚2
LIU Tao1,2, JIANG Guoquan2, LIU Shanshan2, LIU Liu2, HUAN Zhigang2
摘要: 事件抽取作为信息抽取的任务之一,旨在从非结构化文本中抽取出结构化事件信息。当前基于机器学习和深度学习的自动化信息抽取方法过度依赖于标注数据,而大多数领域的标准数据集规模很小且分布不均匀,因此低资源场景成为了限制自动化信息抽取性能的瓶颈。虽然,近年来众多学者针对低资源场景进行了深入研究,并取得了许多显著的成果,但目前针对该场景下的事件抽取研究综述比较缺乏。文中对现有的学术成果进行了较为全面的总结分析,首先介绍了相关任务的定义,并将低资源场景事件抽取任务分为3类;其次围绕此分类重点阐述了6种相关技术方法,包括基于迁移学习、基于提示学习、基于无监督学习、基于弱监督学习、基于数据与辅助知识增强、基于元学习的方法,并指出了当前方法的不足和未来改进的方向;然后介绍了相关数据集及评价指标,并对典型技术方法的实验结果进行了总结分析;最后从全局角度总结分析了当前低资源场景事件抽取工作面临的挑战及未来研究的趋势。
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