计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (2): 217-237.doi: 10.11896/jsjkx.221200142

• 人工智能 • 上一篇    下一篇

低资源场景事件抽取研究综述

刘涛1,2, 蒋国权2, 刘姗姗2, 刘浏2, 环志刚2   

  1. 1 南京信息工程大学计算机学院软件学院网络安全空间学院 南京210044
    2 国防科技大学第六十三研究所 南京210007
  • 收稿日期:2022-12-24 修回日期:2023-04-13 出版日期:2024-02-15 发布日期:2024-02-22
  • 通讯作者: 蒋国权(jianggq2001@163.com)
  • 作者简介:(1987359802@qq.com)
  • 基金资助:
    第四批军事科技领域青年人才托举工程项目(2021-JCJQ-QT-050);中国博士后科学基金资助项目(2021MD703983);江苏省高等学校自然科学研究面上项目(20KJB413003)

Survey of Event Extraction in Low-resource Scenarios

LIU Tao1,2, JIANG Guoquan2, LIU Shanshan2, LIU Liu2, HUAN Zhigang2   

  1. 1 School of Computer Science,Nanjing University of Information Science and Technology,Nanjing 210044,China
    2 The Sixty-third Research Institute,National University of Defense Technology,Nanjing 210007,China
  • Received:2022-12-24 Revised:2023-04-13 Online:2024-02-15 Published:2024-02-22
  • About author:LIU Tao,born in 1998,master candidate.His main research interests include natural language processing and knowledge graph.JIANG Guoquan,born in 1978,master,associate professor,is a member of CCF(No.B2492M).His main research interests include data engineering and knowledge graph.
  • Supported by:
    Fourth Batch of Young Talent Lifting Project of Military Science and Technology(2021-JCJQ-QT-050),China Postdoctoral Science Foundation(2021MD703983) and Natural Science Foundation of the Jiangsu Higher Education Institutions of China(20KJB413003).

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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。

关键词: 事件抽取, 低资源场景, 数据处理, 场景适应

Abstract: As one of the tasks of information extraction,event extraction aims to extract structured event information from unstructured text.The current automated information extraction methods based on machine learning and deep learning rely on labeled data excessively,but standard datasets in most areas are small and unevenly distributed.So the low-resource scenarios become an important bottleneck that limits the performance of automated information extraction.Although in recent years,many scholars have conducted in-depth research on low resource scenarios and produced many remarkable results,there is still a lack of research on event extraction in this scenario at present.This paper makes a comprehensive summary and analysis of existing academic achievements.Firstly,it introduces the definition of related task,and the task of event extraction in low resource scenarios is divided into three categories.Then six kinds of related techniques and methods are discussed around this classification,including transfer learning based,prompt learning based,unsupervised learning based,weakly supervised learning based,data and au-xiliary knowledge enhancement based,and meta learning based approaches.Subsequently,the shortcomings of current methods and strategies for future improvement are pointed out.Then the related datasets and evaluation metrics are introduced and the experimental results of typical techniques are summarized and analyzed.Finally,the challenges and future research trends about event extraction in low resource scenarios are summarized and analyzed from a global perspective.

Key words: Event extraction, Low-resource scenarios, Data processing, Scenarios adaptation

中图分类号: 

  • TP391
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