Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (5): 1247-1255.DOI: 10.11772/j.issn.1001-9081.2020071080

Special Issue: 人工智能 综述

• Artificial intelligence • Previous Articles     Next Articles

Review of event causality extraction based on deep learning

WANG Zhujun1,2, WANG Shi2, LI Xueqing1,2, ZHU Junwu1   

  1. 1. College of Information Engineering, Yangzhou University, Yangzhou Jiangsu 225000, China;
    2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2020-07-23 Revised:2020-11-09 Online:2020-12-23 Published:2021-05-10
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61872313), the Key Project of National Key Research and Development Program of China (2017YFB1002300, 2018YFC1700302).

基于深度学习的事件因果关系抽取综述

王朱君1,2, 王石2, 李雪晴1,2, 朱俊武1   

  1. 1. 扬州大学 信息工程学院, 江苏 扬州 225000;
    2. 中国科学院 计算技术研究所, 北京 100190
  • 通讯作者: 王石
  • 作者简介:王朱君(1996-),男,江苏东台人,硕士研究生,主要研究方向:自然语言处理;王石(1981-),男,山东博兴人,副研究员,博士,主要研究方向:语义分析、知识图谱;李雪晴(1995-),女,江苏泰州人,博士研究生,主要研究方向:自然语言处理;朱俊武(1972-),男,江苏江都人,教授,博士,CCF高级会员,主要研究方向:知识工程、本体论。
  • 基金资助:
    国家自然科学基金资助项目(61872313);国家重点研发计划重点专项(2017YFB1002300,2018YFC1700302)。

Abstract: Causality extraction is a kind of relation extraction task in Natural Language Processing (NLP), which mines event pairs with causality from text by constructing event graph, and play important role in applications of finance, security, biology and other fields. Firstly, the concepts such as event extraction and causality were introduced, and the evolution of mainstream methods and the common datasets of causality extraction were described. Then, the current mainstream causality extraction models were listed. Based on the detailed analysis of pipeline based models and joint extraction models, the advantages and disadvantages of various methods and models were compared. Furthermore, the experimental performance and related experimental data of the models were summarized and analyzed. Finally, the research difficulties and future key research directions of causality extraction were given.

Key words: causality, Natural Language Processing (NLP), relation extraction, event pair, deep learning

摘要: 因果关系抽取是自然语言处理(NLP)中的一种关系抽取任务,它通过构造事件图来挖掘文本中具有因果关系的事件对,已经在金融、安全、生物等领域的应用中发挥重要作用。首先,介绍了事件抽取和因果关系等概念,并介绍了因果关系抽取主流方法的演变和常用数据集;然后,列举了当前主流的因果关系抽取模型,并且在分别对基于流水线的模型和联合抽取模型进行详细分析的基础上,对比了各种方法和模型的优缺点;此外,对各模型的实验性能及相关实验数据进行了归纳分析;最后,给出了当前的因果关系抽取的研究难点和未来的重点研究方向。

关键词: 因果关系, 自然语言处理, 关系抽取, 事件对, 深度学习

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