面向研究问题的深度学习事件抽取综述

万齐智; 万常选; 胡蓉; 刘德喜; 刘喜平; 廖国琼

doi:10.16383/j.aas.c230184

面向研究问题的深度学习事件抽取综述

doi: 10.16383/j.aas.c230184

万齐智^{1, 2,},
万常选^{1, 2,},
胡蓉^{1, 2,},
刘德喜^{1, 2,},
刘喜平^{1, 2,},
廖国琼^{1, 3,}

1.
江西财经大学数据与知识工程江西省高校重点实验室南昌 330013
2.
江西财经大学信息管理学院南昌 330032
3.
江西财经大学虚拟现实现代产业院南昌 330032

基金项目: 国家自然科学基金(619721184, 62272205, 62272206, 62272206, 62076112), 江西省教育厅科学技术项目(GJJ210531), 江西省自然科学基金(20212ACB202002, 20232ACB202008), 江西省主要学科学术和技术带头人培养计划领军人才项目(20213BCJL22041) 资助

详细信息

作者简介:
万齐智：江西财经大学信息管理学院讲师. 主要研究方向为人工智能, 深度学习, 信息抽取, 自然语言处理, 文本数据挖掘. E-mail: wanqizhi1006@163.com

万常选：江西财经大学信息管理学院教授. 主要研究方向为Web数据管理, 情感分析, 数据挖掘和信息检索. 本文通信作者. E-mail: wanchangxuan@263.net

胡蓉：江西财经大学信息管理学院博士研究生. 主要研究方向为信息抽取, 自然语言处理和大数据分析. E-mail: hurong2014@126.com

刘德喜：江西财经大学信息管理学院教授. 主要研究方向为自然语言处理和信息检索. E-mail: dexi.liu@163.com

刘喜平：江西财经大学信息管理学院教授.主要研究方向为信息检索和数据挖掘. E-mail: liuxiping@jxufe.edu.cn

廖国琼：江西财经大学虚拟现实现代产业院教授. 主要研究方向为数据库和数据挖掘. E-mail: liaoguoqiong@163.com

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出版历程
- 收稿日期: 2023-04-06
- 录用日期: 2023-09-08
- 网络出版日期: 2023-10-07

Event Extraction based on Deep Learning: A Survey of Research Issue

WAN Qi-Zhi^{1, 2
,},
WAN Chang-Xuan^{1, 2
,},
HU Rong^{1, 2
,},
LIU De-Xi^{1, 2
,},
LIU Xi-Ping^{1, 2
,},
LIAO Guo-Qiong^{1, 3
,}

1.
Jiangxi Key Laboratory of Data and Knowledge Engineering, Jiangxi University of Finance and Economics, Nanchang 330013
2.
School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330032
3.
Virtual Reality Modern Industrial Institute, Jiangxi University of Finance and Economics, Nanchang 330032

Funds: Supported by National Natural Science Foundation of China (619721184, 62272205, 62272206, 62272206, 62076112), the Science & Technology Project of the Department of Education of Jiangxi Province (GJJ210531), Natural Science and Foundation of Jiangxi Province (20212ACB202002, 20232ACB202008), Funding Program for Academic and Technical Leaders in Major Disciplines of Jiangxi Province (20213BCJL22041)

More Information

Author Bio:
WAN Qi-Zhi　Lecturer at the School of Information Management, Jiangxi University of Finance and Economics. His research interests cover artificial intelligence, deep learning, information extraction, natural language processing, and text data mining

WAN Chang-Xuan　Professor at the School of Information Management, Jiangxi University of Finance and Economics. His research interests cover Web data management, sentiment analysis, data mining, and information retrieval. Corresponding author of this paper

HU Rong　Ph.D. candidate at the School of Information Management, Jiangxi University of Finance and Economics. Her research interests cover information extraction, natural language processing, and big data analysis

LIU De-Xi　Professor at the School of Information Management, Jiangxi University of Finance and Economics. His research interests cover natural language processing and information retrieval

LIU Xi-Ping　Professor at the School of Information Management, Jiangxi University of Finance and Economics. His research interests cover information retrieval and data mining

LIAO Guo-Qiong　Professor at the Virtual Reality Modern Industrial Institute, Jiangxi University of Finance and Economics. His research interests cover database and data mining

摘要

摘要: 事件抽取是一个历史悠久且极具挑战的研究任务, 取得了大量优异的成果. 由于事件抽取涉及的研究内容较多, 它们的目标和重心各不相同, 使得读者难以全面地了解事件抽取包含的研究任务、研究问题以及未来的热点趋势. 尽管现有的少量事件抽取综述梳理了相关成果, 但存在以下局限: 1)研究任务及其研究进展的梳理不清晰; 2)仅从技术路线的角度进行梳理. 由于不同研究任务下的不同研究问题的解决技术不宜一起对比, 因此这样的梳理方式不利于清晰地展示事件抽取在不同方面的研究进展情况. 为此, 面向研究问题对基于深度学习的事件抽取研究成果重新回顾整理. 首先, 界定事件的相关概念, 论述事件抽取的研究任务, 明确各研究任务的目标, 再梳理各任务上的代表性研究成果; 然后, 总结现有事件抽取成果主要致力于解决哪些方面的研究问题, 分析为什么会存在这些问题、为什么需要解决这些问题的原因; 紧接着对每个方面的研究问题进行技术路线梳理, 分析各自的大体研究方案以及研究推进的过程. 最后, 讨论事件抽取可能的发展趋势.
- 事件抽取 /
- 研究任务 /
- 研究进展及解决方案 /
- 深度学习
Abstract: Event extraction is a long-standing and challenging task in natural language processing and has achieved encouraging results. Given various research targets and concerns, it is difficult for readers to comprehensively understand the situations and trends of event extraction. Although limited survey has reviewed related results on event extraction, it shares two limitations, including 1) insufficient depiction for research tasks and development, and 2) focusing on technical classification instead of research issues. Since it is not suitable to compare the techniques of various research issues under different research tasks, such a combing method is not conducive to clearly demonstrate the research development of diverse aspects of event extraction. To tackle these weaknesses, we review event extraction studies from the perspectives of research tasks, research issues and corresponding solving methods. Specifically, the event definition is discussed first, followed by an elaborate description and analysis for research tasks to clarify the targets of diverse research tasks. Meanwhile, we hackle the representative research results. Then, we summarize what aspects of event extraction the existing studies are mainly devoted to solving, and analyze why these issues exist and why they need to be addressed. Subsequently, the technical line of each aspect is sorted out to investigate the development and advancement of each other. Finally, we explore the future direction of event extraction.
- Event extraction /
- research tasks /
- research development and solutions /
- deep learning

HTML全文

图 1 事件识别及其要素抽取的任务框架

Fig. 1 Task framework of event recognition and event element extraction

下载: 全尺寸图片幻灯片

图 2 各任务上的代表性研究成果

Fig. 2 Representative research results for each task

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图 3 语句级事件抽取的主要发展历程

Fig. 3 Main development of sentence-level event extraction

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图 4 文档级事件抽取的主要发展历程

Fig. 4 Main development of document-level event extraction

下载: 全尺寸图片幻灯片

图 5 各模型在DuEE-Fin语料上各事件类型下的F1值

Fig. 5 F1 scores of models under each event type on DuEE-Fin corpus

下载: 全尺寸图片幻灯片

表 1 各模型在ChFinAnn语料上各事件类型下的F1值 (%)

Table 1 F1 scores of models under each event type on ChFinAnn corpus (%)

模型	冻结	回购	减持	增持	质押	平均
DCFEE-O	51.1	83.1	45.3	46.6	63.9	58.0
DCFEE-M	45.6	80.8	44.2	44.9	62.9	55.7
Greedy-Dec	58.9	78.9	51.2	51.3	62.1	60.5
Doc2EDAG	70.2	87.3	71.8	75.0	77.3	76.3
GIT	73.4	90.8	74.3	76.3	77.7	78.5
DE-PPN	73.5	87.4	74.4	75.8	78.4	77.9
SCDEE	80.4	90.5	75.1	70.1	78.1	78.8
PTPCG	71.4	91.6	71.5	72.2	76.4	76.6
ReDEE	74.1	90.7	75.3	78.1	80.1	79.7
TER-MCEE	87.9	97.2	89.8	91.2	78.6	88.9
EDEE	97.4	90.3	93.2	93.4	96.2	94.1
ProCNet	75.7	93.7	76.0	72.0	81.3	79.7

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表 2 处理训练语料不足问题的各方法对比

Table 2 Comparison of methods that handling the problem of insufficient training corpus

方法	本质	需要的数据	解决方式
远程监督	利用外部知识库扩展数据	少量标注数据	直接增加
半监督	少量标注训练模型预测大量无标签数据	少量标注数据+大量无标签数据	直接增加、不增加
无监督	直接根据数据特点或性质判断	大量无标签数据	无标注数据
自监督	从无标签数据中挖掘监督信息用于训练	大量无标签数据	无标注数据
弱监督	针对数据集不可靠情况, 包含3种典型情况	少量标注数据+大量无标签数据	直接增加
主动学习	通过机器学习挑选有用的样本给人工标注	少量标注数据+大量无标签数据	直接增加
强化学习	中途告知学习情况	大量无标签数据	无标注数据
元学习	通过多个任务的数据学习内涵/规律/学习的本领	其他任务或领域的数据	其他领域增加
迁移学习	其它任务/领域下的模型用于目标任务/领域	其它领域的大量数据	其他领域增加
少样本学习	一种任务, 少样本下学习本领	极少的标注数据	直接增加、间接增加、不增加、其他领域数据
零样本学习	一种任务, 零样本下学习本领	给出代表某一类物体语义的嵌入向量	无标注数据

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