面向研究問(wèn)題的深度學(xué)習事件抽取綜述

萬(wàn)齊智; 萬(wàn)常選; 胡蓉; 劉德喜; 劉喜平; 廖國瓊

doi:10.16383/j.aas.c230184

面向研究問(wèn)題的深度學(xué)習事件抽取綜述

doi: 10.16383/j.aas.c230184 cstr: 32138.14.j.aas.c230184

萬(wàn)齊智^{1, 2,},
萬(wàn)常選^{1, 2,},
胡蓉^{1, 2,},
劉德喜^{1, 2,},
劉喜平^{1, 2,},
廖國瓊^{1, 3,}

1.
江西財經(jīng)大學(xué)計算機與人工智能學(xué)院南昌 330032
2.
江西財經(jīng)大學(xué)數據與知識工程江西省高校重點(diǎn)實(shí)驗室南昌 330013
3.
江西財經(jīng)大學(xué)虛擬現實(shí)現代產(chǎn)業(yè)學(xué)院南昌 330032

基金項目: 國家自然科學(xué)基金(62272205, 619721184, 62272206, 62076112), 江西省教育廳科學(xué)技術(shù)項目(GJJ2400411), 江西省自然科學(xué)基金(20242BAB25119, 20232ACB202008), 江西省主要學(xué)科學(xué)術(shù)和技術(shù)帶頭人培養計劃領(lǐng)軍人才項目(20213BCJL22041)資助

詳細信息

作者簡(jiǎn)介:
萬(wàn)齊智：江西財經(jīng)大學(xué)計算機與人工智能學(xué)院講師. 主要研究方向為人工智能, 深度學(xué)習, 信息抽取, 自然語(yǔ)言處理和文本數據挖掘. E-mail: wanqizhi1006@163.com

萬(wàn)常選：江西財經(jīng)大學(xué)計算機與人工智能學(xué)院教授. 主要研究方向為Web數據管理, 情感分析, 數據挖掘和信息檢索. 本文通信作者. E-mail: wanchangxuan@263.net

胡蓉：江西財經(jīng)大學(xué)計算機與人工智能學(xué)院博士研究生. 主要研究方向為信息抽取, 自然語(yǔ)言處理和大數據分析. E-mail: hurong2014@126.com

劉德喜：江西財經(jīng)大學(xué)計算機與人工智能學(xué)院教授. 主要研究方向為自然語(yǔ)言處理, 信息檢索. E-mail: dexi.liu@163.com

劉喜平：江西財經(jīng)大學(xué)計算機與人工智能學(xué)院教授. 主要研究方向為信息檢索, 數據挖掘. E-mail: liuxiping@jxufe.edu.cn

廖國瓊：江西財經(jīng)大學(xué)虛擬現實(shí)現代產(chǎn)業(yè)學(xué)院教授. 主要研究方向為數據庫和數據挖掘. E-mail: liaoguoqiong@163.com

計量
- 文章訪(fǎng)問(wèn)數: 971
- HTML全文瀏覽量: 493
- PDF下載量: 120
- 被引次數: 0
出版歷程
- 收稿日期: 2023-04-06
- 錄用日期: 2023-09-08
- 網(wǎng)絡(luò )出版日期: 2023-10-07
- 刊出日期: 2024-11-26

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

Funds: Supported by National Natural Science Foundation of China (62272205, 619721184, 62272206, 62076112), Science & Technology Project of the Department of Education of Jiangxi Province (GJJ2400411), Natural Science and Foundation of Jiangxi Province (20242BAB25119, 20232ACB202008), and Fundation Programme for Academic and Technical Leaders in Major Disciplines of Jiangxi Province (20213BCJL22041)

More Information

Author Bio:
WAN Qi-Zhi　Lecturer at the School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics. His research interest covers artificial intelligence, deep learning, information extraction, natural language processing, and text data mining

WAN Chang-Xuan　Professor at the School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics. His research interest covers Web data management, sentiment analysis, data mining, and information retrieval. Corresponding author of this paper

HU Rong　Ph.D. candidate at the School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics. Her research interest covers information extraction, natural language processing, and big data analysis

LIU De-Xi　Professor at the School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics. His research interest covers natural language processing and information retrieval

LIU Xi-Ping　Professor at the School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics. His research interest covers information retrieval and data mining

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

摘要

摘要: 事件抽取是一個(gè)歷史悠久且極具挑戰的研究任務(wù), 近年來(lái)取得了大量?jì)?yōu)異成果. 由于事件抽取涉及的研究?jì)热葺^多, 它們的目標和重心各不相同, 使得讀者難以全面地了解事件抽取包含的研究任務(wù)、研究問(wèn)題和未來(lái)熱點(diǎn)趨勢. 為此, 面向研究問(wèn)題, 對基于深度學(xué)習的事件抽取研究成果進(jìn)行整理. 首先, 界定事件相關(guān)概念, 論述事件抽取的研究任務(wù), 明確各研究任務(wù)的目標, 再總結各任務(wù)上的代表性研究成果; 接著(zhù), 總結現有事件抽取成果主要致力于解決哪些方面研究問(wèn)題, 分析為什么會(huì )存在這些問(wèn)題, 分析為什么需要解決這些問(wèn)題; 然后, 對各方面研究問(wèn)題進(jìn)行技術(shù)總結, 分析各自研究方案和研究推進(jìn)過(guò)程; 最后, 討論事件抽取的發(fā)展趨勢.
- 事件抽取 /
- 研究問(wèn)題 /
- 研究進(jìn)展及解決方案 /
- 深度學(xué)習
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. Therefore, 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, the representative research achievements in various tasks are summarized. Then, the main aspects of research problems that existing event extraction achievements focus on addressing, why these problems exist, and why they need to be resolved, are analyzed. Subsequently, the technical line of each aspect is sorted out to investigate the development and advancement of each other. Finally, the future direction of event extraction is discussed.
- Event extraction /
- research issue /
- research development and solutions /
- deep learning

HTML全文

圖 1 事件識別及其要素抽取的任務(wù)框架

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

下載: 全尺寸圖片幻燈片

圖 2 各任務(wù)上的代表性研究成果

Fig. 2 Representative research results for each task

下載: 全尺寸圖片幻燈片

圖 3 語(yǔ)句級事件抽取的主要發(fā)展歷程

Fig. 3 Main development of sentence-level event extraction

下載: 全尺寸圖片幻燈片

圖 4 文檔級事件抽取的主要發(fā)展歷程

Fig. 4 Main development of document-level event extraction

下載: 全尺寸圖片幻燈片

圖 5 各模型在DuEE-Fin語(yǔ)料上各事件類(lèi)型下的F1值

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

下載: 全尺寸圖片幻燈片

表 1 各模型在ChFinAnn語(yǔ)料上各事件類(lèi)型下的F1值 (%)

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

模型	凍結	回購	減持	增持	質(zhì)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
GreedyDec	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

下載: 導出CSV

表 2 處理訓練語(yǔ)料不足問(wèn)題的各方法比較

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

方法	本質(zhì)	需要的數據	解決方式
遠程監督	利用外部知識庫擴展數據	少量標注數據	直接增加
半監督	少量標注訓練模型預測大量無(wú)標簽數據	少量標注數據加大量無(wú)標簽數據	直接增加、不增加
無(wú)監督	直接根據數據特點(diǎn)或性質(zhì)判斷	大量無(wú)標簽數據	不使用標注數據
自監督	從無(wú)標簽數據中挖掘監督信息用于訓練	大量無(wú)標簽數據	不使用標注數據
弱監督	針對數據集不可靠情況, 包含3種典型情況	少量標注數據加大量無(wú)標簽數據	直接增加
主動(dòng)學(xué)習	通過(guò)機器學(xué)習挑選有用的樣本給人工標注	少量標注數據加大量無(wú)標簽數據	直接增加
強化學(xué)習	中途告知學(xué)習情況	大量無(wú)標簽數據	無(wú)標注數據
元學(xué)習	通過(guò)多個(gè)任務(wù)的數據學(xué)習內涵/規律/學(xué)習的本領(lǐng)	其他任務(wù)或領(lǐng)域的數據	其他領(lǐng)域增加
遷移學(xué)習	其他任務(wù)/領(lǐng)域下的模型用于目標任務(wù)/領(lǐng)域	其他領(lǐng)域的大量數據	其他領(lǐng)域增加
小樣本學(xué)習	一種任務(wù), 小樣本下學(xué)習本領(lǐng)	極少的標注數據	直接增加、間接增加、不增加、其他領(lǐng)域增加
零樣本學(xué)習	一種任務(wù), 零樣本下學(xué)習本領(lǐng)	給出代表某一類(lèi)物體語(yǔ)義的嵌入向量	不使用標注數據

下載: 導出CSV

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