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              面向研究問題的深度學習事件抽取綜述

              萬齊智 萬常選 胡蓉 劉德喜 劉喜平 廖國瓊

              萬齊智, 萬常選, 胡蓉, 劉德喜, 劉喜平, 廖國瓊. 面向研究問題的深度學習事件抽取綜述. 自動化學報, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c230184
              引用本文: 萬齊智, 萬常選, 胡蓉, 劉德喜, 劉喜平, 廖國瓊. 面向研究問題的深度學習事件抽取綜述. 自動化學報, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c230184
              Wan Qi-Zhi, Wan Chang-Xuan, Hu Rong, Liu De-Xi, Liu Xi-Ping, Liao Guo-Qiong. Event extraction based on deep learning: a survey of research issue. Acta Automatica Sinica, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c230184
              Citation: Wan Qi-Zhi, Wan Chang-Xuan, Hu Rong, Liu De-Xi, Liu Xi-Ping, Liao Guo-Qiong. Event extraction based on deep learning: a survey of research issue. Acta Automatica Sinica, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c230184

              面向研究問題的深度學習事件抽取綜述

              doi: 10.16383/j.aas.c230184
              基金項目: 國家自然科學基金(619721184, 62272205, 62272206, 62272206, 62076112), 江西省教育廳科學技術項目(GJJ210531), 江西省自然科學基金(20212ACB202002, 20232ACB202008), 江西省主要學科學術和技術帶頭人培養計劃領軍人才項目(20213BCJL22041) 資助
              詳細信息
                作者簡介:

                萬齊智:江西財經大學信息管理學院講師. 主要研究方向為人工智能, 深度學習, 信息抽取, 自然語言處理, 文本數據挖掘. E-mail: wanqizhi1006@163.com

                萬常選:江西財經大學信息管理學院教授. 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:江西財經大學信息管理學院博士研究生. 主要研究方向為信息抽取, 自然語言處理和大數據分析. E-mail: hurong2014@126.com

                劉德喜:江西財經大學信息管理學院教授. 主要研究方向為自然語言處理和信息檢索. E-mail: dexi.liu@163.com

                劉喜平:江西財經大學信息管理學院教授.主要研究方向為信息檢索和數據挖掘. E-mail: liuxiping@jxufe.edu.cn

                廖國瓊:江西財經大學虛擬現實現代產業院教授. 主要研究方向為數據庫和數據挖掘. E-mail: liaoguoqiong@163.com

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

              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: 事件抽取是一個歷史悠久且極具挑戰的研究任務, 取得了大量優異的成果. 由于事件抽取涉及的研究內容較多, 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、研究問題以及未來的熱點趨勢. 盡管現有的少量事件抽取綜述梳理了相關成果, 但存在以下局限: 1)研究任務及其研究進展的梳理不清晰; 2)僅從技術路線的角度進行梳理. 由于不同研究任務下的不同研究問題的解決技術不宜一起對比, 因此這樣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進展情況. 為此, 面向研究問題對基于深度學習的事件抽取研究成果重新回顧整理. 首先, 界定事件的相關概念, 論述事件抽取的研究任務, 明確各研究任務的目標, 再梳理各任務上的代表性研究成果; 然后, 總結現有事件抽取成果主要致力于解決哪些方面的研究問題, 分析為什么會存在這些問題、為什么需要解決這些問題的原因; 緊接著對每個方面的研究問題進行技術路線梳理, 分析各自的大體研究方案以及研究推進的過程. 最后, 討論事件抽取可能的發展趨勢.
              • 圖  1  事件識別及其要素抽取的任務框架

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

                圖  2  各任務上的代表性研究成果

                Fig.  2  Representative research results for each task

                圖  3  語句級事件抽取的主要發展歷程

                Fig.  3  Main development of sentence-level event extraction

                圖  4  文檔級事件抽取的主要發展歷程

                Fig.  4  Main development of document-level event extraction

                圖  5  各模型在DuEE-Fin語料上各事件類型下的F1值

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

                表  1  各模型在ChFinAnn語料上各事件類型下的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
                下載: 導出CSV

                表  2  處理訓練語料不足問題的各方法對比

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

                方法 本質 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標簽數據 少量標注數據+大量無標簽數據 直接增加、不增加
                無監督 直接根據數據特點或性質判斷 大量無標簽數據 無標注數據
                自監督 從無標簽數據中挖掘監督信息用于訓練 大量無標簽數據 無標注數據
                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+大量無標簽數據 直接增加
                主動學習 通過機器學習挑選有用的樣本給人工標注 少量標注數據+大量無標簽數據 直接增加
                強化學習 中途告知學習情況 大量無標簽數據 無標注數據
                元學習 通過多個任務的數據學習內涵/規律/學習的本領 其他任務或領域的數據 其他領域增加
                遷移學習 其它任務/領域下的模型用于目標任務/領域 其它領域的大量數據 其他領域增加
                少樣本學習 一種任務, 少樣本下學習本領 極少的標注數據 直接增加、間接增加、不增加、其他領域數據
                零樣本學習 一種任務, 零樣本下學習本領 給出代表某一類物體語義的嵌入向量 無標注數據
                下載: 導出CSV
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