Survey on Document-Level Event Extraction Based on Deep Learning

doi:10.3778/j.issn.1002-8331.2205-0397

Abstract

Abstract: Event extraction is a very important and challenging task in the field of information extraction, which plays a pivotal role in the construction of event evolutionary graph, network monitoring system, and situation awareness. At present, sentence-level event extraction is the most researched task, while document-level event extraction based on deep learning has been created in response to the difficulties of argument-scattering and multi-events, sentence-level. Following a summary of the definition, primary tasksand challenges of document-level event extraction, four distinct dimensions are covered in more detail, including gathering semantic information at various word, sentence, and document granularities, capturing document structural features to model various graph structures, fusing semantic information with structural features, and incorporating event extraction into other task solutions like reading comprehension and intelligent question answer. On the basis of a detailed discussion of the models and techniques connected to domain document-level event extraction, the datasets that are used most frequently are compiled, and the extraction results of common techniques are assessed and contrasted. Finally, document-level event extraction research trends are discussed.

Key words: document-level event extraction, argument-scattering, multi-events, deep learning, evaluation metrics

摘要： 事件抽取是信息抽取领域中一项十分重要且具有挑战性的任务，在事理图谱构建、舆情监控、态势感知等方面起着举足轻重的作用。目前研究较多的是句子级事件抽取，而面对“论元分散”和“多事件”的挑战，基于深度学习的篇章级事件抽取陆续展开。总结了篇章级事件抽取的定义、主要任务和面临的挑战，分别从获取词语、句子和文档三种不同粒度的语义信息，捕获文档结构特征建模不同的图结构，融合语义信息和结构特征，以及将事件抽取转化为阅读理解、智能问答等其他任务解决方案等四个不同的维度，详细讨论了近年来篇章级事件抽取相关的模型和方法，在此基础上归纳了常用数据集，并对典型方法的抽取效果进行了评估和对比。展望了篇章级事件抽取的研究趋势。

关键词: 论元分散, 多事件, 深度学习, 评价指标

HU Ruijuan, ZHOU Huijuan, LIU Haiyan, LI Jian. Survey on Document-Level Event Extraction Based on Deep Learning[J]. Computer Engineering and Applications, 2022, 58(24): 47-60.

胡瑞娟, 周会娟, 刘海砚, 李健. 基于深度学习的篇章级事件抽取研究综述[J]. 计算机工程与应用, 2022, 58(24): 47-60.

References

[1] WU X D，WU J，FU X Y，et al.Automatic knowledge graph construction：a report on the 2019 ICDM/ICBK contest[C]//2019 IEEE International Conference on Data Mining.Beijing，China：IEEE，2019：1540-1545.
[2] BOSSELUT A，CHOI Y.Dynamic knowledge graph construction for zero-shot commonsense question answering[C]//Association for the Advancement of Artificial Intelligence（AAAI），2021.
[3] LIU C Y，ZHOU C，JIA W，et al.CPMF：a collective pair wise matrix factorization model for upcoming event rec-ommendation[C]//2017 International Joint Conference on Neural Networks（IJCNN），2017：1532-1539.
[4] GAO L，WU J，QIAO Z，et al.Collaborative social group influence for event recommendation[C]//Proceedings of the 25th ACM International Conference on Information and Knowledge Management，Indianapolis，Indiana，USA.New York，NY，USA：ACM，2016：1941-1944.
[5] BOYD-GRABER J，B?RSCHINGER B.What question answering can learn from trivia nerds[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Stroudsburg，PA，USA：Association for Computational Linguistics，2020.
[6] CAO Q Q，TRIVEDI H，BALASUBRAMANIAN A，et al.DeFormer：decomposing pre-trained transformers for faster question answering[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Stroudsburg，PA，USA：Association for Computational Linguistics，2020.
[7] SU X，XUE S，LIU F，et al.A comprehensive survey on community detection with deep learning[J].IEEE Transactions on Neural Networks and Learning Systems，2021：1-29.
[8] LIU F Z，XUE S，WU J，et al.Deep learning for community detection：progress，challenges and opportunities[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence，July 11-17，2020.
[9] MA X X，WU J，XUE S，et al.A comprehensive survey on graph anomaly detection with deep learning[J].IEEE Transactions on Knowledge and Data Engineering，2021：1-31.
[10] 高李政，周刚，罗军勇，等.元事件抽取研究综述[J].计算机科学，2019，46（8）：9-15.
GAO L Z，ZHOU G，LUO J Y，et al.Survey on meta-event extraction[J].Computer Science，2019，46（8）：9-15.
[11] DODDINGTON G.The automatic content extraction （ACE） program-tasks，data，and evaluation[C]//Proceedings of the 2004 International Conference on Language Resources and Evaluation，2004.
[12] QI L，JI H，LIANG H.Joint event extraction via structured prediction with global features[C]//Meeting of the Association for Computational Linguistics，2013.
[13] NGUYEN T H，GRISHMAN R.Event detection and domain adaptation with convolutional neural networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing（Volume 2：Short Papers），Beijing，China.Stroudsburg，PA，USA：Association for Computational Linguistics，2015：365-371.
[14] CHEN Y B，XU L H，LIU K，et al.Event extraction via dynamic multi-pooling convolutional neural networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing（Volume 1：Long Papers），Beijing，China.Stroudsburg，PA，USA：Association for Computational Linguistics，2015.
[15] FENG X C，QIN B，LIU T.A language-independent neural network for event detection[J].Science China Information Sciences，2018，61（9）：1-12.
[16] HOGENBOOM F，FRASINCAR F，KAYMAK U，et al.A survey of event extraction methods from text for decision support systems[J].Decision Support Systems，2016，85：12-22.
[17] YANG H，CHEN Y B，LIU K，et al.DCFEE：a document-level Chinese financial event extraction system based on automatically labeled training data[C]//Proceedings of ACL 2018，System Demonstrations，Melbourne，Australia.Stroudsburg，PA，USA：Association for Computational Linguistics，2018.
[18] NGUYEN T H，CHO K，GRISHMAN R.Joint event extraction via recurrent neural networks[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics：Human Language Technologies，San Diego，California.Stroudsburg，PA，USA：Association for Computational Linguistics，2016.
[19] CHAN Y S，FASCHING J，QIU H，et al.Rapid customi-
zation for event extraction[J].arXiv：1809.07783，2018.
[20] CAO Y，PENG H，WU J，et al.Knowledge-preserving incremental social event detection via heterogeneous GNNs[C]//Proceedings of the Web Conference，2021：3383-3395.
[21] LU Y，LIN H，XU J，et al.Text2event：controllable sequence-to-structure generation for end-to-end event extraction[J].arXiv：2106.09232，2021.
[22] SUBBURATHINAM A，LU D，JI H，et al.Cross-lingual structure transfer for relation and event extraction[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing（EMNLP-IJCNLP），Hong Kong，China.Stroudsburg，PA，USA：Association for Computational Linguistics，2019.
[23] MIKOLOV T，CHEN K，CORRADO G，et al.Efficient estimation of word representations in vector space[J].arXiv：1301.3781，2013
[24] PENNINGTON J，SOCHER R，MANNING C.Glove：global vectors for word representation[C]//Conference on Empirical Methods in Natural Language Processing，2014.
[25] PETERS M，NEUMANN M，IYYER M，et al.Deep contextualized word representations[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics：Human Language Technologies，Volume 1（Long Papers），New Orleans，Louisiana.Stroudsburg，PA，USA：Association for Computational Linguistics，2018.
[26] DEVLIN J，CHANG M W，LEE K，et al.BERT：pre-training of deep bidirectional transformers for language understanding[J].arXiv：1810.04805，2018.
[27] ZHU Z，LI S S，ZHOU G D，et al.Bilingual event extraction：a case study on trigger type determination[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics（Volume 2：Short Papers），Baltimore，Maryland.Stroudsburg，PA，USA：Association for Computational Linguistics，2014.
[28] 罗明，黄海量.基于词汇-语义模式的金融事件信息抽取方法[J].计算机应用，2018，38（1）：84-90.
LUO M，HUANG H L.Information extraction method of financial events based on lexical-semantic pattern[J].Journal of Computer Applications，2018，38（1）：84-90.
[29] LIU S L，CHEN Y B，HE S Z，et al.Leveraging FrameNet to improve automatic event detection[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics（Volume 1：Long Papers），Berlin，Germany.Stroudsburg，PA，USA：Association for Computational Linguistics，2016.
[30] 游飞.基于深度学习的军事事件抽取研究[D].北京：中国电子科技集团公司电子科学研究院，2018.
YOU F.Research on military event extraction based on deep learning[D].Beijing：Electronic Science Research Institute of China Electronics Technology Corporation，2018.
[31] 路扬.面向小样本不平衡数据的生物医学事件抽取方法研究[D].长春：吉林大学，2019.
LU Y.Research on biomedical event extraction method for small sample unbalanced data[D].Changchun：Jilin University，2019.
[32] 田梓函，李欣.基于BERT-CRF模型的中文事件检测方法研究[J].计算机工程与应用，2021，57（11）：135-139.
TIAN Z H，LI X.Research on Chinese event detection method based on BERT-CRF model[J].Computer Engineering and Applications，2021，57（11）：135-139.
[33] LIU S L，CHEN Y B，LIU K，et al.Exploiting argument information to improve event detection via supervised attention mechanisms[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics（Volume 1：Long Papers），Vancouver，Canada.Stroudsburg，PA，USA：Association for Computational Linguistics，2017.
[34] 黄细凤.基于动态掩蔽注意力机制的事件抽取[J].计算机应用研究，2020，37（7）：1964-1968.
HUANG X F.Event extraction based on dynamic masked attention[J].Application Research of Computers，2020，37（7）：1964-1968.
[35] 曹渝昆，孙涛.基于GLSTM和Attention的中文事件要素提取[J].计算机工程与应用，2022，58（6）：157-163.
CAO Y K，SUN T.Chinese event argument extraction based on GLSTM and Attention[J].Computer Engineering and Applications，2022，58（6）：157-163.
[36] LIU X，LUO Z C，HUANG H Y.Jointly multiple events extraction via attention-based graph information aggrega-tion[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing，Brussels，Belgium.Stroudsburg，PA，USA：Association for Computational Linguistics，2018.
[37] YANG S，FENG D W，QIAO L B，et al.Exploring pre-trained language models for event extraction and gen-eration[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics，Florence，Italy.Stroudsburg，PA，USA：Association for Computational Linguistics，2019.
[38] WADDEN D，WENNBERG U，LUAN Y，et al.Entity，relation，and event extraction with contextualized span representations[J].arXiv：1909.03546，2019.
[39] LOU D，LIAO Z，DENG S，et al.MLBiNet：a cross-sentence collective event detection network[J].arXiv：2105.09458.2021.
[40] DU X Y，CARDIE C.Document-level event role filler extraction using multi-granularity contextualized encod-ing[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Stroudsburg，PA，USA：Association for Computational Linguistics，2020.
[41] SHA L，QIAN F，CHANG B B，et al.Jointly extracting event triggers and arguments by dependency-bridge RNN and tensor-based argument interaction[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2018.
[42] MARCHEGGIANI D，TITOV I.Encoding sentences with graph convolutional networks for semantic role labeling[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing，Copenhagen，Denmark.Stroudsburg，PA，USA：Association for Computational Linguistics，2017：1506-1515.
[43] CUI S，YU B，LIU T，et al.Edge-enhanced graph convolution networks for event detection with syntactic relation[C]//Conference on Empirical Methods in Natural Language Processing（EMNLP），2020：2329-2339.
[44] AHMAD W U，PENG N，CHANG K W.GATE：graph attention transformer encoder for cross-lingual relation and event extraction[C]//Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence，2021：74-75.
[45] 刘一仝.篇章级事件表示及相关性计算[D].哈尔滨：哈尔滨工业大学，2019.
LIU Y T.Document-level event representation and correlation calculation[D].Harbin：Harbin Institute of Technology，2019.
[46] ZHENG S，CAO W，XU W，et al.Doc2EDAG：an end-to-end document-level framework for Chinese financial event extraction[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing（EMNLP-IJCNLP），Hong Kong，China.Stroudsburg，PA，USA：Association for Computational Linguistics，2019：337-346.
[47] XU R，LIU T，LI L，et al.Document-level event extrac-tion via heterogeneous graph-based interaction model with a tracker[J].arXiv：2105.14924 2021.
[48] YANG H，SUI D B，CHEN Y B，et al.Document-level event extraction via parallel prediction networks[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing（Volume 1：Long Papers）.Stroudsburg，PA，USA：Association for Computational Linguistics，2021：6298-6308.
[49] LEVY O，SEO M，CHOI E，et al.，Zero-shot relation extraction via reading comprehension[J].arXiv：1706.04115，2017.
[50] LIU J，CHEN Y B，LIU K，et al.Event extraction as machine reading comprehension[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing（EMNLP）.Stroudsburg，PA，USA：Association for Computational Linguistics，2020：1641-1651.
[51] DU X，CARDIE C.Event extraction by answering（almost） natural questions[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing（EMNLP），2020：671-683.
[52] MEHTA S，RANGWALA H，RAMAKRISHNAN N.Improving zero-shot event extraction via sentence simplification[J].arXiv：2204.02531，2022.
[53] CHEN M，WU F，WANG Z，et al.Chinese event argument extraction using reading comprehension framework[C]//Proceedings of the 19th Chinese National Conference on Computational Linguistics，2020：376-389.
[54] CHEN S，WANG Y，LIU J，et al.Bidirectional machine reading comprehension for aspect sentiment triplet extrac-tion[J].arXiv：2103.07665，2021.
[55] KWOK C，ETZIONI O，WELD D S.Scaling question answering to the web[J].ACM Transactions on Information Systems，2001，19（3）：242-262.
[56] MEKALA D，SHANG J B.Contextualized weak super-vision for text classification[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Stroudsburg，PA，USA：Association for Computational Linguistics，2020：323-333.
[57] WANG J，HAN B，WANG F，et al.Document-level core events extraction based on QA[J].Journal of Physics：Conference Series，2022，2171（1）：012062.
[58] LI F，PENG W，CHEN Y，et al.Event extraction as multi-turn question answering[C]//Findings of the Association for Computational Linguistics，2020：829-838.
[59] CHEN B，LIN X，THOMAS C，et al.Joint multimedia event extraction from video and article[J].arXiv：2109.12776，2021.
[60] XU Z，WANG Y，BAI L，et al.Writing style aware docu-ment-level event extraction[J].arXiv：2201.03188，2022.
[61] SHA L，LI S，CHANG B，et al.Joint learning templates and slots for event schema induction[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics：Human Language Technologies.Stroudsburg，USA：ACL，2016：428-434.
[62] HUANG L F，CASSIDY T，FENG X C，et al.Liberal event extraction and event schema induction[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics（Volume 1：Long Papers），Berlin，Germany.Stroudsburg，PA，USA：Association for Computational Linguistics，2016：258-268.
[63] FERGUSON J，LOCKARD C，WELD D S，et al.Semi-supervised event extraction with paraphrase clusters[J].arXiv：1808.08622，2018.
[64] AHN N.Inducing event types and roles in reverse：using function to discover theme[C]//Proceedings of the Events and Stories in the News Workshop，Vancouver，Canada.Stroudsburg，PA，USA：Association for Computational Linguistics，2017：66-76.
[65] LIU X，HUANG H Y，ZHANG Y.Open domain event extraction using neural latent variable models[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics，Florence，Italy.Stroudsburg，PA，USA：Association for Computational Linguistics，2019：2860-2871.
[66] WANG R，ZHOU D，HE Y.Open event extraction from online text using a generative adversarial network[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing，2019：282-291.
[67] MODI A，TITOV I.Inducing neural models of script knowledge[C]//Proceedings of the Eighteenth Conference on Computational Natural Language Learning，Ann Arbor，Michigan.Stroudsburg，PA，USA：Association for Computational Linguistics，2014：49-57.
[68] RUDINGER R，RASTOGI P，FERRARO F，et al.Script induction as language modeling[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing，Lisbon，Portugal.Stroudsburg，PA，USA：Association for Computational Linguistics，2015：1681-1686.
[69] PICHOTTA K，MOONEY R J.Using sentence-level LSTM language models for script inference[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics（Volume 1：Long Papers），Berlin，Germany.Stroudsburg，PA，USA：Association for Computational Linguistics，2016：279-289.
[70] NAIK A，ROSE C.Towards open domain event trigger identification using adversarial domain adaptation[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Stroudsburg，PA，USA：Association for Computational Linguistics，2020：7618-7624.
[71] HUANG J，LI C，SUBUDHI K，et al.Few-shot named entity recognition：a comprehensive study[J].arXiv：2012.
14978，2020.
[72] MA R T，ZHOU X，GUI T，et al.Template-free prompt tuning for few-shot NER[J].arXiv：2109.13532，2021.
[73] MA Y B，WANG Z H，CAO Y X，et al.Prompt for extraction?PAIE：prompting argument interaction for event argument extraction[J].arXiv：2202.12109，2022.
[74] LIU P F，YUAN W Z，FU J L，et al.Pre-train，prompt，and predict：a systematic survey of prompting methods in natural language processing[J].arXiv：2107.13586，2021.
[75] DAS S S S，KATIYAR A，PASSONNEAU R J，et al.Container：few-shot named entity recognition via contrastive learning[J].arXiv：2109.07589，2021.
[76] LIN J J，JIAN J，CHEN Q.Eliciting knowledge from language models for event extraction[J].arXiv：2109.05190，2021.
[77] MCLEAN V.Fourth message understanding conference （muc-4）[C]//Proceedings of Fourth Message Understanding Conference（MUC-4），1992.
[78] LEETARU K，SCHRODT P A.Gdelt：global data on events，location，and tone，1979-2012[C]//Annual Meeting of the International Studies Association，2013.
[79] EBNER S，XIA P，CULKIN R，et al.Multi-sentence argument linking[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Stroudsburg，PA，USA：Association for Computational Linguistics，2020：8057-8077.
[80] LI S，JI H，HAN J W.Document-level event argument extraction by conditional generation[C]//Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics：Human Language Technologies.Stroudsburg，PA，USA：Association for Computational Linguistics，2021：894-908.
[81] LI X Y，LI F Y，PAN L，et al.DuEE：a large-scale dataset for Chinese event extraction in real-world scenarios[C]//Natural Language Processing and Chinese Computing，2020：534-545.
[82] 张亚军，刘宗田，李强，等.面向事件的中文指代语料库的构建[J].上海大学学报（自然科学版），2018，24（6）：900-911.
ZHANG Y J，LIU Z T，LI Q，et al.Construction of event-oriented Chinese reference corpus[J].Journal of Shanghai University（Natural Science Edition），2018，24（6）：900-911.
[83] WANG X，WANG Z，HAN X，et al.MAVEN：a massive general domain event detection dataset[J].arXiv：2004.
13590，2020.
[84] DENG S M，ZHANG N Y，KANG J J，et al.Meta-learning with dynamic-memory-based prototypical network for few-shot event detection[C]//WSDM’20：Proceedings of the 13th International Conference on Web Search and Data Mining，2020：151-159.
[85] 周梦迪.融合结构信息的小样本关系抽取技术研究[D].杭州：浙江大学，2020.
ZHOU M D.Research on few-shot learning for relation extraction with structure information[D].Hangzhou：Zhejiang University，2020.
[86] 罗萍，丁玲，杨雪，等.基于数据增强和弱监督对抗训练的中文事件检测[J].计算机应用，2022（1）：1-7.
LUO P，DING L，YANG X，et al.Chinese event detection based on data augmentation and weakly supervised adversarial training[J].Journal of Computer Applications，2022（1）：1-7.
[87] AMIT S.Introducing the knowledge graph[R].America： Official Blog of Google，2012.