基于双重注意力的无触发词中文事件检测

doi:10.11896/jsjkx.211000071

Abstract

Abstract: Event extraction is an essential task of natural language processing,and event detection is one of the critical steps of event extraction,whose goal is to detect the occurrence of events and classify them.Currently,Chinese event detection has problems of polysemous words and mismatches between words and triggers,which affect the accuracy of event detection models.We propose the event detection without triggers based on dual attention(EDWTDA),which skips the process of trigger word recognition and directly determines event types without trigger word tags.First,the ALBERT model is applied to improve the semantic representation ability of word embedding vectors.Second,we fusion local attention and event types to capture key semantic information and simulate hidden event triggers to solve the problem of mismatch between words and triggers.Third,the global attention is introduced to mine contextual information in documents to solve the problem of polysemous words.Further,the event detection task is converted into a binary classification task for solving multi-label problem.Finally,the focal loss function is used to address the sample imbalance after conversion.Experimental results on the ACE2005 Chinese corpus show that compared with the best baseline model JMCEE,the accuracy rate,recall rate,and F1-score of the proposed model increases by 3.40%,3.90% and 3.67%,respectively.

Key words: Double attention, Without triggers, Chinese event detection, ACE2005, Binary classification

CLC Number:

TP391

CHENG Yong, MAO Yingchi, WAN Xu, WANG Longbao, ZHU Min. Chinese Event Detection Without Triggers Based on Dual Attention[J].Computer Science, 2023, 50(1): 276-284.

References

[1]JI H,GRISHMAN R.Refining event extraction through cross-document inference[C]//Proceedings of ACL-08:Hlt.Columbus:ACL,2008:254-262.
[2]LI Q,JI H,HUANG L.Joint event extraction via structured prediction with global features[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers).Sofia:ACL,2013:73-82.
[3]CHEN Y,XU L,LIU K,et al.Event extraction via dynamic multi-pooling convolutional neural networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computatio-nal Linguistics and the 7th International Joint Conference on Natural Language Processing(Volume 1:Long Papers).Beijing:ACL,2015:167-176.
[4]LIU J,CHEN Y,LIU K,et al.Event detection via gated multilingual attention mechanism[C]//Proceedings of the AAAI Conference on Artificial Intelligence.New Orleans:AAAI press,2018:4865-4872.
[5]YAN H,JIN X,MENG X,et al.Event detection with multi-order graph convolution and aggregated attention[C]//Procee-dings 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:ACL,2019:5770-5774.
[6]CUI S,YU B,LIU T,et al.Edge-enhanced graph convolution networks for event detection with syntactic relation[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing:Findings.2020:2329-2339.
[7]NGUYEN T H,GRISHMAN R.Graph convolutional networks with argument-aware pooling for event detection[C]//Thirty-second AAAI Conference on Artificial Intelligence.New Orleans:AAAI Press,2018:5900-5907.
[8]CAO P,CHEN Y,ZHAO J,et al.Incremental Event Detection via Knowledge Consolidation Networks[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing(EMNLP).2020:707-717.
[9]LIN H,LU Y,HAN X,et al.Nugget Proposal Networks forChinese Event Detection[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics(Vo-lume 1:Long Papers).Melbourne:ACL,2018:1565-1574.
[10]DING N,LI Z,LIU Z,et al.Event detection with trigger-aware lattice neural network[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:ACL,2019:347-356.
[11]LIU S,LI Y,ZHANG F,et al.Event Detection without Triggers[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Volume 1(Long and Short Papers).Minneapolis:ACL,2019:735-744.
[12]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.
[13]NGUYEN T H,GRISHMAN R.Event detection and domain adaptation with convolutional neural networks[C]//Procee-dings 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:ACL,2015:365-371.
[14]NGUYEN T H,GRISHMAN R.Modeling skip-grams for event detection with convolutional neural networks[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.Austin:ACL,2016:886-891.
[15]FENG X,QIN B,LIU T.Alanguage-independent neural net-work for event detection[J].Science China Information Sciences.China:Science in China Press,2018,61(9):1-12.
[16]KODELJA D,BESANÇON R,FERRET O.Exploiting a more global context for event detection through bootstrapping[C]//European Conference on Information Retrieval.Cham:Springer,2019:763-770.
[17]JI Y,LIN Y,GAO J,et al.Exploiting the Entity Type Sequence to Benefit Event Detection[C]//Proceedings of the 23rd Confe-rence on Computational Natural Language Learning(CoNLL).Hong Kong:ACL,2019:613-623.
[18]LAI V D,NGUYEN T N,NGUYEN T H.Event detection:Gate diversity and syntactic importance scores for graph convolution neural networks[J].arXiv:2010.14123,2020.
[19]DUTTA S,MA L,SAHA T K,et al.GTN-ED:Event Detection Using Graph Transformer Networks[J].arXiv:2104.15104,2021.
[20]LIU J,CHEN Y,LIU K,et al.Neural cross-lingual event detection with minimal parallel resources[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:ACL,2019:738-748.
[21]DENG S,ZHANG N,KANG J,et al.Meta-learning with dynamic-memory-based prototypical network for few-shot event detection[C]//Proceedings of the 13th International Conference on Web Search and Data Mining.Houston:Association for Computing Machinery,2020:151-159.
[22]LAI V D,DERNONCOURT F,NGUYEN T H.Extensivelymatching for few-shot learning event detection[J].arXiv:2006.10093,2020.
[23]HUANG L,JI H.Semi-supervised new event type induction and event detection[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing(EMNLP).2020:718-724.
[24]TONG M,XU B,WANG S,et al.Improving event detection via open-domain trigger knowledge[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.ACL,2020:5887-5897.
[25]WANG X,HAN X,LIU Z,et al.Adversarial training for weakly supervised event detection[C]//Proceedings of the 2019 Confe-rence of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Volume 1(Long and Short Papers).Minneapolis:ACL,2019:998-1008.
[26]CHEN Y,YANG H,LIU K,et al.Collective event detection via a hierarchical and bias tagging networks with gated multi-level attention mechanisms[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.Brussels:ACL,2018:1267-1276.
[27]ZHAO Y,JIN X,WANG Y,et al.Document embedding en-hanced event detection with hierarchical and supervised attention[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics(Volume 2:Short Papers).Melbourne:ACL,2018:414-419.
[28]XIANG Y X,TONG Z,WEI Y,et al.A Hybrid Character Rep-resentation for Chinese Event Detection[C]//2019 International Joint Conference on Neural Networks(IJCNN).Budapest:IEEE,2019:1-8.
[29]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:ACL,2019:337-346.
[30]BENGIO Y,DUCHARME R,VINCENT P,et al.A neuralprobabilistic language model[J].The Journal of Machine Lear-ning Research,2003,3:1137-1155.
[31]ERHAN D,COURVILLE A,BENGIO Y,et al.Why does unsupervised pre-training help deep learning? [C]//Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics.JMLR Workshop and Conference Procee-dings.Sardinia:Microtome Publishing,2010:201-208.
[32]LAN Z,CHEN M,GOODMAN S,et al.Albert:A lite bert for self-supervised learning of language representations[J].arXiv:1909.11942,2019.
[33]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[J].arXiv:1301.3781,2013.
[34]DEVLIN J,CHANG M W,LEE K,et al.BERT:Pretraining of Deep Bidirectional Transformers for Language Understanding [C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Volume 1(Long and Short Papers),2019:4171-4186.
[35]LAMPLE G,BALLESTEROS M,SUBRAMANIAN S,et al.Neural Architectures for Named Entity Recognition[C]//Proceedings of NAACL-HLT.San Diego:ACL,2016:260-270.
[36]XIANG W,WANG B.A survey of event extraction from text[J].IEEE Access,2019,7:173111-173137.
[37]XU N,XIE H,ZHAO D.A Novel Joint Framework for Multiple Chinese Events Extraction[C]//China National Conference on Chinese Computational Linguistics.Cham:Springer,2020:174-183.

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