Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 52-56.doi: 10.11896/jsjkx.201200259

• Intelligent Computing • Previous Articles     Next Articles

Event Argument Extraction Using Gated Graph Convolution and Dynamic Dependency Pooling

WANG Shi-hao, WANG Zhong-qing, LI Shou-shan, ZHOU Guo-dong   

  1. School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:WANG Shi-hao,born in 1997,postgra-duate,is a member of China Computer Federation.His main research interests include natural language processing and so on.
    WANG Zhong-qing,born in 1987,Ph.D,associate professor.His main research interests include natural language processing and so on.
  • Supported by:
    Young Scientists Fund of the National Natural Science Foundation of China(61806137).

PDF (PC)

695

Abstract: Event argument extraction is a very challenging subtask of event extraction.This task aims to extract the arguments in the event and the role they played.It is found that the semantic features and dependency features of sentences play a very important role in event argument extraction,and the existing methods often don't consider how to integrate them effectively.Therefore,this paper proposes an event argument extraction model using gated graph convolution and dynamic dependency pooling.This method uses BERT to extract the semantic features of sentences,and then two same graph convolution networks are used to extract the dependency features of sentences based on the dependency tree.The output of one graph convolution is used as the gating unit through the activation function.Then semantic features and dependency features are added and fused through gate unit.In addition,a dynamic dependency pooling layer is designed to pool the fused features.The experiment results on ACE2005 dataset show that the proposed model can effectively improve the performance of event argument extraction.

Key words: Dependency features, Event argument extraction, Gate mechanism, Graph convolution, Semantic features

CLC Number: 

  • TP391
[1]DEVLIN J,CHANG M W,LEE K,et al.Bert:pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of NAACL-HLT.2019:4171-4186.
[2]KIPF T N,WELLING M.Semi-Supervised Classification withGraph Convolutional Networks[C]//Proceedings of ICLR.2017.
[3]SU J L.Reading comprehension question answering model based on CNN:DGCNN [EB/OL](2018-07-28).https://spaces.ac.cn/archives/-5409.
[4]DAVID A.The stages of event extraction[C]//Proceedings of the Workshop on Annotating and Reasoning about Time and Events.2006:1-8.
[5]LIAO S,GRISHMAN R.Using document level cross-event inference to improve event extraction[C]//Proceedings of ACL.2010:789-797.
[6]HONG Y,ZHANG J F,MA B,et al.Using cross-entity infer-ence to improve event extraction[C]//Proceedings of ACL-HLT.2011:1127-1136.
[7]MCCLOSKY D,SURDEANU M,CHRISTOPHER D M.Event extraction as dependency parsing[C]//Proceedings of ACL-HLT.2011:1626-1635.
[8]LI Q,JI H,HUANG L.Joint event extraction via structuredprediction with global features[C]//Proceedings of ACL.2013:73-82.
[9]LI P,ZHU Q,ZHOU G.Joint modeling of argument identification and role determination in Chinese event extraction with discourse-level information[C]//Proceedings of IJCAI.2013.
[10]CHEN Y B,XU L H,LIU K,et al.Event extraction via dynamic multi-pooling convolutional neural networks[C]//Proceedings of ACL.2015:409-419.
[11]NGUYEN T H,CHO K,GRISHMAN R.Joint event extraction via recurrent neural networks[C]//Proceedings of NAACL-HLT.2016:300-309.
[12]WANG X Z,WANG Z Q,HAN X,et al.HMEAE:Hierarchical Modular Event Argument Extraction[C]//Proceedings of EMNLP.2019:5781-5787.
[13]NGUYEN T,GRISHMAN R.Graph convolutional networkswith argument-aware pooling for event detection[C]//Proceedings of AAAI.2018:5900-5907.
[14]YAN H,JIN X L,MEMG X B,et al.Event Detection with Multi-Order Graph Convolution and Aggregated Attention[C]//Proceedings of EMNLP.2019:5766-5770.
[15]LIU X,ZHUNCHEN L,HEYAN H.Jointly multiple events extraction via attentionbased graph information aggregation[C]//Proceedings of EMNLP.2018:1247-1256.
[16]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 AAAI.2018:5916-5923.
[17]KINGM D,BA J.Adam:A Method for Stochastic Optimization[C]//Proceedings of ICLR.2015.
[18]SHA L,LIU J,LIN C Y,et al.RBPB:Regularization-based pattern balancing method for event extraction[C]//Proceedings of ACL.2016:1224-1234.
[1] TAN Ying-ying, WANG Jun-li, ZHANG Chao-bo. Review of Text Classification Methods Based on Graph Convolutional Network [J]. Computer Science, 2022, 49(8): 205-216.
[2] WANG Ming, PENG Jian, HUANG Fei-hu. Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction [J]. Computer Science, 2022, 49(8): 40-48.
[3] LI Jian-zhi, WANG Hong-ling, WANG Zhong-qing. Automatic Generation of Patent Summarization Based on Graph Convolution Network [J]. Computer Science, 2022, 49(6A): 172-177.
[4] SHAO Yan-hua, LI Wen-feng, ZHANG Xiao-qiang, CHU Hong-yu, RAO Yun-bo, CHEN Lu. Aerial Violence Recognition Based on Spatial-Temporal Graph Convolutional Networks and Attention Model [J]. Computer Science, 2022, 49(6): 254-261.
[5] ZHAO Xiao-hu, YE Sheng, LI Xiao. Multi-algorithm Fusion Behavior Classification Method for Body Bone Information Reconstruction [J]. Computer Science, 2022, 49(6): 269-275.
[6] LI Zi-yi, ZHOU Xia-bing, WANG Zhong-qing, ZHANG Min. Stance Detection Based on User Connection [J]. Computer Science, 2022, 49(5): 221-226.
[7] GAO Yue, FU Xiang-ling, OUYANG Tian-xiong, CHEN Song-ling, YAN Chen-wei. EEG Emotion Recognition Based on Spatiotemporal Self-Adaptive Graph ConvolutionalNeural Network [J]. Computer Science, 2022, 49(4): 30-36.
[8] ZHOU Hai-yu, ZHANG Dao-qiang. Multi-site Hyper-graph Convolutional Neural Networks and Application [J]. Computer Science, 2022, 49(3): 129-133.
[9] LI Hao, ZHANG Lan, YANG Bing, YANG Hai-xiao, KOU Yong-qi, WANG Fei, KANG Yan. Fine-grained Sentiment Classification of Chinese Microblogs Combining Dual Weight Mechanismand Graph Convolutional Neural Network [J]. Computer Science, 2022, 49(3): 246-254.
[10] DENG Wei-bin, ZHU Kun, LI Yun-bo, HU Feng. FMNN:Text Classification Model Fused with Multiple Neural Networks [J]. Computer Science, 2022, 49(3): 281-287.
[11] PAN Zhi-hao, ZENG Bi, LIAO Wen-xiong, WEI Peng-fei, WEN Song. Interactive Attention Graph Convolutional Networks for Aspect-based Sentiment Classification [J]. Computer Science, 2022, 49(3): 294-300.
[12] XIE Yu, YANG Rui-ling, LIU Gong-xu, LI De-yu, WANG Wen-jian. Human Skeleton Action Recognition Algorithm Based on Dynamic Topological Graph [J]. Computer Science, 2022, 49(2): 62-68.
[13] MIAO Qi-guang, XIN Wen-tian, LIU Ru-yi, XIE Kun, WANG Quan, YANG Zong-kai. Graph Convolutional Skeleton-based Action Recognition Method for Intelligent Behavior Analysis [J]. Computer Science, 2022, 49(2): 156-161.
[14] ZHANG Hu, BAI Ping. Graph Convolutional Networks with Long-distance Words Dependency in Sentences for Short Text Classification [J]. Computer Science, 2022, 49(2): 279-284.
[15] GAN Chuang, WU Gui-xing, ZHAN Qing-yuan, WANG Peng-kun, PENG Zhi-lei. Multi-scale Gated Graph Convolutional Network for Skeleton-based Action Recognition [J]. Computer Science, 2022, 49(1): 181-186.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.