|Table of Contents|

Chinese Event Extraction Method Based on RBBLC Model(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2022年03期
Page:
38-44,82
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Chinese Event Extraction Method Based on RBBLC Model
Author(s):
Yang Denghui; Liu Jing
(College of Computer Science,Inner Mongolia University,Hohhot 010021,China)
Keywords:
event extraction; RoBERTa; bidirectional LSTM; sequence tagging; text big data analysis
PACS:
TP311.5
DOI:
10.3969/j.issn.1672-1292.2022.03.006
Abstract:
In big data analysis in the field of public security and law,discipline inspection and supervision,structured data and unstructured text data often become the main data source. When conducting business analysis based on this type of data,it is necessary to focus on extracting the implicit associations behind the data,and event extraction is the core basis for association analysis of such text data. The past event extraction task separates event trigger word recognition and event element recognition. The event trigger word and event type obtained from the event trigger recognition are used for subsequent event element recognition. There is a problem of error propagation,and the previous representation-based method is constructed Word vectors lack the ability to extract sentence-level features. This paper proposes a RBBLC joint extraction model,which completes event recognitionand event element recognition at the same time by means of sequence labeling. The RBBLC model builds word vectors containing richer context information based on RoBERTa,and then uses the network structure of BiLSTM-CNN to capture the relevant information within thesentence for event trigger word and argumentlabelprediction and event type prediction. The experiment is carried out on the CEC corpus. Compared with the baseline method,the F1 value,accuracy rate,and recall rate of our method are improved by 16%,28% and 24% respectively,which is effective improved the performance of event extraction tasks.

References:

[1]程思伟,葛唯益,王羽,等. BGCN:基于BERT和图卷积网络的触发词检测[J]. 计算机科学,2021,48(7):302-308.
[2]KIM J T,MOLDOVAN D I. Acquisition of linguistic patterns for knowledge-based information extraction[J]. IEEE Transactions on Knowledge and Data Engineering,1995,7(5):713-724.
[3]GUPTA S,PATEL D. NE2:named event extraction engine[J]. Knowledge and Information Systems,2019,59(2):311-335.
[4]YUBO C,L IHENG X,KANG L,et al. Event extractionvia dynamic multi-pooling convolutional neural networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th Inter-national Joint Conference on Natural Language Processing.Beijing,China:ACL,2015:167-176.
[5]ZENG Y,YANG H,FENG Y,et al. A convolution Bi-LSTM neural network model for chinese event extraction[C]//Natural Language Understanding and Intelligent Applications. Berlin,Germany:Springer,2016:275-287.
[6]潘璋,黄德根. 事件要素注意力与编码层融合的触发词抽取研究[J]. 小型微型计算机系统,2021,42(4):3-7.
[7]CHEN S,LIN H F,FAN X C,et al. Biomedical event trigger detection with convolutional highway neural network and extreme learning machine[J]. Applied Soft Computing Journal,2019,84:105661.
[8]ZHAN L Y,JIANG X P,LIU Q. Research on Chinese event extraction method based on HMM and multi-stage method[J]. Journal of Physics:Conference Series,2021,1732(1):012024.
[9]YU W,YI M,HUANG X,et al. Make it directly:event extraction based on tree-LSTM and Bi-GRU[J]. IEEE Access,2020(8):14344-14354.
[10]吴文涛,李培峰,朱巧明. 基于混合神经网络的实体和事件联合抽取方法[J]. 中文信息学报,2019,33(8):82-88.
[11]季忠祥,吴悦. 基于组合神经网络的中文事件抽取[J]. 上海大学学报(自然科学版),2021,27(3):129-137.
[12]王雷,李瑞轩,李玉华. 文档级无触发词事件抽取联合模型[J]. 计算机科学与探索,2021,15(12):1-9.
[13]DEVLIN J,CHANG M W,LEE K. BERT:pretraining of deep bidirectional transformers for language understanding[J]. arXiv Preprint arXiv:1810.04805,2019.
[14]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. California,USA,2016.
[15]张江英,郝矿荣,王直杰. 基于Lattice LSTM-CRF模型的中文紧急事件抽取[C]//2020中国自动化大会(CAC2020)论文集. 上海:中国自动化学会,2020.

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Last Update: 2022-09-15