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Chinese Event Extraction Algorithm of Multi-Information Semantic Enhancements

Published: 28 February 2024 Publication History

Abstract

To address the problems of inaccurate event element extraction, inability to directly handle long texts and errors in the fine-tuning stage of the task due to insufficient semantic information and incomplete semantic feature samples in the current Chinese domain event extraction model, this paper proposes the Chinese Event Extraction Algorithm of Multi-Information Semantic Enhancements (MiSE). In the embedding stage, the algorithm is designed a coding structure that fusing multiple information: Using the Roformer model based on rotational encoding for word embedding and text embedding, and augment the Roformer word embedding matrix by mapping the word embedding matrixs of event trigger, argument information and their location information to it, forming an vector matrix that incorporates multiple information; the matrix is then placed into the BiLSTM for forward and backward chain calculation to enhance the model's ability to extract semantic information from Chinese text. In the decoding stage, using the decoding structure to obtain the set of tags with the highest probability. Comparing this algorithm with several Chinese event extraction algorithms on the Baidu DuEE dataset and its extraction results outperformed other event extraction algorithms in all evaluation metrics.

References

[1]
Ma, Chunming, Li, Xihong, Li, Zhe, A review of event extraction [J]. Computer Applications, 2022, 42(10): 2975-2989.
[2]
Qiu X, Sun T, Xu Y, Pre-trained Models for Natural Language Processing: A Survey[J]. Science China Technological Sciences, 2020, 63(10): 1872-1897.
[3]
Wollmer M, Weninger F, Knaup T,et al. YouTube Movie Reviews: Sentiment Analysis in an Audio-Visual Context[J].IEEE Intelligent Systems, 2013, 28(3): 46-53.
[4]
Liu Huiling,Tao Jie,Qiu Lei. Implementation of Python-based One-hot coding [J]. Journal of Wuhan Shipbuilding Vocational Technology College,2021,20(03):136-139.
[5]
Xi Ningli, Zhu Lijia, Wang Lutong, Chen Jun, Wan Xiaorong. A Word2vec implementation method for building word vector models[J]. Computer and Information Technology,2023,31(01):43-46.
[6]
Devlin J, Chang Mingwei, Lee K, Bert: pre-training of deep bi-directional transformers for language understanding [C] //Proc of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA:ACL, 2019: 4171-4186.
[7]
Ji Zongxiang,Wu Yue. Chinese event extraction based on combinatorial neural network[J]. Journal of Shanghai University (Natural Science Edition),2021,27(03):535-543.
[8]
Peng Zhengyang. Research and application of Chinese event extraction technology based on GCNN [D]. University of Chinese Academy of Sciences (Shenyang Institute of Computing Technology, Chinese Academy of Sciences), 2020.
[9]
Zou Xinchun. Research and implementation of element extraction technology for Chinese news [D]. University of Electronic Science and Technology, 2022.
[10]
Yang, D.-H., Liu, J. A Chinese event extraction method based on RBBLC model [J]. Journal of Nanjing Normal University (Engineering Technology Edition),2022,22(03):38-44+82.
[11]
Ma X, Liu Y, Ouyang C . Hybrid Syntactic Graph Convolutional Networks for Chinese Event Detection[M]. 2021.
[12]
Annan Chen. Research on Chinese Event Extraction Method Based on BERT-DGCNN[J]. Computer Science and Application, 2021, 11(5):1572-1578.
[13]
Zhai P, Wang C, Fang Y . LSLSD: Fusion Long Short-Level Semantic Dependency of Chinese EMRs for Event Extraction[J]. Applied Sciences, 2021.
[14]
Zhang Hongkuan,Song Hui,Xu Bo,Wang Shuyi. BERT-based end-to-end event extraction for Chinese chapters[J]. Chinese Journal of Information,2022,36(10):97-106.
[15]
Lu Y, Yang R, Jiang X, Research on Military Event Detection Method Based on BERT-BiGRU-Attention[C]// 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE, 2021.
[16]
Ke Xinfei. Research on BERT-based end-to-end Chinese event extraction method [D]. Xi'an University of Electronic Science and Technology, 2022.
[17]
Liu J, Zhang J, Huang X, Syntactic-GCN Bert based Chinese Event Extraction[J]. 2021.
[18]
Cui Y, Che W, Liu T, Revisiting Pre-Trained Models for Chinese Natural Language Processing[C]. Proceedings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020, Nov 16-20, 2020, ACL, 2020: 657-668.
[19]
Li X, Li F, Pan L, DuEE: A Large-Scale Dataset for Chinese Event Extraction in Real-World Scenarios[C]. CCF International Conference on Natural Language Processing and Chinese Computing, Oct 14-18, 2020, Springer Science and Business Media Deutschland GmbH, 2020: 534-545.
[20]
Li PY. Prosodic Unit Boundary Prediction of My-anmar Based on BERT-CRF Model[J]. Computer Science and Application, 2021, 11(03): 505-514.
[21]
Wei JQ, Ren XZ, Li XG, NEZHA: Neural Contextualized Representation for Chinese Language Understanding[J]. arXiv preprint arXiv: 1909.00204, 2019.

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  1. Chinese Event Extraction Algorithm of Multi-Information Semantic Enhancements

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    ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
    October 2023
    589 pages
    ISBN:9798400707988
    DOI:10.1145/3633637
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 28 February 2024

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    Author Tags

    1. BiLSTM
    2. Chinese Event Extraction
    3. Multi-Information Semantic Enhancements
    4. Roformer

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    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    • the Development Fund Project of Hebei Key Laboratory of Intelligent Information Perception and Processing
    • the Innovation Project of GUET Graduate Education
    • the Development Foundation of the 54th Research Institute of China Electronics Technology Group Corporation
    • the National Natural Science Foundation of China
    • the Guilin Science and Technology Development Program
    • the Guangxi Key Research and Development Program
    • the Natural Science Foundation of Guangxi

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