sis 18: e27

Research Article

Correlation temporal feature extraction network via residual network for English relation extraction

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  • @ARTICLE{10.4108/eai.19-11-2021.172213,
        author={Ping Li},
        title={Correlation temporal feature extraction network via  residual network for English relation extraction},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={11},
        keywords={English relation extraction, correlation temporal feature extraction network, residual network, Softmax},
        doi={10.4108/eai.19-11-2021.172213}
    }
    
  • Ping Li
    Year: 2021
    Correlation temporal feature extraction network via residual network for English relation extraction
    SIS
    EAI
    DOI: 10.4108/eai.19-11-2021.172213
Ping Li1,*
  • 1: Department of Public Instruction, Nanyang Medical College, Nanyang City 473000, China
*Contact email: snowycry@qq.com

Abstract

In relation extraction, a major challenge is the absence of annotated samples. Relation extraction aims to extract the relationships between entity pairs from a large amount of unstructured data. To solve the above problems, this paper presents a new method for English relation extraction based on correlation temporal feature extraction network via residual network. Firstly, the attention mechanism and recurrent neural network are used to obtain the temporal features of English word correlation. Secondly, a multi-branch feature sensing convolutional neural network is constructed to obtain global and local temporal correlation features respectively. Residual network can dynamically reduce the influence of noise data and better extract the deep information of English text. Finally, the relation extraction is realized with Softmax classifier. Experimental results show that the proposed method can extract English relation effectively than other methods.