Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China

Research Article

Automatic Text Classification Method of Personnel Electronic Archives Based on Word Segmentation Algorithm

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  • @INPROCEEDINGS{10.4108/eai.17-6-2022.2322692,
        author={Jiangjing  Lin and Ming  Guo and Linhua  Gong and Jiafa  Hu},
        title={Automatic Text Classification Method of Personnel Electronic Archives Based on Word Segmentation Algorithm},
        proceedings={Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China},
        publisher={EAI},
        proceedings_a={ICIDC},
        year={2022},
        month={10},
        keywords={personnel files; electronic information; intelligent classification; word segmentation algorithm},
        doi={10.4108/eai.17-6-2022.2322692}
    }
    
  • Jiangjing Lin
    Ming Guo
    Linhua Gong
    Jiafa Hu
    Year: 2022
    Automatic Text Classification Method of Personnel Electronic Archives Based on Word Segmentation Algorithm
    ICIDC
    EAI
    DOI: 10.4108/eai.17-6-2022.2322692
Jiangjing Lin1,*, Ming Guo1, Linhua Gong1, Jiafa Hu1
  • 1: Wuhan Second Ship Design Institute
*Contact email: linjiangjing@whhwtech.com

Abstract

To improve the level of personnel management, we need to learn from advanced management theory and advanced science and technology. Thus, establishing an intelligent personnel information management system to assist daily management is helpful to improve the efficiency of personnel management and the quality of service. Aiming at the lack of intelligent analysis and decision-making function in personnel management informatization, this paper proposes a method based on machine learning and deep learning to transform relationship extraction into classification task, and realizes the automatic classification method of personnel electronic archives text by combining entity context information. It can also integrate dependency, part of speech and other multiple features. Corpus data sets are selected in the experiment, and the experimental results show that the proposed method has better performance in convergence speed and model accuracy. It realizes the purpose of intelligent classification of personnel file information. The proposed method can provide promotion and reference for related work of other industries and departments.