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Data Information in Online Environments. 4th EAI International Conference, DIONE 2023, Nanchang, China, November 25–27, 2023, Proceedings

Research Article

A Lithium-Ion Battery Cathode Material Literature Entity Recognition Method Based on Deep Learning

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-80713-8_8,
        author={Ziyu Yin and Gang Lei and Jianmao Xiao and Xinji Qiu and Qian Zhang and Lei Chang and Haohui Chen and Musheng Wu and Xushan Zhao},
        title={A Lithium-Ion Battery Cathode Material Literature Entity Recognition Method Based on Deep Learning},
        proceedings={Data Information in Online Environments. 4th EAI International Conference, DIONE 2023, Nanchang, China, November 25--27, 2023, Proceedings},
        proceedings_a={DIONE},
        year={2025},
        month={2},
        keywords={Lithium-ion Battery Materials Information Extraction Classification System Named Entity Identification},
        doi={10.1007/978-3-031-80713-8_8}
    }
    
  • Ziyu Yin
    Gang Lei
    Jianmao Xiao
    Xinji Qiu
    Qian Zhang
    Lei Chang
    Haohui Chen
    Musheng Wu
    Xushan Zhao
    Year: 2025
    A Lithium-Ion Battery Cathode Material Literature Entity Recognition Method Based on Deep Learning
    DIONE
    Springer
    DOI: 10.1007/978-3-031-80713-8_8
Ziyu Yin1, Gang Lei1, Jianmao Xiao1,*, Xinji Qiu2, Qian Zhang3, Lei Chang2, Haohui Chen1, Musheng Wu2, Xushan Zhao3
  • 1: School of Software, Jiangxi Normal University, Nanchang
  • 2: Department of Physics, Jiangxi Normal University, Nanchang
  • 3: Innovation Laboratory, Contemporary Amperex Technology Co., Limited, Ningde
*Contact email: jm_xiao@jxnu.edu.cn

Abstract

With the continuous deepening of research on lithium-ion battery materials, the number of literature in related fields has shown explosive growth, posing a challenge to researchers in terms of literature reading and information screening. Named entity recognition technology can automatically mark the pre-defined entity content in the literature, helping researchers to quickly locate the key information of the articles, and improve the reading rate of the literature. In this paper, the classification system of lithium-ion battery materials literature is constructed through the combination of automation and manual analysis, and the named entity recognition operation is conducted. We adopt the named entity recognition model based on BiLSTM-CRF, combined with Bert word embedding technology, to automatically identify and extract entities in the literature, and develope an automatic document annotation tool. Our work provides strong support for researchers to quickly understand the literature content and key information, and lays a foundation for subsequent relationship extraction and the construction of a Knowledge graph.

Keywords
Lithium-ion Battery Materials Information Extraction Classification System Named Entity Identification
Published
2025-02-09
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-80713-8_8
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