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EIBC: a deep learning framework for Chinese toponym recognition with multiple layers

Author

Listed:
  • Yijiang Zhao

    (Hunan University of Science and Technology)

  • Daoan Zhang

    (Hunan University of Science and Technology)

  • Lei Jiang

    (Hunan University of Science and Technology)

  • Qi Liu

    (Hunan University of Science and Technology)

  • Yizhi Liu

    (Hunan University of Science and Technology)

  • Zhuhua Liao

    (Hunan University of Science and Technology)

Abstract

Existing methods based on BERT are difficult to automatically identify and efficiently detect Chinese toponyms due to its irregularity and the intricate structure. To address this issue, this article introduces a novel toponym recognition model named EIBC, which is the abbreviation of ERNIE-Gram-IDCNN-BiLSTM-CRF. It consists of four parts: (1) ERNIE-Gram is selected for dynamic vector representations of toponyms and extracts toponym features; (2) the context features are dilated by IDCNN with different dilation scales; (3) BiLSTM is employed to capture bidirectional context information and to grasp a broader range of global context features, while removing the noise information through its gating mechanisms; and (4) it incorporates CRF for global optimization of toponym sequence labels, enhancing toponym recognition effectiveness. The proposed model is constructed based on a multi-layer deep learning framework by utilizing various advanced techniques to enhance the model's performance. Experimental results show that the EIBC model outperforms existing some state-of-the-art Chinese toponym recognition models.

Suggested Citation

  • Yijiang Zhao & Daoan Zhang & Lei Jiang & Qi Liu & Yizhi Liu & Zhuhua Liao, 2024. "EIBC: a deep learning framework for Chinese toponym recognition with multiple layers," Journal of Geographical Systems, Springer, vol. 26(3), pages 407-425, July.
    • Handle: RePEc:kap:jgeosy:v:26:y:2024:i:3:d:10.1007_s10109-024-00441-4
    DOI: 10.1007/s10109-024-00441-4
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    References listed on IDEAS

    as
    1. Kai Ma & YongJian Tan & Zhong Xie & Qinjun Qiu & Siqiong Chen, 2022. "Chinese toponym recognition with variant neural structures from social media messages based on BERT methods," Journal of Geographical Systems, Springer, vol. 24(2), pages 143-169, April.
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    More about this item

    Keywords

    Toponym recognition; ERNIE-Gram; IDCNN; BiLSTM; Conditional random field; Dynamic word vector;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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