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MC-TCMNER: A Multi-modal Fusion Model Combining Contrast Learning Method for Traditional Chinese Medicine NER

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MultiMedia Modeling (MMM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14556))

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Abstract

Traditional Chinese Medicine (TCM) texts contain a wealth of knowledge accumulated over thousands of years, making the extraction of knowledge from these texts a pivotal concern. Named Entity Recognition (NER) can serve as an effective tool for extracting knowledge information from TCM texts. However, TCM texts contain a large number of rare characters and homophones, and the attributions of entities are also more complex, making TCMNER more challenging. In order to address this issue, this paper introduces MC-TCMNER, a novel method that leverages the multi-modal features of Chinese characters and incorporates a training strategy based on contrastive learning. Experiments have shown that our proposed method achieves an F1 score of 94.05% on the TCMNER dataset and 52.84% on the C-CLUE benchmark, demonstrating the effectiveness of MC-TCMNER. Furthermore, owing to the limited availability of a comprehensive dataset for TCMNER, we have taken the initiative to publicly release a TCMNER dataset that we meticulously collected and annotated.

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Notes

  1. 1.

    https://github.com/cshan-github/TCM_NER_datasets.

  2. 2.

    https://github.com/ethan-yt/guwenbert.

  3. 3.

    https://pypi.python.org/pypi/pypinyin.

  4. 4.

    https://www.zhzyw.com/jbdq.html.

  5. 5.

    https://github.com/jiesutd/YEDDA.

  6. 6.

    https://github.com/Sporot/TCM_word2vec.

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Acknowledgment

This work was supported by Industry-University-Research Cooperation Project of Fujian Science and Technology Planning (No: 2022H6012), Science and Technology Research Project of Jiangxi Provincial Department of Education (No. GJJ 2206003), Natural Science Foundation of Fujian Province of China (No. 2021J011 169, No .2022J011224).

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Authors and Affiliations

  1. School of Informatics, Xiamen University, Xiamen, China

    Shan Cao

  2. Xiamen University, Xiamen, China

    Qingfeng Wu

Authors
  1. Shan Cao
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  2. Qingfeng Wu
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Correspondence to Qingfeng Wu .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  2. Delft University of Technology, Delft, The Netherlands

    Alan Hanjalic

  3. Delft University of Technology, Delft, The Netherlands

    Cynthia Liem

  4. University of Amsterdam, Amsterdam, The Netherlands

    Marcel Worring

  5. Reykjavik University, Reykjavik, Iceland

    Björn Þór Jónsson

  6. Microsoft Research Lab – Asia, Beijing, China

    Bei Liu

  7. The University of Tokyo, Tokyo, Japan

    Yoko Yamakata

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Cao, S., Wu, Q. (2024). MC-TCMNER: A Multi-modal Fusion Model Combining Contrast Learning Method for Traditional Chinese Medicine NER. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14556. Springer, Cham. https://doi.org/10.1007/978-3-031-53311-2_25

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  • DOI: https://doi.org/10.1007/978-3-031-53311-2_25

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  • Online ISBN: 978-3-031-53311-2

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