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Establishment of herbal prescription vector space model based on word co-occurrence

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Abstract

This paper analyzes the establishment of vector space model (VSM) of herbal prescription. Currently, VSM has been frequently used in knowledge acquisition and information retrieval. However, as VSM ignores the association between words, the herbal prescription can only be expressed with single Chinese herb as unit and the potential semantic information in herbal prescription cannot be fully reflected, which limits the clustering result of herbal prescription. This study investigates the significance of word co-occurrence for the research on the formulation theory of herbal prescription, verifies the association between the major function of herbal prescription between word co-occurrence and proposes the word co-occurrence-based VSM and expression method of second-order weighted eigenvalues. It can be concluded that the proposed method can achieve better effect in clustering analysis of herbal prescription comparing traditional VSM-based expression method.

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Acknowledgements

This paper is financed by the National Social Science Foundation of China (16BGL181) and the Natural Science Foundation of Shandong Province (ZR201702130105).

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Authors and Affiliations

  1. Center of Hear of the Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China

    Shouqiang Chen

  2. School of Information Science and Engineering, Shandong Normal University, Jinan, China

    Yang Chen

  3. University of Waterloo, Waterloo, ON, Canada

    Yang Chen

  4. Key Laboratory of TCM Data Cloud Service in Universities of Shandong, Shandong Management University, Jinan, China

    Feng Yuan & Xiaowei Chang

Authors
  1. Shouqiang Chen
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  2. Yang Chen
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  3. Feng Yuan
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  4. Xiaowei Chang
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Correspondence to Feng Yuan.

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Chen, S., Chen, Y., Yuan, F. et al. Establishment of herbal prescription vector space model based on word co-occurrence. J Supercomput 76, 3590–3601 (2020). https://doi.org/10.1007/s11227-018-2559-3

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  • DOI: https://doi.org/10.1007/s11227-018-2559-3

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