Published on in Vol 9, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28219, first published .
Extraction of Traditional Chinese Medicine Entity: Design of a Novel Span-Level Named Entity Recognition Method With Distant Supervision

Extraction of Traditional Chinese Medicine Entity: Design of a Novel Span-Level Named Entity Recognition Method With Distant Supervision

Extraction of Traditional Chinese Medicine Entity: Design of a Novel Span-Level Named Entity Recognition Method With Distant Supervision

Journals

  1. Ma Y, Liu Y, Zhang D, Zhang J, Liu H, Xie Y, Zhang D. A Multigranularity Text Driven Named Entity Recognition CGAN Model for Traditional Chinese Medicine Literatures. Computational Intelligence and Neuroscience 2022;2022:1 View
  2. Zhang T, Huang Z, Wang Y, Wen C, Peng Y, Ye Y, Zhou X. Information Extraction from the Text Data on Traditional Chinese Medicine: A Review on Tasks, Challenges, and Methods from 2010 to 2021. Evidence-Based Complementary and Alternative Medicine 2022;2022:1 View
  3. Pan D, Guo Y, Fan Y, Wan H. Development and Application of Traditional Chinese Medicine Using AI Machine Learning and Deep Learning Strategies. The American Journal of Chinese Medicine 2024;52(03):605 View
  4. Mai C, Chen Y, Gong Z, Wang H, Qiu M, Yuan C, Huang Y. PromptCNER: A Segmentation-based Method for Few-shot Chinese NER with Prompt-tuning. ACM Transactions on Asian and Low-Resource Language Information Processing 2024 View
  5. Qu X, Tian Z, Cui J, Li R, Li D, Zhang X. A Review of Knowledge Graph in Traditional Chinese Medicine: Analysis, Construction, Application and Prospects. Computers, Materials & Continua 2024;81(3):3583 View
  6. Pechsiri C, Piriyakul I, Pechsiri J. Grouped semantic-feature relation extraction from texts to represent medicinal-plant property knowledge on social media. Frontiers in Artificial Intelligence 2025;8 View

Conference Proceedings

  1. Zhang T, Liu G. 2023 IEEE International Conference on Control, Electronics and Computer Technology (ICCECT). RDRS: Represent Document-level Relation with Sentence-level Relation by Distant Supervision View