Research on Named Entity Recognition in Traditional Chinese Medicine Herbal Texts
- Resource Type
- Conference
- Authors
- Tong, Lin; Liu, Sihong; Zeng, Ziling; Chen, Guangkun; Zhang, Yu; Niu, Qikai; Zheng, Danping; Li, Hongtao; Zhang, Huamin; Zhang, Lei
- Source
- 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2023 IEEE International Conference on. :4636-4639 Dec, 2023
- Subject
- Bioengineering
Computing and Processing
Engineering Profession
Robotics and Control Systems
Signal Processing and Analysis
Training
Solid modeling
Text recognition
Annotations
Biological system modeling
Data models
Object recognition
named entity recognition
Traditional Chinese Medicine
herbal texts
- Language
- ISSN
- 2156-1133
Objective To address the issues in named entity recognition (NER) in the field of traditional Chinese medicine (TCM), this study proposes a method for identifying entities in TCM herbal literature; Methods We identify and describe the types of knowledge entities and entity relationships involved in herbal literature. We apply the BIO sequence labeling method to generate a training corpus dataset and use our self-developed CNLP text annotation system for text annotation. The Bert model is employed for recognizing named entities; Results The Bert model achieved entity recognition results for various entities in TCM herbal literature with precision (P) of 71.49%, recall (R) of 72.33%, and F1 score of 71.91%; Conclusion The Bert model demonstrates a certain level of applicability in recognizing various entities in TCM herbal literature. This model is helpful in extracting valuable structured information from a large volume of text data.