Research on Named Entity Recognition in Traditional Chinese Medicine Herbal Texts
lin Xiao Tong, Sihong Liu, Ziling Zeng, Guangkun Chen, Yu Zhang, Qikai Niu, Danping Zheng, Hongtao Li, Huamin Zhang, Lei Zhang · 2023
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.