Automatic labeling and extraction of terms in natural language processing in acupuncture clinical literature / 中国针灸
Chinese Acupuncture & Moxibustion
;
(12): 327-331, 2022.
Article
in Chinese
| WPRIM
| ID: wpr-927383
ABSTRACT
The paper analyzes the specificity of term recognition in acupuncture clinical literature and compares the advantages and disadvantages of three named entity recognition (NER) methods adopted in the field of traditional Chinese medicine. It is believed that the bi-directional long short-term memory networks-conditional random fields (Bi LSTM-CRF) may communicate the context information and complete NER by using less feature rules. This model is suitable for term recognition in acupuncture clinical literature. Based on this model, it is proposed that the process of term recognition in acupuncture clinical literature should include 4 aspects, i.e. literature pretreatment, sequence labeling, model training and effect evaluation, which provides an approach to the terminological structurization in acupuncture clinical literature.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Natural Language Processing
/
Acupuncture Therapy
/
Electronic Health Records
Type of study:
Prognostic study
Language:
Chinese
Journal:
Chinese Acupuncture & Moxibustion
Year:
2022
Type:
Article
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