Automatic labeling and extraction of terms in natural language processing in acupuncture clinical literature

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.