The research of TCM informatization is usually based on the text of the patient's symptoms. TCM physicians use synonyms to describe a patient's symptoms, which can cause data redundancy in the patient's symptom text. Therefore, in the process of TCM informatization, it is first necessary to process the TCM text and standardize the synonyms in the symptom text. In this paper, a synonym set is constructed based on the symptom text of patients, and the ED-BERT model is proposed for the normalization of symptom texts in traditional Chinese medicine, which lays a foundation for the follow-up research of traditional Chinese medicine informatization. The accuracy of the proposed model for TCM synonym recognition reaches 0.8613, which has advanced advantages over traditional methods.
This article proposes a sequence-to-sequence based multi-label text classification model. The model combines neural convolutional networks and self-attention mechanisms as encoders, and designs a novel decoder to decode and generate label sequences. The proposed method not only fully considers the interpretable fine-grained information in the source text, but also effectively utilizes this information to generate label sequences. When predicting labels, the global information and local information can be effectively combined to improve the accuracy of label prediction. To verify the effectiveness of the proposed model, this article conducted a large number of comparative experiments. The results show that compared with other models, the model proposed in this article has better performance in different indicators.
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