Authors:
(1) Hanqing ZHAO, College of Traditional Chinese Medicine, Hebei University, Funded by National Natural Science Foundation of China (No.82004503) and Science and Technology Project of Hebei Education Department(BJK2024108) and a Corresponding Author ([email protected]);
(2) Yuehan LI, College of Traditional Chinese Medicine, Hebei University.
Table of Links
2. Materials and Methods
2.1 Experimental Data and 2.2 Conditional random fields mode
2.3 TF-IDF algorithm and 2.4 Dependency Parser Based on Neural Network
3 Experimental results
3.1 Results of word segmentation and entity recognition
3.2 Visualization results of related entity vocabulary map
3.3 Results of dependency parsing
4 Final Remarks
In this study, the named entity recognition method based on conditional random fields is used to analyze the entity vocabulary, semantic features and syntactic structure of the text data of the Yishui School of traditional Chinese Medicine. The extraction of key named entities from unstructured text data has achieved good results. It has important theoretical and practical guiding value for the summary of academic views of different doctors of the Yishui School, the discovery of differences in academic ideas, and the study of the inheritance of the Yishui School. In the next step, on the basis of named entity recognition, we will continue to study TCM entity relation extraction from classical Chinese data, and then construct the knowledge graph of Yishui School, which provides reference for the application of artificial intelligence methods in the research of TCM school.
This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
