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
3.3 Results of dependency parsing
This study completed the partial syntactic analysis of all the clauses of the three works. Taking the text description of the theory of quoting classics in Medical Qi Yuan as an example, the sample data were extracted for relation extraction and image rendering.
The sample texts are as follows:
Each sutra quotes the Sun Sutra, Qiang Huo; In the lower yellow cypress, small intestine, bladder also. Shaoyang meridian, Bupleurum; In the lower Qingpi, bile, sanjiao also. Yangming meridian, cohosh, angelica dahurica; In the lower, gypsum, stomach, large intestine also. Taiyin meridian, Baishao medicine, spleen, lung also. Shaoyin meridian, anemarrhena, heart and kidney. Jieyin meridian, Qingpi; In the lower, bupleurum, liver, envelop also. The medicine of the above 12 classics is also.
The dependency grammar tree is constructed as shown in Figure 3. The model can recognize this text in classical Chinese, analyze its grammatical structure according to the entity recognition results, and extract the relationship between entities. Taking the Sun Meridian as an example, it can clearly distinguish the relationship between the Sun Meridian and Qiang Huo, and between the yellow cypress and small intestine, bladder.
This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
