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
2.5 Experimental Environment
The research was implemented on a small artificial intelligence platform equipped with Intel Xeon Gold 6248R [email protected]*96, 256GB memory and NVIDIA A100 80G*2 GPU computing card in the Laboratory of Traditional Chinese Medicine Informatics of Hebei University. Ubuntu 18.04.6LTS, Python 3.9 environment.
This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
