Deep Learning-Based Entity Relationship Extraction of Ancient Chinese Medical Texts
SHI Yujing
LIU Wei
HU Wei
Abstract：Relationship extraction is a key part of knowledge graph construction,meanwhile,TCM information extraction has been a research focus in the field of natural language processing in TCM.In this paper,entity relationship extraction of Huangdi Nei-jing is studied by the Pipeline method based on RoBERTa-wwm-BiLSTM-CRF and the Joint algorithm based on TPLinker.After the comparison study,the TPLinker model is proved to have better performance in the TCM relationship extraction task with accura-cy,recall and F1 values of 94.54%,95.59%and 95.04%.This shows the superiority of TPLinker in the relationship extraction task,and it can also provide a reference for other TCM information extraction tasks and lay the foundation for the construction of TCM knowledge graphs.
Keywords：deep learningrelationship extractionHuangdi Neijing
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：6( 3066-3071 )