Adaptive Extraction of Entity Relationship with Few Samples Based on Relationship Redundancy
The rich medical knowledge in medical texts can provide data support for the construction of medical knowledge map,but there are a few knowledge texts in medical texts,which leads to practical problems such as unbalanced knowledge distribu-tion and few evidence-based knowledge samples,and the existing entity relationship extraction model has no good solution to the problems of relationship redundancy and entity overlap.Aiming at the above problems,this paper proposes an adaptive entity rela-tionship extraction model with few samples based on relationship redundancy,which makes up for the shortcomings of the existing extraction models,such as over reliance on a large number of labeled corpus and inability to solve entity overlap.The experimental results show that the performance of this model is improved by 4.9%compared with the existing extraction model F1.
medical fieldknowledge atlasfew samplesinformation extraction
万方数据
维普
