Entity Relation Extraction Based on Pre-trained Language Model for Tibetan Medicine
The texts in the field of Tibetan medicine are mainly stored in unstructured form.The information extrac-tion of Tibetan medicine texts plays an important role in excavating the knowledge of famous Tibetan medicine.In response to the problems of poor semantic expression ability and low accuracy of nested entity extraction in existing Tibetan entity relation extraction models,this paper introduces a pre-trained entity relation extraction method.The TibetanAI_ALBERT_v2.0 pre-trained language model is used to enable the model to better recognize entities,and the Span method is used to solve the problem of entity nesting.On the basis of Dropout,a KL divergence loss func-tion is added to enhance the model's generalization ability.Experiments on the TibetanAI_TMIE_v1.0 dataset of Ti-betan medicine show that the precision,recall,and F1 score have reached 84.5％,80.1％,and 82.2％,respectively.The F1 score has increased by 4.4 percentage points compared to the baseline.The results demonstrate the effective-ness of the proposed method.
Tibetan medicineentity relation extractionpre-trained language model
万方数据
维普
