Enriching Medcial Terminology Knowledge Bases via Pre-trained Language Model and Graph Convolutional Network
TABLE III: Comparative Results for Ablation Analysis of Our Proposed Model
['Components', 'Hits@1', 'Hits@5', 'Hits@10']
The pre-trained language model BERT show its ability to disambiguate, and can improve Hits@k score by more than 15%. GCN model can capture structural information contained in the graph, so that it can help to improve Hits@1 score to 59.58%. (2) Since the most nodes in terminology KB are synonyms and lack of distinctive relations, BERT node embedding without fine-tuning can improve Hits@1 score from random initialized node embedding from 49.04% to 56.41%, which is more than 7%, and specific fine-tuning can further improve Hits@1 score of our proposed model by more than 3%.