Transfer Learning from BERT to Support Insertion of New Concepts into SNOMED CT.
Hao Liu, Yehoshua Perl, James Geller · PubMed · 2019
model can address a challenging problem in automatic terminology enrichment - insertion of new concepts. Adding a pre-training strategy enhances the results. We apply our strategies to the two largest hierarchies of SNOMED CT, with one release as training data and the following release as test data. The performance of the combined two proposed TL models achieves an average F1 score of 0.85 and 0.86 for the two hierarchies, respectively.