Transfer Learning from BERT to Support Insertion of New Concepts into SNOMED CT.
Year: 2019
Type: article
Abstract: 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.
Source: PubMed Central
Authors: Hao Liu, Yehoshua Perl, James Geller
Institution: New Jersey Institute of Technology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funder: National Cancer Institute
OpenAlex ID: W3026153896