Biomedical Entity Representations with Synonym Marginalization
Table 2: Experimental results on four biomedical entity normalization datasets
['Models', 'NCBI Disease Acc@1', 'NCBI Disease Acc@5', 'BC5CDR Disease Acc@1', 'BC5CDR Disease Acc@5', 'BC5CDR Chemical Acc@1', 'BC5CDR Chemical Acc@5', 'TAC2017ADR Acc@1', 'TAC2017ADR Acc@5']
Our model outperforms all previous models on the four datasets and achieves new state-of-the-art performance. The Acc@1 improvement on NCBI Disease, BC5CDR Disease, BC5CDR Chemical and TAC2017ADR are 1.1%, 2.6%, 0.8% and 2.4%, respectively. Training with only dense candidates (α=1.0) often achieves higher Acc@5 than BioSyn showing the effectiveness of dense candidates.