Biomedical Entity Representations with Synonym Marginalization
Table 3: Comparison of two different training methods on the development sets of BC5CDR Disease, BC5CDR Chemical
['Methods', 'BC5CDR D. Acc@1', 'BC5CDR D. Acc@5', 'BC5CDR C. Acc@1', 'BC5CDR C. Acc@5']
The pair-wise training requires a binary classification model. For the pair-wise training, we minimize the binary cross-entropy loss using samples created by pairing each positive and negative candidate in the top candidates with the input mention. The results show that MML used in our framework learns better semantic representations than other methods.