Similarity loss (Wang et al., 2019), which is a metric-learning loss function that considers rela- tive similarities between positive and negative pairs. Let us denote the set of entities in the mini-batch by B and the set of positive and negative samples for the entity x0 i 2 B by Pi and Ni. We define the cosine similarity of two entities x0 i and x0 j as Si,j, resulting in a similarity matrix S 2 R|B| ⇥ |B|. Based on Pi, Ni, and S, the following training objectives are set: LMS = 1 |B| |B| X i=1 ⇢ 1 ↵ log ⇥ 1 + X k2Pi e ↵(Sik ) ⇤ + 1 log ⇥ 1 + X k2Ni e (Sik ) ⇤ , where ↵, are the temperature scales and is the offset applied on S. For pair mining, we follow the original paper (Wang et al., 2019). Test Datasets & Evaluation Metric We evalu- ated BIOCOM on three datasets for the biomedi- cal entity normalization task: NCBI disease cor- pus (NCBID) (Do˘ gan et al., 2014), BioCreative V Chemical Disease Relation (BC5CDR) (Li et al., 2016), and MedMentions (Mohan and Li, 2018). Following previous studies (D’Souza and Ng, 2015; Mondal et al., 2019), we used the accuracy as the evaluation metric. Given that BC5CDR and MedMentions contain mentions whose concepts are not in MEDIC, these were filtered out during the evaluation. We refer to these as “BC5CDR-d” and “MedMentions-d” respectively. Model Details The contextual representation for each entity x was obtained from PubMed- BERT (Gu et al., 2020), which was trained on a large number of PubMed abstracts using
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