  Biomedical named entities often play important roles in many biomedical text
mining tools. However, due to the incompleteness of provided synonyms and
numerous variations in their surface forms, normalization of biomedical
entities is very challenging. In this paper, we focus on learning
representations of biomedical entities solely based on the synonyms of
entities. To learn from the incomplete synonyms, we use a model-based candidate
selection and maximize the marginal likelihood of the synonyms present in top
candidates. Our model-based candidates are iteratively updated to contain more
difficult negative samples as our model evolves. In this way, we avoid the
explicit pre-selection of negative samples from more than 400K candidates. On
four biomedical entity normalization datasets having three different entity
types (disease, chemical, adverse reaction), our model BioSyn consistently
outperforms previous state-of-the-art models almost reaching the upper bound on
each dataset.
