Biomedical Entity Representations with Synonym Marginalization
This work addresses the problem of biomedical entity normalization for text mining tools, offering a novel method to handle incomplete synonyms and surface form variations, with incremental improvements in performance.
The paper tackled the challenge of biomedical entity normalization by learning entity representations from incomplete synonyms, using a model-based candidate selection and marginal likelihood maximization. The model, BioSyn, consistently outperformed previous state-of-the-art models on four datasets, nearly reaching the upper bound for each.
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.