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Medical entity span extraction and linking are critical steps for manyhealthcare NLP tasks. Most existing entity extraction methods either have afixed vocabulary of medical entities or require span annotations. In thispaper, we propose a method for linking an open set of entities that does notrequire any span annotations. Our method, Open Set Label Attention Transformer(OSLAT), uses the label-attention mechanism to learn candidate-entitycontextualized text representations. We find that OSLAT can not only linkentities but is also able to implicitly learn spans associated with entities.We evaluate OSLAT on two tasks: (1) span extraction trained without explicitspan annotations, and (2) entity linking trained without span-level annotation.We test the generalizability of our method by training two separate models ontwo datasets with low entity overlap and comparing cross-dataset performance.

OSLAT: Open Set Label Attention Transformer for Medical Entity Retrieval and Span Extraction