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