  In this paper, we report a knowledge-based method for Word Sense
Disambiguation in the domains of biomedical and clinical text. We combine word
representations created on large corpora with a small number of definitions
from the UMLS to create concept representations, which we then compare to
representations of the context of ambiguous terms. Using no relational
information, we obtain comparable performance to previous approaches on the
MSH-WSD dataset, which is a well-known dataset in the biomedical domain.
Additionally, our method is fast and easy to set up and extend to other
domains. Supplementary materials, including source code, can be found at https:
//github.com/clips/yarn
