  Biomedical word sense disambiguation (WSD) is an important intermediate task
in many natural language processing applications such as named entity
recognition, syntactic parsing, and relation extraction. In this paper, we
employ knowledge-based approaches that also exploit recent advances in neural
word/concept embeddings to improve over the state-of-the-art in biomedical WSD
using the MSH WSD dataset as the test set. Our methods involve weak supervision
- we do not use any hand-labeled examples for WSD to build our prediction
models; however, we employ an existing well known named entity recognition and
concept mapping program, MetaMap, to obtain our concept vectors. Over the MSH
WSD dataset, our linear time (in terms of numbers of senses and words in the
test instance) method achieves an accuracy of 92.24% which is an absolute 3%
improvement over the best known results obtained via unsupervised or
knowledge-based means. A more expensive approach that we developed relies on a
nearest neighbor framework and achieves an accuracy of 94.34%. Employing dense
vector representations learned from unlabeled free text has been shown to
benefit many language processing tasks recently and our efforts show that
biomedical WSD is no exception to this trend. For a complex and rapidly
evolving domain such as biomedicine, building labeled datasets for larger sets
of ambiguous terms may be impractical. Here, we show that weak supervision that
leverages recent advances in representation learning can rival supervised
approaches in biomedical WSD. However, external knowledge bases (here sense
inventories) play a key role in the improvements achieved.
