  In this work, we propose a method for the automated refinement of subject
annotations in biomedical literature at the level of concepts. Semantic
indexing and search of biomedical articles in MEDLINE/PubMed are based on
semantic subject annotations with MeSH descriptors that may correspond to
several related but distinct biomedical concepts. Such semantic annotations do
not adhere to the level of detail available in the domain knowledge and may not
be sufficient to fulfil the information needs of experts in the domain. To this
end, we propose a new method that uses weak supervision to train a concept
annotator on the literature available for a particular disease. We test this
method on the MeSH descriptors for two diseases: Alzheimer's Disease and
Duchenne Muscular Dystrophy. The results indicate that concept-occurrence is a
strong heuristic for automated subject annotation refinement and its use as
weak supervision can lead to improved concept-level annotations. The
fine-grained semantic annotations can enable more precise literature retrieval,
sustain the semantic integration of subject annotations with other domain
resources and ease the maintenance of consistent subject annotations, as new
more detailed entries are added in the MeSH thesaurus over time.
