  Tools to explore scientific literature are essential for scientists,
especially in biomedicine, where about a million new papers are published every
year. Many such tools provide users the ability to search for specific entities
(e.g. proteins, diseases) by tracking their mentions in papers. PubMed, the
most well known database of biomedical papers, relies on human curators to add
these annotations. This can take several weeks for new papers, and not all
papers get tagged. Machine learning models have been developed to facilitate
the semantic indexing of scientific papers. However their performance on the
more comprehensive ontologies of biomedical concepts does not reach the levels
of typical entity recognition problems studied in NLP. In large part this is
due to their low resources, where the ontologies are large, there is a lack of
descriptive text defining most entities, and labeled data can only cover a
small portion of the ontology. In this paper, we develop a new model that
overcomes these challenges by (1) generalizing to entities unseen at training
time, and (2) incorporating linking predictions into the mention segmentation
decisions. Our approach achieves new state-of-the-art results for the UMLS
ontology in both traditional recognition/linking (+8 F1 pts) as well as
semantic indexing-based evaluation (+10 F1 pts).
