  The detection and normalization of diseases in biomedical texts are key
biomedical natural language processing tasks. Disease names need not only be
identified, but also normalized or linked to clinical taxonomies describing
diseases such as MeSH. In this paper we describe deep learning methods that
tackle both tasks. We train and test our methods on the known NCBI disease
benchmark corpus. We propose to represent disease names by leveraging MeSH's
graphical structure together with the lexical information available in the
taxonomy using graph embeddings. We also show that combining neural named
entity recognition models with our graph-based entity linking methods via
multitask learning leads to improved disease recognition in the NCBI corpus.
