  Entity linking (or Normalization) is an essential task in text mining that
maps the entity mentions in the medical text to standard entities in a given
Knowledge Base (KB). This task is of great importance in the medical domain. It
can also be used for merging different medical and clinical ontologies. In this
paper, we center around the problem of disease linking or normalization. This
task is executed in two phases: candidate generation and candidate scoring. In
this paper, we present an approach to rank the candidate Knowledge Base entries
based on their similarity with disease mention. We make use of the Triplet
Network for candidate ranking. While the existing methods have used carefully
generated sieves and external resources for candidate generation, we introduce
a robust and portable candidate generation scheme that does not make use of the
hand-crafted rules. Experimental results on the standard benchmark NCBI disease
dataset demonstrate that our system outperforms the prior methods by a
significant margin.
