  Automatic extraction of funding information from academic articles adds
significant value to industry and research communities, such as tracking
research outcomes by funding organizations, profiling researchers and
universities based on the received funding, and supporting open access
policies. Two major challenges of identifying and linking funding entities are:
(i) sparse graph structure of the Knowledge Base (KB), which makes the commonly
used graph-based entity linking approaches suboptimal for the funding domain,
(ii) missing entities in KB, which (unlike recent zero-shot approaches)
requires marking entity mentions without KB entries as NIL. We propose an
entity linking model that can perform NIL prediction and overcome data scarcity
issues in a time and data-efficient manner. Our model builds on a
transformer-based mention detection and bi-encoder model to perform entity
linking. We show that our model outperforms strong existing baselines.
