  Entity disambiguation (ED) is the task of mapping an ambiguous entity mention
to the corresponding entry in a structured knowledge base. Previous research
showed that entity overshadowing is a significant challenge for existing ED
models: when presented with an ambiguous entity mention, the models are much
more likely to rank a more frequent yet less contextually relevant entity at
the top. Here, we present NICE, an iterative approach that uses entity type
information to leverage context and avoid over-relying on the frequency-based
prior. Our experiments show that NICE achieves the best performance results on
the overshadowed entities while still performing competitively on the frequent
entities.
