  Entity disambiguation, or mapping a phrase to its canonical representation in
a knowledge base, is a fundamental step in many natural language processing
applications. Existing techniques based on global ranking models fail to
capture the individual peculiarities of the words and hence, either struggle to
meet the accuracy requirements of many real-world applications or they are too
complex to satisfy real-time constraints of applications.
  In this paper, we propose a new disambiguation system that learns specialized
features and models for disambiguating each ambiguous phrase in the English
language. To train and validate the hundreds of thousands of learning models
for this purpose, we use a Wikipedia hyperlink dataset with more than 170
million labelled annotations. We provide an extensive experimental evaluation
to show that the accuracy of our approach compares favourably with respect to
many state-of-the-art disambiguation systems. The training required for our
approach can be easily distributed over a cluster. Furthermore, updating our
system for new entities or calibrating it for special ones is a computationally
fast process, that does not affect the disambiguation of the other entities.
