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

Distributed Entity Disambiguation with Per-Mention Learning