  Named Entity Disambiguation (NED) is the task of linking a named-entity
mention to an instance in a knowledge-base, typically Wikipedia. This task is
closely related to word-sense disambiguation (WSD), where the supervised
word-expert approach has prevailed. In this work we present the results of the
word-expert approach to NED, where one classifier is built for each target
entity mention string. The resources necessary to build the system, a
dictionary and a set of training instances, have been automatically derived
from Wikipedia. We provide empirical evidence of the value of this approach, as
well as a study of the differences between WSD and NED, including ambiguity and
synonymy statistics.
