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intelligible words, the method can also be used to
help wikify possibly erroneous text from real-world
sources such as the Web, optical character recogni-
tion, or speech recognition. The numerical evalu-
ations demonstrated that the method works consis-
tently even in large-scale experiments, when disam-
biguating between up to 1000 Wikipedia articles.
A possible future application of the presented
method is the verification of links to Wikipedia. The
method can assign a single weight to each candidate
article: the sum of the weights in its group in the rep-
resentation vector α
α
α. If the weight corresponding to
the target of the link is small in contrast to weights of
other articles, the link is probably incorrect.
The presented method can be generalized, as it
can work with arbitrarily labeled text fragments as
well as contexts of Wikipedia links. This more gen-
eral framework may have further applications, as the
idea of distributional similarity offers solutions to
many natural language processing problems. For ex-
ample, topics might be assigned to documents as
in centroid-based document classification (Han and
Karypis, 2000).
ACKNOWLEDGEMENTS
The research has been supported by the ‘European
Robotic Surgery’ EC FP7 grant (no.: 288233). Any
opinions, findings and conclusions or recommenda-
tions expressed in this material are those of the au-
thors and do not necessarily reflect the views of other
members of the consortium or the European Commis-
sion.
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