  The problem of collecting reliable estimates of occurrence of entities on the
open web forms the premise for this report. The models learned for tagging
entities cannot be expected to perform well when deployed on the web. This is
owing to the severe mismatch in the distributions of such entities on the web
and in the relatively diminutive training data. In this report, we build up the
case for maximum mean discrepancy for estimation of occurrence statistics of
entities on the web, taking a review of named entity disambiguation techniques
and related concepts along the way.
