+ Another techniques for normalizing spelling variants might use the
+ properties of \emph{NGrams}.
+
+ By using \emph{NGrams} the context of the word is also taken into
+ account. For example in the \emph{Muhammad} example, the Chinese
+ language has two forms for the name, one form is for the prophet and
+ one form is for the normal name. From the context it might be very
+ clear which one is meant and therefore \emph{NGrams} will probably have
+ a higher precision in this case.
+
+ Of course, when there is enough data at hand one could also use a
+ neural network to normalize spelling variants. The advantage of neural
+ networks would be the fact that it might even detect never seen
+ before spelling variant pretty well.
+
+ Features that are usable for the learners are of course the characters
+ itself. Contextual information is also very important in the feature
+ set. Besides those two, the cultural variables in which a language
+ resides can also be used. Some cultures use some names more than
+ others which can be very valuable information when normalizing proper
+ names.
+
+ Besides information about the word itself it might also be fruitful,
+ especially in the neural network case, to include the language
+ production rules in the feature set. These rules describe result in
+ distinguishing a typo from an intentional spelling variant.
+ This could even be extended by including the errors typists often make.
+ Some letters are closer on the keyboard than others and that could be a
+ feature that improves the performance of detecting typo's.