thanks.
it's much better than mine
4m30s vs. 14m(I suspect it's because all that hash look-ups I make use of)
but it's not putting the real words in the classes
it's putting the words modified by that regex wich
strips off unwanted characters.
altough there still may remain cases like:
when one writes this : アレの i るs オ i
wich at a closer look is "Ploiesti" wich is a city
in Romania but it is interpreted as Iasi(wich is another city in Romania) just because the edit-distance is close
but I agree that this is a very special case
also Caracas is a city in Venezuela and it has edit-distance
very close to Caracal wich is a small city in Romania
Should I check first against a list of cities ?
I also found this list of all cities,regions and countries in the world
and I'm wondering if it would be a good
Ideea to parse those(they're csv I guess),get some tables in the
database with them and check against them when a new
entry is beein added ... but that could take some time

In reply to Re^2: separating text words in similarity classes using levenshtein by spx2
in thread separating text words in similarity classes using levenshtein by spx2

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