hi, I'm parsing documents,each of them has a location
written on them.
There is a slight problem as people write differently
their location.
A friend suggested some time ago using Levenshtein's
algorithm for getting the edit distance between words
if needed.
I used that to establish similarity between words
calculating to know if they belong to a certain class.
So,what I'm trying to do:
Having a list of inconsistent locations,divide them into
equivalence classes choosing an appropriate representant
for each of the classes.
In doing so ,I have used a hash and a similarity function
wich establishes if two words are similar or not like this:

f(w1,w2)
  return 1 if w1 is contained in w2
  return 1 if w2 is contained in w1
  return
     edit_distance(w1,w2)/length(w2) < 0.34
     ? 1
     : 0

[download]
I have a hash whose keys are representants of an equivalence
class and whose values are the classes themselfes.
And after words are classified for a first time. The classes suffer a further re-merging.

Here is the script wich does this.
Here is a information sample on wich it runs.
These are the results I got.
The results are pretty much ok,but they need to be improved.
Questions : are there articles regarding this problem, wich present better solutions? How can I optimise this one ?
Any suggestions are welcome
PS:you can decomment line 92 to make it not process all the file and run faster.
EDIT:
corrected problem in the f function above instead
of > it's <

In reply to separating text words in similarity classes using levenshtein by spx2

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