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Re: st: Re: Finding "near"-matches


From   Seb Buechte <sfbuechte@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Re: Finding "near"-matches
Date   Fri, 28 Oct 2005 15:13:53 +0200

Clyde and Michael,

I also programmed something to find out how similar two strings are
using the edit-distance-method. The edit-distance between two strings
is the number of changes required to change one string in such way
that it equals the other. I admit that what I programmed is somehow
"quick&dirty" code. If you would like, I can email it to you, but if
you would like to know how it works you could check out this website:

http://www.csse.monash.edu.au/~lloyd/tildeAlgDS/Dynamic/Edit/

There you find a description of the underlying algorithm.

Kind regards,
sebastian

On 10/27/05, Michael Blasnik <michael.blasnik@verizon.net> wrote:
> "Clyde Schechter" <cschecht@aecom.yu.edu> wrote about trying to match not
> quite identical text strings between datasets.  I also spend a great deal of
> time trying to match across administrative databases and have developed a
> few tools to help.  There is a fair amount of literature on string
> comparators (e.g., US Census web site) that produce some rating of the
> similarity of two text strings.  I have coded up a couple of them and tend
> to use the bigram (which counts the proportion of 2 character substrings
> that exist in both strings).  I have also automated some of the common-typo
> problems (e.g., l vs. 1, 0 vs O) for specific projects where I simply create
> a new version of each of the strings that replaces all occurences of l and
> O, with 1 and 0 (and other common errors) before running the string
> comparison.
>
> If there is interest, I can email the bigram ado file or potentially post it
> on SSC when I get around to writing up the help.
>
> Michael Blasnik
> michael.blasnik@verizon.net .
>
>
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