You may have better luck checking out methods used in parsing natural
language. In order to use statistical parsing techniques such as
Probabilistic Context Free Grammars ([1],[2] ) the standard approach is to
extract rule probabilities from an annotated corpus, that is collection of
strings with associated parse trees. Maybe you could use your 2/3 of
addresses that you know are correctly parsed as your training material.
A PCFG parser can output all (or n-best) parses ordered according to
probabilities so that would seem to be fit your requirements.
[1] http://en.wikipedia.org/wiki/Stochastic_context-free_grammar
[2]
http://www.cs.colorado.edu/~martin/slp2.html#Chapter14
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Best,
Grzegorz
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Hi Grzegorz,
Wow, Natural Language Processing looks quite complex! But it also seems to be closely related to my problem. If someone finds a "NPL for dummies" article or book I'm interested. ;-)
Thanks for your help,
Olivier.