That should work, you might want to include street and square if its
part of the name.
The tags need to be separated by white spaces, otherwise the parser
fails to recognize them.
You will need a quite a few samples to train it, 10 or 20 will not be
enough.
Jörn
On 04/20/2012 03:45 PM, mauro fraboni wrote:
I was thinking to train with a file made in this way:
via<START:street>massarenti<END
<START:number>300<END>,<START:town>Bologna<END>,<START:province>BO<END>,<START:Country>IT>END>.
piazza<START:street>maggiore<END
<START:number>3<END>,<START:town>Trento<END>,<START:province>TN<END>,<START:Country>IT>END>
............
via (meaning is street) and piazza (meaning is square) are two descriptors
that could not be classified according to my opinion.
ciao
On Fri, Apr 20, 2012 at 3:29 PM, Jim - FooBar();<[email protected]>wrote:
On 20/04/12 14:16, mauro fraboni wrote:
I am investigating if it is possible to use OpenNLP to parse italian post
addresses.
I do not want to validate the input address using an official address
database; I just need to divide a single address string into its
individual
component parts and I thought to use NameFinder.
My idea was to train Name Finder using some italian addresses indicating
in
training data the parts like Street, Town, Province, Post Code, Country
Do you think that it can work? Someone has experience about it?
Thanks and ciao.
Hmmm, that sounds like it should work....however you don't want to
separate your entities to Street, Town, Province, Post Code, Country etc
cos then how are you going to join them to get your 'real' entity
(address)? I would say keep the whole address as 1 entity and produce some
training data that mark the whole thing...of course if you already have
some training is better otherwise you will spend a bit of time creating
your annotated corpus...
My logic says that this is the way to go - maybe I'm wrong is some way....
Any different opinions anyone?
Jim
ps. In your first sentence did you by any chance mean to say "recognise"
instead of "parse"?
