You are viewing a plain text version of this content. The canonical link for it is here.
Posted to users@opennlp.apache.org by mauro fraboni <ma...@gmail.com> on 2012/04/20 15:16:15 UTC
Post Address Parsing and OpenNLP
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.
Re: Post Address Parsing and OpenNLP
Posted by Jörn Kottmann <ko...@gmail.com>.
On 04/20/2012 03:29 PM, Jim - FooBar(); wrote:
>
> 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...
For me it sounds like he has already a string which contains the address.
He just wants to get the individual parts of the address recognized,
right?
I would train one name finder and let him recognize all the types.
Have a look at our documentation on how to do that, if the results do
not work out of the box, you can easily adapt the feature generation a
bit for your needs, e.g using a town dictionary, street dictionary, etc.
Jörn
Re: Post Address Parsing and OpenNLP
Posted by mauro fraboni <ma...@gmail.com>.
I was thinking to create some hundreds of train records ....may be one
thousands.
The fact that I could use some dictionaries for feature generation is quite
interesting but how could I do it? I read the Custom Feature Generation
paragraph in Developer Documentation but it is not so clear. Do you have
some examples on how is possible to add dictionaries?
thanks.
On Fri, Apr 20, 2012 at 3:53 PM, Jörn Kottmann <ko...@gmail.com> wrote:
> 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();<jimpil1985@gmail.com**
>> >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"?
>>>
>>>
>
Re: Post Address Parsing and OpenNLP
Posted by Jörn Kottmann <ko...@gmail.com>.
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();<ji...@gmail.com>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"?
>>
Re: Post Address Parsing and OpenNLP
Posted by mauro fraboni <ma...@gmail.com>.
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(); <ji...@gmail.com>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"?
>
Re: Post Address Parsing and OpenNLP
Posted by "Jim - FooBar();" <ji...@gmail.com>.
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"?