Jorn, please, could you link me that model?
2016-07-04 14:42 GMT+02:00 Joern Kottmann :
> The co-referencer we used used to have in opennlp-tools has a model to
> detect the gender of names. That could could be extracted and put into a
> stand alone component.
>
> Jörn
>
> On
Hello,
there are also other interesting properties e.g. person title (e.g.
professor, doctor), job title/position,
company legal form. And much more for other entity types.
Maybe it would be worth it to build a dedicated component to extract
properties from entities.
Jörn
On Fri, Jul 1, 2016
Hi,
Sorry for my late reply. I didn't understand well your last email, but here
is what I meant:
Given a simple dictionary you have that has the following columns:
Name Type Gender
Agatha First F
JohnFirst M
Smith Both
Hi Mondher,
could you give me a raw example to understand how i should train the
classifier model?
Thank you in advance!
Damiano
2016-06-30 6:57 GMT+02:00 Mondher Bouazizi :
> Hi,
>
> I would recommend a hybrid approach where, in a first step, you use a plain
>
Hi,
I would recommend a hybrid approach where, in a first step, you use a plain
dictionary and then perform the classification if needed.
It's straightforward, but I think it would present better performances than
just performing a classification task.
In the first step you use a dictionary of
Awesome! Thank you so much WIlliam!
2016-06-29 13:36 GMT+02:00 William Colen :
> To create a NER model OpenNLP extracts features from the context, things
> such as: word prefix and suffix, next word, previous word, previous word
> prefix and suffix, next word prefix and
To create a NER model OpenNLP extracts features from the context, things
such as: word prefix and suffix, next word, previous word, previous word
prefix and suffix, next word prefix and suffix etc.
When you don't configure the feature generator it will apply the default:
Thank you William! Really appreciated!
I only do not get one point, when you said "You could increment your
model using
Custom Feature Generators" does it mean that i can "put" these features
inside ONE *.bin* file (model) that implement different things, or, name
finder is one thing and those
Not exactly. You would create a new NER model to replace yours.
In this approach you would need a corpus like this:
Pierre Vinken , 61 years old , will join the board
as a nonexecutive director Nov. 29 .
Mr . Vinken is chairman of Elsevier N.V. , the
Dutch publishing group . Jessie Robson
Hi William,
Ok, so you are talking about a kind of pipe where we execute:
1. NER (personM for example)
2. Regex (filter to reduce false positives)
3. Plain dictionary (filter as above) ?
Yes we can split out model in two for M and F, it is not a big problem, we
have a database grouped by gender.
Do you plan to use the surrounding context? If yes, maybe you could try to
split NER in two categories: PersonM and PersonF. Just an idea, never read
or tried anything like it. You would need a training corpus with these
classes.
You could add both the plain dictionary and the regex as NER
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