The learnable NER component could probably detect the names if you would
have
much more training data. I suggest to use some rule based extractor,
maybe have a look
at UIMA Ruca.
Jörn
On 08/06/2013 03:22 PM, Markus Marks wrote:
Hi all,
i'm a german computer science student, who is currently writing on his
bachelor thesis. I write you because i'm very desperate. I have to
solve an information extraction task and i'm not quite sure, how to
solve it and i was hoping, you could help me or tell me if openNLP
would work out.
Ok... here it comes:
Let's assume I have a sender's adress from a letter. And i have few
annotated examples.
new document example with annotation
Mr. XYZ Enterprise Something
Example Company John Doe
Sample road 12514 somewhere else
somewhere another road
something
something something else
So the problem is how to generate a matching or learning algorithm, so
that I'm able to extract for example the name of the company or the
name of a new sender, considering some annotated examples i can
provide, with the problem that not every sender is written with the
same order or expressions.
The thing is that, i only have really few examples, like less than 10.
You have any suggestions how to solve this? I would be really
thankful, since i'm very disappointed, not finding a solution.
Yours thankfully,
Markus
