1
$\begingroup$

I have found a number of parsers for the automatic extraction of institution names from texts (e.g. this one). My task is in a sense the inverse one: I want to automatically generate reality-like institution names, with a possibility to differentiate them by type (private-held, public, educational, etc.) and by branch.

Is there any algorithm / application / papers to be found? Alternatively, is there any (free access) database with such data?

Improve this question
$\endgroup$
3
  • 1
    $\begingroup$ Do you mean generate fictional entities? Anyway it's not really the inverse of NER, because NER relies on the context words in a sentence to detect entities names. In theory NER should work as well with fictional entities than with real ones (except that it knows some real ones from the training). $\endgroup$
    – Erwan
    Commented Jun 21, 2020 at 15:35
  • 1
    $\begingroup$ Important question: do you have training data? A.k.a institution namesz differentiated by type $\endgroup$
    – Guillermo Mosse
    Commented Jun 21, 2020 at 16:38
  • $\begingroup$ Thanks Erwan, thanks Guillermo. I do understand that what I want is not exactly the inverse of NER, therefore the question. And no, I do mean "fictional" names. I do not have therefore any training data, but just a vague idea of classification (Private Company vs. Public Institution vs. Educational etc.) $\endgroup$
    – Alex Konnen
    Commented Jun 22, 2020 at 6:10

2 Answers 2

Reset to default
1
$\begingroup$

Conditional on you having data, yes, you can. Check out Generative Adversarial Networks and/or Reinforcement Learning for text generation. This paper is a good starting point: https://openreview.net/forum?id=rJedV3R5tm.

Also, here's a tool that might help you. What you can do is generat these institution names without differentiating by type, and then build another model to classify them.

Improve this answer
$\endgroup$
2
  • $\begingroup$ Guillermo, many thanks for the link. Highly interesting; the stuff ins very new to me, so I will probably need more time to digest (I did a random text generator myself once - but without almost any science). If I am not mistaken, however, the second link does not lead to any tool, but to the paper itself - am I wrong? $\endgroup$
    – Alex Konnen
    Commented Jun 22, 2020 at 6:25
  • 1
    $\begingroup$ Thanks Alex! You are right, my mistake. The link points to the tool now. $\endgroup$
    – Guillermo Mosse
    Commented Jun 22, 2020 at 7:52
1
$\begingroup$

If you want to build your own dataset, you could look at packages such as:

They both provide features to generate company/institution names based on certain locales as well.

If your goal is to generate training data for a NER task, this should be a good start. If it's to generate company names, this will already cover quite a bit.

Improve this answer
$\endgroup$
1
  • 1
    $\begingroup$ Thanks Valentin, I have already had a look at Mimesis, seems to be very attractive; Faker is a bit more complicated for me, as I am not a native Pythonese speaker (rether C#). But I am going to take some time to look at it, too. $\endgroup$
    – Alex Konnen
    Commented Jun 22, 2020 at 10:02

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.