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?
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.
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.
