I'm starting a project where I want to extract keywords from given messages. The keywords are for example something like: "hard disk", "watch" or other technical components. I'm working with a dataset where a technician wrote a small text if he maintenanced something on a given object.
The messages are often very different in their form. For example sometimes the messages start with the repaired object and sometimes with the current date.
I looked into some NER-Libaries and it doesn't seem like they can handle tasks like that. Especially the German language makes it hard for those libaries to detect entities.
I had the idea to use CRFsuite to train my own NER-model. But I'm not sure how accurate the outcome will be. It would mean that I have to tag a lot of training data and I'm not sure if the outcome will match the time I have to spend to tag those keywords.
Does anybody have any experience with such custom NER-models? How accurate can such a model extract wanted keywords?