I'm starting a project where I want to extract keywoards from given messages. The keywoards 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-Libarys and it doesn't seem like they can handle tasks like that. Especially the german language makes it hard for those libarys 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. The process would include 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 keywoards.
Does anybody has experience with such custom NER-Models? How accurate can such Model extract wanted Keywoards?
Any kind of feedback is appreaciated! Greetz