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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

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I would recommend you to use German Bert for embeddings: https://huggingface.co/bert-base-german-cased

Together with a cosine similarity, like described in this article: https://towardsdatascience.com/keyword-extraction-with-bert-724efca412ea

Bert is very powerful to extract knowledge from text efficiently, I used to reach extraordinary results thanks to that library.

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  • $\begingroup$ Please do not "sign" your posts (edited). $\endgroup$
    – desertnaut
    Jun 17 at 19:20

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