I'm pretty new to spaCy and NLP in general, and I'm trying to figure out how to classify text. I've already gone through quite a few tutorials, and have figured out how to train my model, based on already classified datasets.

However, I'm struggling to understand how the text classification works, and how I can feed more data into it, to make it more accurate. For example, I want to build a custom rule based NER model, and want the classification model to to also look at those entities.

Is something like this possible, and how should I go ahead with this?

  • $\begingroup$ please provide more specific information and possibly some examples of what you are after, as it stands your question is vague $\endgroup$
    – Nikos M.
    Nov 21 '21 at 10:11
  • $\begingroup$ @NikosM. Instead of the text classifier just looking at the text, I want to provide it context, based on different NER models, so that the classifier can more accurately predict the classification. For example, I have an NER model that extract names of football players, and want to feed this to the classifier, so that it's able to predict an article that mentions many football players, is in fact talking about football. $\endgroup$
    – Thomas
    Nov 22 '21 at 8:38

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