Synonyms

[ines (Ines Montani)]

I hope I understand the question correctly. But I think you probably want to focus on training your model to recognise DRUG (any drug) until it's reasonably good at it. You can then add a rule-based component on top later that normalises them and groups them into subtypes. I actually outlined a very similar approach here:

Nested labels for NER

One thing you could do is add a second pipeline component after the entity recognizer that looks for DRUG entities and then sets a custom attribute on those entities specifying a list of subtypes (e.g. ent._.entity_subtypes). Here’s an example of a rule-based solution – but you could obviously also swap out the dictionary for a statistical solution, or a combination. from spacy.tokens import Span # dictionary of lowercase entities mapped to subtypes DRUG_SUBTYPES = { 'citalopram': ['ANTID…