Thanks @honnibal, good to know that it's on the to-do list for Prodigy! And of course, I assume that if I first create a custom spacy parser model with a little bit of manually created seed training data, I could technically use dep.teach after that?
Good point about using text classification in the simple cases... I think mine requires some more structure though. Not dense like dependency parsing, but more than a single relation per sentence. Here's an example:
I want to exchange black t-shirts for white jeans... 10 of the former, 5 of the latter
I.e., a sentence can contain multiple products, each with accompanying attributes, and then I can have disjointly placed entities that reference specific products mentioned. Seems to me this would be best handled by a relationship parser, do you agree? Or is there a simpler approach?
Bonus question: let's say you have an ontology where your objects are uniquely defined by a set of attributes, but you're only selling one type of object so it's not necessary to specify the root:
"I want two black t-shirts in medium and three white t-shirts, small",
"I want two black in medium and three white, small"
How would you define the relations here? I know that there are two "hidden" t-shirt entities, which have directed relations to the attributes. But the grouping black+medium and white+small isn't really directional. Is there any non-directed relationship in spaCy?