What's the best approach for machine learning on deeply hierarchical JSON/XML/DOM documents (not counting text nodes)?

Say I want to recognize and generate documents similar to a training set of around 4000 documents, and assume that I can have test data with "bad" documents which I don't have yet. I have boiled my feature vector down to (parent type, parent id, child type, child id or value, location of child in parent). However, the step beyond that is my question. Do I start doing one-hot encodings of types and ids, or do I use embeddings?

If parents and children are the axes of my matrix, what do I use for the values in the matrix? I don't have ratings, all I have is child ID/Value and child location. Is that what I use in the matrix to do embeddings? (Sorry for the very general question, I'm just getting started on machine learning.)

Also, can I do nested embeddings? Say I want to embed words in my child values. Also, If I use one-hot encodings or embeddings, can I use types and ids which are not valid? (Probably not, I would guess.)

So I'll really only be testing the structural soundness of my data. What I want to do is generate structurally beautiful data (my documents are of a visual nature and relationships between typed data is important). If someone has already done this, and has a paper or open source software, please let me know.


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