I am having a hard time thinking and finding prior reasearch about how to include higher level features in neural networks. Here is a couple silly example of what I mean...
Let's say I am building a convolutional neural network to classify if an image has a boat in it or not. How would I include, non pixel level, static features such as "This image was scraped from a site with a .boat TLD".
Let's say I am building some type of recurrant neural network to determine the next word in a sequence of text. How would I include information like "The text I am trying to predict on is coming from the sports section of a newspaper."
I can think of a few ways to do this, for example concat the higher level features represented as word or pixel in the input data but that seems a little crude. Perhaps stacking models that handle observation level features is an answer? Is there a cononical way of handling this?