New answers tagged supervised-learning
0
Supervised
Pros:
highest accuracy
Cons:
need a large human-labeled training set
brittle (doesn't work well with examples that are in a different genre from the training set)
Semi-supervised
Relation bootstrapping
Pros:
only requires a small set of labeled data (seed relations)
Cons:
complex iterative process
Distant supervision
Pros:
training ...
2
It's perfectly reasonable to apply 'traditional' Deep Learning approaches to try and learn an adjacency matrix (a matrix is just a vector of vectors, which can be flattened into a single output vector) but you might need a lot of training data as N gets larger.
Your outputs could certainly have the form of an adjacency matrix, as you describe. Whether it's ...
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