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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 ...


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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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