Using Graphs of Classifiers to Impose Constraints on Semi-supervised Relation Extraction
Lidong Bing, William W. Cohen, Bhuwan Dhingra, Richard Wang · 2016
We propose a general approach to modeling semi-supervised learning constraints on unlabeled data.Both traditional supervised classification tasks and many natural semisupervised learning heuristics can be approximated by specifying the desired outcome of walks through a graph of classifiers.We demonstrate the modeling capability of this approach in the task of relation extraction, and experimental results show that the modeled constraints achieve better performance as expected.