Human-defined concepts are fundamental building-blocks in constructing knowledge bases such as ontologies. Statistical learning techniques provide an alternative automated approach to concept definition, driven by data rather than prior knowledge. In this paper we propose a probabilistic modeling framework that combines both human-defined concepts and data-driven topics in a principled manner. The methodology we propose is based on applications of statistical topic models (also known as latent Dirichlet allocation models). We demonstrate the utility of this general framework in two ways. We first illustrate how the methodology can be used to automatically tag Web pages with concepts from a known set of concepts without any need for labeled documents. We then perform a series of experiments that quantify how combining human-defined semantic knowledge with data-driven techniques leads to better language models than can be obtained with either alone.

Abstract. We evaluate the effectiveness of neural networks as a tool for predicting whether a particular combination of preconditioner and iterative method will correctly solve a given sparse linear system Ax = b. We consider several scenarios corresponding to different assumptions about the relationship between the systems used to train the neural network and those for which the neural network is expected to predict behavior. Greater similarity between those two sets leads to better accuracy, but even when the two sets are very different prediction accuracy can be improved by using additional computation.