Collective Classification using Semantic Based Regularization

Semantic Based Regularization (SBR) is a framework for injecting prior knowledge expressed as FOL clauses into a semi-supervised learning problem. The prior knowledge is converted into a set of continuous constraints, which are enforced during training. SBR employs the prior knowledge only at training time, hoping that the learning process is able to encode the knowledge via the training data into its parameters. This paper defines a collective classification approach employing the prior knowledge at test time, naturally reusing most of the mathematical apparatus developed for standard SBR. The experimental results show that the presented method outperforms state-of-the-art classification methods on multiple text categorization tasks. © 2013 IEEE.