Sprinkling Topics for Weakly Supervised Text Classification

Abstract

Supervised text classification algorithms require a large number of documents labeled by humans, that involve a laborintensive and time consuming process. In this paper, we propose a weakly supervised algorithm in which supervision comes in the form of labeling of Latent Dirichlet Allocation (LDA) topics. We then use this weak supervision to "sprinkle" artificial words to the training documents to identify topics in accordance with the underlying class structure of the corpus based on the higher order word associations. We evaluate this approach to improve performance of text classification on three real world datasets.