Introducing semantics in short text classification

Abstract

To overcome short text classification issues due to shortness and sparseness, the enrichment process is classically proposed: topics (word clusters) are extracted from external knowledge sources using Latent Dirichlet Allocation. All the words, associated to topics which encompass short text words, are added to the initial short text content. We propose (i) an explicit representation of a two-level enrichment method in which the enrichment is considered either with respect to each word in the text or to the global semantic meaning of the short text and (ii) a new semantic Random Forest kind in which semantic relations between features are taken into account at node level rather than at tree level as it was recently proposed in the literature to avoid potential tree correlation. We demonstrate that our enrichment method is valid not only for Random Forest based methods but also for other methods like MaxEnt, SVM and Naive Bayes.