Semantic based dcm models for text classification

Abstract

This contribution deals with the problem of documents classification. The proposed approach is probabilistic and it is based on a mixture of a Dirichlet and Multinomial distribution. Our aim is to build a classifier able, not only to take into account the words frequency, but also the latent topics contained within the available corpora. This new model, called sbDCM, allows us to insert directly the number of topics (known or unknown) that compound the document, without losing the “burstiness” phenomenon and the classification performance. The distribution is implemented and tested according to two different contexts: on one hand, the number of latent topics is defined by experts in advance, on the other hand, such number is unknown.