Does topic modelling reflect semantic prototypes?

Abstract

The chapter introduces a representation of a textual event as a mixture of semantic stereotypes and factual information. We also present a method to distinguish semantic prototypes that are specific for a given event from generic elements that might provide cause and result information. Moreover, this chapter discusses the results of experiments of unsupervised topic extraction performed on documents from a large-scale corpus with an additional temporal structure. These experiments were realized as a comparison of the nature of information provided by Latent Dirichlet Allocation based on Log-Entropy weights and Vector Space modelling. The impact of different corpus time windows on this information is discussed. Finally, we try to answer if the unsupervised topic modelling may reflect deeper semantic information, such as elements describing given event or its causes and results, and discern it from factual data.