In this paper, we propose a graph model to represent the contextual discourse information from both lexical and topic perspectives.
 Topic Modeler: For estimating transition probability Pt(i,m), we employ GibbsLDA++ 6 , an LDA model using Gibbs Sampling technique for parameter estimation and inference.